A critical review on applications of artificial intelligence in manufacturing

Abbas AT, Hamza K, Aly MF (2014) CNC machining path planning optimization for circular hole patterns via a hybrid ant colony optimization approach. Mech Eng Res. https://doi.org/10.5539/mer.v4n2p16

Abdi MR, Labib AW (2004) Feasibility study of the tactical design justification for reconfigurable manufacturing systems using the fuzzy analytical hierarchical process. Int J Prod Res 42:3055–3076. https://doi.org/10.1080/00207540410001696041

Ahmed F, Kim K-Y (2017) Data-driven weld nugget width prediction with decision tree algorithm. Procedia Manuf 10:1009–1019. https://doi.org/10.1016/j.promfg.2017.07.092

Ajmeria R, Mondal M, Banerjee R et al (2022) A Critical survey of EEG-based BCI systems for applications in industrial internet of things. IEEE Commun Surv Tutorials. https://doi.org/10.1109/COMST.2022.3232576

Aksoy B, Koru M (2020) Estimation of casting mold interfacial heat transfer coefficient in pressure die casting process by artificial intelligence methods. Arab J Sci Eng 45:8969–8980. https://doi.org/10.1007/s13369-020-04648-7

Al Faruque MA, Chhetri SR, Canedo A, Wan J (2016) Acoustic side-channel attacks on additive manufacturing systems. 2016 ACM/IEEE 7th Int Conf Cyber-Physical Syst ICCPS 2016—Proc. https://doi.org/10.1109/ICCPS.2016.7479068

Alam MT, Arif S, Ansari AH, Alam MN (2019) Optimization of wear behaviour using Taguchi and ANN of fabricated aluminium matrix nanocomposites by two-step stir casting. Mater Res Express 6:065002. https://doi.org/10.1088/2053-1591/ab0871

Alfaro-Cortés E, Alfaro-Navarro J-L, Gámez M, García N (2020) Using Random forest to interpret out-of-control signals. Acta Polytech Hungarica 17:115–130. https://doi.org/10.12700/APH.17.6.2020.6.7

Al-Ghamdi K, Taylan O (2015) A comparative study on modelling material removal rate by ANFIS and polynomial methods in electrical discharge machining process. Comput Ind Eng 79:27–41. https://doi.org/10.1016/j.cie.2014.10.023

Alghandoor A, Phelan PE, Villalobos R, Phelan BE (2008) U.S. manufacturing aggregate energy intensity decomposition: the application of multivariate regression analysis. Int J Energy Res 32:91–106. https://doi.org/10.1002/er.1334

Al-Jarrah R, Shahzad A, Roth H (2015) Path planning and motion coordination for multi-robots system using probabilistic neuro-fuzzy. IFAC-PapersOnLine 48:46–51. https://doi.org/10.1016/j.ifacol.2015.08.106

Anglani A, Pacella M (2018) Logistic regression and response surface design for statistical modeling of investment casting process in metal foam production. Procedia CIRP 67:504–509. https://doi.org/10.1016/j.procir.2017.12.252

Anjum N, Amjad MK, Ayaz Y (2019) Analysis of computational efficiency of artificial intelligence based search techniques in trajectory planning of industrial manipulator. 2019 Int Conf Robot Autom Ind ICRAI 2019. https://doi.org/10.1109/ICRAI47710.2019.8967374

Antal C, Cioara T, Antal M, Anghel I (2021) Blockchain platform for COVID-19 vaccine supply management. IEEE Open J Comput Soc 2:164–178. https://doi.org/10.1109/ojcs.2021.3067450

Aoyagi K, Wang H, Sudo H, Chiba A (2019) Simple method to construct process maps for additive manufacturing using a support vector machine. Addit Manuf 27:353–362. https://doi.org/10.1016/j.addma.2019.03.013

Arkhipov MV, Matrosova VV, Volnov IN (2020) Automation in foundry industry: modern information and cyber-physical systems. Lecture notes in electrical engineering. Springer International Publishing, Cham, pp 382–392

Arora T, Gigras Y, Arora V (2014) Robotic path planning using genetic algorithm in dynamic environment. Int J Comput Appl 89:8–12. https://doi.org/10.5120/15674-4422

Asadollahi-Yazdi E, Gardan J, Lafon P (2018) Multi-objective optimization of additive manufacturing process. IFAC-PapersOnLine 51:152–157. https://doi.org/10.1016/j.ifacol.2018.08.250

Asif K, Zhang L, Derrible S et al (2020) Machine learning model to predict welding quality using air-coupled acoustic emission and weld inputs. J Intell Manuf. https://doi.org/10.1007/s10845-020-01667-x

Balasubramani S, Balaji N, Ramakrishnan T et al (2020) Defect identification in casting surface using image processing techniques. Green Mater Adv Manuf Technol. https://doi.org/10.1201/9781003056546-13

Ball D, Yan R, Licht T et al (2008) A strategy for decomposing large-scale energy-constrained sensor networks for system monitoring. Prod Plan Control 19:435–447. https://doi.org/10.1080/09537280802034653

Baseri H, Rahmani B, Bakhshi-Jooybari M (2011) Selection of bending parameters for minimal spring-back using an ANFIS model and simulated annealing algorithm. J Manuf Sci Eng 133:139. https://doi.org/10.1115/1.4004139

Bassoli E, Gatto A, Iuliano L, Violante MG (2007) 3D printing technique applied to rapid casting. Rapid Prototyp J 13:148–155. https://doi.org/10.1108/13552540710750898

Baumol WJ, Vinod HD (1970) An inventory theoretic model of freight transport demand. Manage Sci 16:413–421. https://doi.org/10.1287/mnsc.16.7.413

Bechtsis D, Tsolakis N, Vlachos D, Iakovou E (2017) Sustainable supply chain management in the digitalisation era: the impact of Automated Guided Vehicles. J Clean Prod 142:3970–3984. https://doi.org/10.1016/j.jclepro.2016.10.057

Behrens B-A, Santangelo A, Buse C (2013) Acoustic emission technique for online monitoring during cold forging of steel components: a promising approach for online crack detection in metal forming processes. Prod Eng 7:423–432. https://doi.org/10.1007/s11740-013-0452-8

Behrens B-A, Hübner S, Wölki K (2017) Acoustic emission: a promising and challenging technique for process monitoring in sheet metal forming. J Manuf Process 29:281–288. https://doi.org/10.1016/j.jmapro.2017.08.002

Benzidia S, Makaoui N, Bentahar O (2021) The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance. Technol Forecast Soc Change 165:120557. https://doi.org/10.1016/j.techfore.2020.120557

Bewoor LA, Prakash VC, Sapkal SU (2018) Production scheduling optimization in foundry using hybrid Particle Swarm Optimization algorithm. Procedia Manuf 22:57–64. https://doi.org/10.1016/j.promfg.2018.03.010

Bhat NN, Kumari K, Dutta S et al (2015) Friction stir weld classification by applying wavelet analysis and support vector machine on weld surface images. J Manuf Process 20:274–281. https://doi.org/10.1016/j.jmapro.2015.07.002

Bhat NN, Dutta S, Pal SK, Pal S (2016a) Tool condition classification in turning process using hidden Markov model based on texture analysis of machined surface images. Measurement 90:500–509. https://doi.org/10.1016/j.measurement.2016.05.022

Bhat NN, Dutta S, Vashisth T et al (2016b) Tool condition monitoring by SVM classification of machined surface images in turning. Int J Adv Manuf Technol 83:1487–1502. https://doi.org/10.1007/s00170-015-7441-3

Bhattacharyya P, Sengupta D, Mukhopadhyay S (2007) Cutting force-based real-time estimation of tool wear in face milling using a combination of signal processing techniques. Mech Syst Signal Process 21:2665–2683. https://doi.org/10.1016/j.ymssp.2007.01.004

Biau G, Scornet E (2016) A random forest guided tour. TEST 25:197–227. https://doi.org/10.1007/s11749-016-0481-7

Bo H, Rongxi J, Gongxuan Z (2014) Heuristic search for scheduling flexible manufacturing systems using multiple heuristic functions. Lect Notes Artif Intell 8481:178–187. https://doi.org/10.1007/978-3-319-07455-9_19

Bodendorf F, Zimmermann R (2005) Proactive supply-chain event management with agent technology. Int J Electron Commer 9:58–89. https://doi.org/10.1080/10864415.2003.11044340

Boër CR, Petitti M, Lombardi F, Simon J-P (1990) A CAPP}/{CAM expert system for a high productivity, high flexibility CNC turning center. CIRP Ann 39:481–483. https://doi.org/10.1016/s0007-8506(07)61101-9

Bogataj D, Bogataj M (2019) NPV approach to material requirements planning theory: a 50-year review of these research achievements. Int J Prod Res 57:5137–5153. https://doi.org/10.1080/00207543.2018.1524167

Bologa O, Breaz R-E, Racz S-G (2018) Using the Analytic Hierarchy Process ({AHP}) and fuzzy logic to evaluate the possibility of introducing single point incremental forming on industrial scale. Procedia Comput Sci 139:408–416. https://doi.org/10.1016/j.procs.2018.10.262

Borselli A, Colla V, Vannucci M, Veroli M (2010) A fuzzy inference system applied to defect detection in flat steel production. In: international conference on fuzzy systems. IEEE, pp 1–6

Bove O, Rinaldi F (1988) Semi-automatic welding processes and the mechanised ‘PASSO’ process. Weld Int 2:160–167. https://doi.org/10.1080/09507118809447461

Brender N, Markov I (2013) Risk perception and risk management in cloud computing: results from a case study of Swiss companies. Int J Inf Manage 33:726–733. https://doi.org/10.1016/j.ijinfomgt.2013.05.004

Cao H, Chen X, Zi Y et al (2008) End milling tool breakage detection using lifting scheme and Mahalanobis distance. Int J Mach Tools Manuf 48:141–151. https://doi.org/10.1016/j.ijmachtools.2007.09.001

Cao H, Lei Y, He Z (2013) Chatter identification in end milling process using wavelet packets and Hilbert–Huang transform. Int J Mach Tools Manuf 69:11–19. https://doi.org/10.1016/j.ijmachtools.2013.02.007

Cao XC, Chen BQ, Yao B, He WP (2019) Combining translation-invariant wavelet frames and convolutional neural network for intelligent tool wear state identification. Comput Ind 106:71–84. https://doi.org/10.1016/j.compind.2018.12.018

Carvalho AV, Chouchene A, Lima TM, Charrua-Santos F (2020) Cognitive manufacturing in industry 4.0 toward cognitive load reduction: A conceptual framework. Appl Syst Innov 3:1–14. https://doi.org/10.3390/asi3040055

Cavusoglu O, Gurun H (2016) Investigation and fuzzy logic prediction of the effects of clearance on the blanking process of CuZn30 sheet metal. Met Mater 54:125–131. https://doi.org/10.4149/km_2016_2_125

Chae B (2009) Developing key performance indicators for supply chain: an industry perspective. Supply Chain Manag 14:422–428. https://doi.org/10.1108/13598540910995192

Chan FTS, Chan HK, Kazerooni A (2003) Real time fuzzy scheduling rules in FMS. J Intell Manuf 14:341–350. https://doi.org/10.1023/A:1024601826420

Chang B, Chang C-W, Wu C-H (2011) Fuzzy DEMATEL method for developing supplier selection criteria. Expert Syst Appl 38:1850–1858. https://doi.org/10.1016/j.eswa.2010.07.114

Chate GR, Deshpande AS, Parappagoudar MB (2017) Modeling and optimization of furan molding sand system using design of experiments and particle swarm optimization. Proc Inst Mech Eng Part E J Process Mech Eng 232:579–598. https://doi.org/10.1177/0954408917728636

Chen S, Kaufmann T (2022) Development of data-driven machine learning models for the prediction of casting surface defects. Metals 12:1–15. https://doi.org/10.3390/met12010001

Cheng Y, Zhu H, Hu K et al (2019) Multisensory data-driven health degradation monitoring of machining tools by generalized multiclass support vector machine. IEEE Access 7:47102–47113. https://doi.org/10.1109/ACCESS.2019.2908852

Chipman HA, Kolaczyk ED, McCulloch RE (1997) Adaptive Bayesian wavelet shrinkage. J Am Stat Assoc 92:1413–1421. https://doi.org/10.1080/01621459.1997.10473662

Cho HH, Kang SH, Kim SH et al (2012) Microstructural evolution in friction stir welding of high-strength linepipe steel. Mater Des 34:258–267. https://doi.org/10.1016/j.matdes.2011.08.010

Christopher M, Holweg M (2011) “Supply Chain 2.0”: Managing supply chains in the era of turbulence. Int J Phys Distrib Logist Manag 41:63–82. https://doi.org/10.1108/09600031111101439

Chung H-Y, Hou C-C, Liu S-C (2013) Automatic Navigation of a wheeled mobile robot using Particle Swarm Optimization and Fuzzy Control. In: 2013 IEEE International Symposium on Industrial Electronics. IEEE, pp 1–6

Das R, Pradhan MK (2013) ANN modelling for surface roughness in electrical discharge machining: a comparative study. Int J Serv Comput Oriented Manuf 1:124. https://doi.org/10.1504/ijscom.2013.058674

Das P, Samanta SK, Das R, Dutta P (2014) Optimization of degree of sphericity of primary phase during cooling slope casting of A356 Al alloy: Taguchi method and regression analysis. Measurement 55:605–615. https://doi.org/10.1016/j.measurement.2014.05.022

Das B, Pal S, Bag S (2017) Weld quality prediction in friction stir welding using wavelet analysis. Int J Adv Manuf Technol 89:711–725. https://doi.org/10.1007/s00170-016-9140-0

Date N, Krishnaswami P, Motipalli VVSK (2009) Automated process planning method to machine A B-Spline free-form feature on a mill–turn center. Comput Ind Eng 56:198–207. https://doi.org/10.1016/j.cie.2008.05.004

Datta A, Dutta S, Pal SK, Sen R (2013) Progressive cutting tool wear detection from machined surface images using Voronoi tessellation method. J Mater Process Technol 213:2339–2349. https://doi.org/10.1016/j.jmatprotec.2013.07.008

de Souza Borges Ferreira R, Sabbaghi A, Huang Q (2020) Automated geometric shape deviation modeling for additive manufacturing systems via bayesian neural networks. IEEE Trans Autom Sci Eng 17:584–598. https://doi.org/10.1109/TASE.2019.2936821

Deris AM, Zain AM, Sallehuddin R (2013) Hybrid GR}-{SVM for prediction of surface roughness in abrasive water jet machining. Meccanica 48:1937–1945. https://doi.org/10.1007/s11012-013-9710-2

Devagiri JS, Paheding S, Niyaz Q et al (2022) Augmented reality and artificial intelligence in industry: trends, tools, and future challenges. Expert Syst Appl 207:118002. https://doi.org/10.1016/j.eswa.2022.118002

Dhavamani C, Alwarsamy T (2012) Optimization of machining parameters for aluminum and silicon carbide composite using genetic algorithm. Procedia Eng 38:1994–2004. https://doi.org/10.1016/j.proeng.2012.06.241

dit Leksir Yazid L, Salah B, Seghir BM, Jurgen B (2012) Adaptive support vector machine-based surface quality evaluation and temperature monitoring. Application to billet continuous casting process. Int J Adv Manuf Technol 67:2063–2073. https://doi.org/10.1007/s00170-012-4631-0

Dogan A, Birant D (2021) Machine learning and data mining in manufacturing. Expert Syst Appl 166:114060. https://doi.org/10.1016/j.eswa.2020.114060

Dominguez R, Cannella S (2020) Insights on multi-agent systems applications for supply chain management. Sustain 12:1–13. https://doi.org/10.3390/su12051935

Donald Waters (2003) Global logistics and distribution planning-Strategies for management. Kogan Page Limited, ISBN 0 7494 3930 0 71:1811–1815

Draper NR, Smith H (1998) Applied regression analysis. Wiley, New York

Duan L, Yang K, Ruan L (2021) Research on automatic recognition of casting defects based on deep learning. IEEE Access 9:12209–12216. https://doi.org/10.1109/access.2020.3048432

Duanmu J, Taaffe K (2007) Measuring manufacturing throughput using takt time analysis and simulation. Proc - Winter Simul Conf, pp. 1633–1640. https://doi.org/10.1109/WSC.2007.4419783

Dučić N, Ćojbašić Ž, Manasijević S et al (2016) Optimization of the gating system for sand casting using genetic algorithm. Int J Met 11:255–265. https://doi.org/10.1007/s40962-016-0040-8

Dumarey M, Goodwin DJ, Davison C (2015) Multivariate modelling to study the effect of the manufacturing process on the complete tablet dissolution profile. Int J Pharm 486:112–120. https://doi.org/10.1016/j.ijpharm.2015.03.040

Dumitrache I, Caramihai SI, Moisescu MA, Sacala IS (2019) Neuro-inspired Framework for cognitive manufacturing control. IFAC-PapersOnLine 52:910–915. https://doi.org/10.1016/j.ifacol.2019.11.311

Elangovan K, Narayanan CS, Narayanasamy R (2010) Modelling of forming limit diagram of perforated commercial pure aluminium sheets using artificial neural network. Comput Mater Sci 47:1072–1078. https://doi.org/10.1016/j.commatsci.2009.12.016

Ferreira L, Borenstein D (2012) A fuzzy-Bayesian model for supplier selection. Expert Syst Appl 39(9):7834–7844. https://doi.org/10.1016/j.eswa.2012.01.068

Fleming P, Lammlein D, Wilkes D et al (2008) In-process gap detection in friction stir welding. Sens Rev 1:62–67. https://doi.org/10.1108/02602280810850044

Fok KY, Cheng CT, Ganganath N et al (2018) Accelerating 3D printing process using an extended ant colony optimization algorithm. Proc IEEE Int Symp Circuits Syst 20:15. https://doi.org/10.1109/ISCAS.2018.8351113

Fok KY, Cheng CT, Ganganath N et al (2019) An ACO-based tool-path optimizer for 3-D printing applications. IEEE Trans Ind Inform 15:2277–2287. https://doi.org/10.1109/TII.2018.2889740

Fu Z, Mo J, Chen L, Chen W (2010) Using genetic algorithm-back propagation neural network prediction and finite-element model simulation to optimize the process of multiple-step incremental air-bending forming of sheet metal. Mater Des 31:267–277. https://doi.org/10.1016/j.matdes.2009.06.019

Gandhi K, Schmidt B, Ng AHC (2018) Towards data mining based decision support in manufacturing maintenance. Procedia CIRP 72:261–265. https://doi.org/10.1016/j.procir.2018.03.076

Ganesan H, Mohankumar G (2013) Optimization of machining techniques in CNC turning centre using genetic algorithm. Arab J Sci Eng 38:1529–1538. https://doi.org/10.1007/s13369-013-0539-8

Gangadhar N, Kumar H, Narendranath S, Sugumaran V (2014) Fault diagnosis of single point cutting tool through vibration signal using decision tree algorithm. Procedia Mater Sci 5:1434–1441. https://doi.org/10.1016/j.mspro.2014.07.462

Ganganath N, Cheng C-T, Tse CK (2014) An ACO-based off-line path planner for nonholonomic mobile robots. In: 2014 IEEE international symposium on circuits and systems (ISCAS). IEEE, pp 1038–1041

Gao X, You D, Katayama S (2012) Infrared image recognition for seam tracking monitoring during fiber laser welding. Mechatronics 22:370–380. https://doi.org/10.1016/j.mechatronics.2011.09.005

Garg R, Kiwelekar AW, Netak LD, Bhate SS (2021) Potential use-cases of natural language processing for a logistics organization. In: Gunjan VK, Zurada JM (eds) Modern approaches in machine learning and cognitive science: a walkthrough: latest trends in AI, vol 2. Springer International Publishing, Cham, pp 157–191

GC MP, Krishna P, Parappagoudar MB (2016) Squeeze casting process modeling by a conventional statistical regression analysis approach. Appl Math Model 40:6869–6888. https://doi.org/10.1016/j.apm.2016.02.029

Ghorbani S, Polushin NI (2017) A comparison of single of decision tree, decision tree forest and group method of data handling to evaluate the surface roughness in machining process. Int J Mech Mechatr Eng 11:326–333

Ghosh A, Chattopadhyaya S, Das RK, Sarkar PK (2011) Assessment of heat affected zone of submerged Arc welding process through digital image processing. Procedia Eng 10:2782–2785. https://doi.org/10.1016/j.proeng.2011.04.462

Gien D (1999) Towards a unified representation of quality in manufacturing systems. Int J Comput Integr Manuf 12:141–155. https://doi.org/10.1080/095119299130380

Gisario A, Barletta M, Conti C, Guarino S (2011) Springback control in sheet metal bending by laser-assisted bending: experimental analysis, empirical and neural network modelling. Opt Lasers Eng 49:1372–1383. https://doi.org/10.1016/j.optlaseng.2011.07.010

Gong X, Liao D, Chen T et al (2016) Optimization of steel casting feeding system based on BP neural network and genetic algorithm. China Foundry 13:182–190. https://doi.org/10.1007/s41230-016-6008-8

González-Yero G, Leyva RR, Mendoza MR et al (2020) Neuro-fuzzy system for compensating slow disturbances in adaptive mold level control. Metals 11:56. https://doi.org/10.3390/met11010056

Gope AK, Liao Y-S, Kuo C-FJ (2022) Quality prediction and abnormal processing parameter identification in polypropylene fiber melt spinning using artificial intelligence machine learning and deep learning algorithms. Polymers 14:2739. https://doi.org/10.3390/polym14132739

Gordon ER, Shokrani A, Flynn JM et al (2016) A surface modification decision tree to influence design in additive manufacturing. Smart Innov Syst Technol 52:423–434. https://doi.org/10.1007/978-3-319-32098-4_36

Grabot B (1993) A decision support system for variable routings management in manufacturing systems. Fuzzy Sets Syst 58:87–104. https://doi.org/10.1016/0165-0114(93)90324-B

Gu S, Ni J, Yuan J (2002) Non-stationary signal analysis and transient machining process condition monitoring. Int J Mach Tools Manuf 42:41–51. https://doi.org/10.1016/s0890-6955(01)00097-9

Wu G, Kwak H, Jang S, et al (2008) Design of online surface inspection system of hot rolled strips. In: 2008 IEEE International conference on automation and logistics. IEEE, pp 2291–2295

Guosheng H, Guohong Z (2008) Comparison on neural networks and support vector machines in suppliers’ selection. J Syst Eng Electron 19:316–320. https://doi.org/10.1016/S1004-4132(08)60085-7

Gupta MK, Sood PK, Sharma VS (2015) Machining parameters optimization of titanium alloy using response surface methodology and particle swarm optimization under minimum-quantity lubrication environment. Mater Manuf Process 31:1671–1682. https://doi.org/10.1080/10426914.2015.1117632

Gurupavan HR, Devegowda TM, Ravindra HV, Ugrasen G (2017) Estimation of machining performances in WEDM of aluminium based metal matrix composite material using ANN. Mater Today Proc 4:10035–10038. https://doi.org/10.1016/j.matpr.2017.06.316

Hamouche E, Loukaides EG (2018) Classification and selection of sheet forming processes with machine learning. Int J Comput Integr Manuf 31:921–932. https://doi.org/10.1080/0951192x.2018.1429668

van Hasselt H, Guez A, Silver D (2015) Deep reinforcement learning with double Q-learning. https://doi.org/10.48550/arXiv.1509.06461

Hazir E, Ozcan T (2018) Response surface methodology integrated with desirability function and genetic algorithm approach for the optimization of CNC machining parameters. Arab J Sci Eng 44:2795–2809. https://doi.org/10.1007/s13369-018-3559-6

He B, Lei Y, Jiang M, Wang F (2022) Optimal design of the gating and riser system for complex casting using an evolutionary algorithm. Materials 15:7490. https://doi.org/10.3390/ma15217490

Heher YK, Chen Y (2017) Process mapping: a cornerstone of quality improvement. Cancer Cytopathol 125:887–890. https://doi.org/10.1002/cncy.21946

Heiden B, Alieksieiev V, Volk M, Tonino-Heiden B (2021) Framing artificial intelligence (AI) additive manufacturing (AM). Procedia Comput Sci 186:387–394. https://doi.org/10.1016/j.procs.2021.04.161

Helgoson M, Kalhori V (2012) A conceptual model for knowledge integration in process planning. Procedia CIRP 3:573–578. https://doi.org/10.1016/j.procir.2012.07.098

Hermann G (1990) Artificial intelligence in monitoring and the mechanics of machining. Comput Ind 14:131–135. https://doi.org/10.1016/0166-3615(90)90113-4

Hetmaniok E (2015) Solution of the two-dimensional inverse problem of the binary alloy solidification by applying the Ant Colony Optimization algorithm. Int Commun Heat Mass Transf 67:39–45. https://doi.org/10.1016/j.icheatmasstransfer.2015.05.029

Hetmaniok E, Slota D (2014) Determination of the heat flux in the process of solidification by applying the ant colony optimization algorithm. Key Eng Mater 622–623:764–771. https://doi.org/10.4028/www.scientific.net/kem.622-623.764

Hetmaniok E, Słota D, Zielonka A (2015) Restoration of the cooling conditions in a three-dimensional continuous casting process using artificial intelligence algorithms. Appl Math Model 39:4797–4807. https://doi.org/10.1016/j.apm.2015.03.056

Hirt G, Ames J, Bambach M et al (2004) Forming strategies and process modelling for CNC incremental sheet forming. CIRP Ann 53:203–206. https://doi.org/10.1016/s0007-8506(07)60679-9

Hlaing ZCSS, Khine MA (2011) Solving traveling salesman problem by using improved ant colony optimization algorithm. Int J Inf Educ Technol 1:404–409. https://doi.org/10.7763/IJIET.2011.V1.67

Horváth L, Rudas IJ (1997) Manufacturing process modeling method for CAD/CAM and flexible manufacturing systems. Re-engineering for sustainable industrial production. Springer US, Boston, pp 471–483

Hosseini E, Ghanbari PG, Keller F et al (2021) Deploying artificial intelligence for component-scale multi-physical field simulation of metal additive manufacturing. In: Meboldt M, Klahn C (eds) Industrializing additive manufacturing. Springer International Publishing, Cham, pp 268–276

Hsu C-C, Hou R-Y, Wang W-Y (2013) Path planning for mobile robots based on improved ant colony optimization. In: 2013 IEEE international conference on systems, man, and cybernetics. IEEE, pp 2777–2782

Hu C, Wang Y (2020) An efficient convolutional neural network model based on object-level attention mechanism for casting defect detection on radiography images. IEEE Trans Ind Electron 67:10922–10930. https://doi.org/10.1109/tie.2019.2962437

Huang R, Dai N, Li D et al (2018) Parallel non-dominated sorting genetic algorithm-II for optimal part deposition orientation in additive manufacturing based on functional features. Proc Inst Mech Eng Part C J Mech Eng Sci 232:3384–3395. https://doi.org/10.1177/0954406217737105

Iarovyi S, Lastra JLM, Haber R, Del Toro R (2015) From artificial cognitive systems and open architectures to cognitive manufacturing systems. Proceeding: 2015 IEEE Int Conf Ind Informatics, INDIN 2015, pp 1225–1232. https://doi.org/10.1109/INDIN.2015.7281910

Ibrahim A, Hamdan W (2015) Application of adaptive neuro-fuzzy inference system for prediction of surface roughness in incremental sheet metal forming process. Eng Technol J 33:380–399. https://doi.org/10.30684/etj.2015.101907

Jain V, Raj T (2018) Prediction of cutting force by using ANFIS. Int J Syst Assur Eng Manag 9:1137–1146. https://doi.org/10.1007/s13198-018-0717-x

Janglová D (2004) Neural networks in mobile robot motion. Int J Adv Robot Syst 1:2. https://doi.org/10.5772/5615

Janssen MA, Jager W (2003) Simulating market dynamics: Interactions between consumer psychology and social networks. Artif Life 9(4):343–356. https://doi.org/10.1162/106454603322694807

Jenab A, Sarraf IS, Green DE et al (2016) The use of genetic algorithm and neural network to predict rate-dependent tensile flow behaviour of {AA}5182-O sheets. Mater Des 94:262–273. https://doi.org/10.1016/j.matdes.2016.01.038

Jeswiet J, Geiger M, Engel U et al (2008) Metal forming progress since 2000. CIRP J Manuf Sci Technol 1:2–17. https://doi.org/10.1016/j.cirpj.2008.06.005

Jeyapoovan T, Murugan M (2013) Surface roughness classification using image processing. Measurement 46:2065–2072. https://doi.org/10.1016/j.measurement.2013.03.014

John T, DeWitt W, Keebler JS, Min S, Nix NW, Smith CD, Zacharia ZG (2001) Defining Supply Chain Management. J Bus Logist 22(2):1–25. https://doi.org/10.1002/jbl.2001.22.issue-2. https://doi.org/10.1002/j.2158-1592.2001.tb00001.x

Jordan MI, Mitchell TM (2015) Machine learning: trends, perspectives, and prospects. Science 349:255–260. https://doi.org/10.1126/science.aaa8415

Junior P, D’Addona D, Aguiar P, Teti R (2018) Dressing tool condition monitoring through impedance-based sensors: part 2{\textemdash}neural networks and k-nearest neighbor classifier approach. Sensors 18:4453. https://doi.org/10.3390/s18124453

Kadirgama K, Noor MM, Alla ANA (2010) Response ant colony optimization of end milling surface roughness. Sensors 10:2054–2063. https://doi.org/10.3390/s100302054

Kamalahmadi M, Parast MM (2016) A review of the literature on the principles of enterprise and supply chain resilience: major findings and directions for future research. Int J Prod Econ 171:116–133. https://doi.org/10.1016/j.ijpe.2015.10.023

Kamath C, Fan YJ (2018) Regression with small data sets: a case study using code surrogates in additive manufacturing. Knowl Inf Syst 57:475–493. https://doi.org/10.1007/s10115-018-1174-1

Kang K, Hong K, Kim KH, Lee C (2017) Shipment consolidation policy under uncertainty of customer order for sustainable supply chain management. Sustainability 9:1675. https://doi.org/10.3390/su9091675

Kannan G, Noorul Haq A, Devika M (2009) Analysis of closed loop supply chain using genetic algorithm and particle swarm optimisation. Int J Prod Res 47:1175–1200. https://doi.org/10.1080/00207540701543585

Karaağaç İ (2017) The experimental investigation of springback in V-bending using the flexforming process. Arab J Sci Eng 42:1853–1864. https://doi.org/10.1007/s13369-016-2329-6

Karaboga D, Kaya E (2018) Adaptive network based fuzzy inference system ({ANFIS}) training approaches: a comprehensive survey. Artif Intell Rev 52:2263–2293. https://doi.org/10.1007/s10462-017-9610-2

Karayel E, Bozkurt Y (2020) Additive manufacturing method and different welding applications. J Mater Res Technol 9:11424–11438. https://doi.org/10.1016/j.jmrt.2020.08.039

Karl AA, Micheluzzi J, Leite LR, Pereira CR (2018) Supply chain resilience and key performance indicators: a systematic literature review. Production. https://doi.org/10.1590/0103-6513.20180020

Katsaliaki K, Galetsi P, Kumar S (2022) Supply chain disruptions and resilience: a major review and future research agenda. Ann Oper Res 319:965–1002. https://doi.org/10.1007/s10479-020-03912-1

Katuu S (2020) Enterprise resource planning: past, present, and future. New Rev Inf Netw 25:37–46. https://doi.org/10.1080/13614576.2020.1742770

Kaufman LM (2009) Data security in the world of cloud computing. IEEE Secur Priv Mag 7:61–64. https://doi.org/10.1109/MSP.2009.87

Kaur J, Gupta N (2020) Artificial neural network: a review. Int J Tech Res Sci. https://doi.org/10.30780/specialissue-ICACCG2020/007

Kerre EE (2007) An overview of fuzzy relational calculus and its applications. Modeling decisions for artificial intelligence. Springer, Berlin Heidelberg, pp 1–13

Kim HM, Choi D-H (2016) Defects detection of gas pipeline near the welds based on self quotient image and discrete cosine transform. Russ J Nondestruct Test 52:175–183. https://doi.org/10.1134/S1061830916030049

Kim A, Oh K, Jung J-Y, Kim B (2017) Imbalanced classification of manufacturing quality conditions using cost-sensitive decision tree ensembles. Int J Comput Integr Manuf 31:701–717. https://doi.org/10.1080/0951192x.2017.1407447

Kim JS, Lee CS, Kim SM, Lee SW (2018) Development of data-driven in-situ monitoring and diagnosis system of fused deposition modeling (FDM) process based on support vector machine algorithm. Int J Precis Eng Manuf Green Technol 5:479–486. https://doi.org/10.1007/s40684-018-0051-4

Kim SW, Kong JH, Lee SW, Lee S (2022) Recent advances of artificial intelligence in manufacturing industrial sectors: a review. Int J Precis Eng Manuf 23:111–129. https://doi.org/10.1007/s12541-021-00600-3

Kirisci PT, Thoben KD (2018) A method for designing physical user interfaces for intelligent production environments. Adv Hum-Comput Interact. https://doi.org/10.1155/2018/6487070

Klancnik S, Brezocnik M, Balic J (2016) Intelligent CAD/CAM system for programming of CNC machine tools. Int J Simul Model 15:109–120. https://doi.org/10.2507/IJSIMM15(1)9.330

Ko M, Tiwari A, Mehnen J (2010) A review of soft computing applications in supply chain management. Appl Soft Comput J 10:661–674. https://doi.org/10.1016/j.asoc.2009.09.004

Ko H, Witherell P, Lu Y et al (2021) Machine learning and knowledge graph based design rule construction for additive manufacturing. Addit Manuf 37:101620. https://doi.org/10.1016/j.addma.2020.101620

Koch C, Tononi G (2008) Can machines be conscious? yes - and a new turing test might prove it

Koester LW, Taheri H, Bigelow TA et al (2018) In-situ acoustic signature monitoring in additive manufacturing processes. AIP Conf Proc. https://doi.org/10.1063/1.5031503

Kozłowski J, Sika R, Górski F, Ciszak O (2019) Modeling of foundry processes in the era of Industry 40. Lecture notes in mechanical engineering. Springer International Publishing, Berlin, pp 62–71

Krishnakumar P, Rameshkumar K, Ramachandran KI (2015) Tool wear condition prediction using vibration signals in high speed machining (HSM) of Titanium (Ti-6Al-4V) alloy. Procedia Comput Sci 50:270–275. https://doi.org/10.1016/j.procs.2015.04.049

Ktari A, El Mansori M (2022) Digital twin of functional gating system in 3D printed molds for sand casting using a neural network. J Intell Manuf 33:897–909. https://doi.org/10.1007/s10845-020-01699-3

Kulvicius T, Herzog S, Lüddecke T, et al (2020) One-shot path planning for multi-agent systems using fully convolutional neural network. arXiv Prepr arXiv200400568

Kumar A, Maji K (2020) Selection of process parameters for near-net shape deposition in wire arc additive manufacturing by genetic algorithm. J Mater Eng Perform 29:3334–3352. https://doi.org/10.1007/s11665-020-04847-1

Kumar R, Kumar Sahooa A, Satyanarayana K, Venkateswara Rao G (2013) Some studies on cutting force and temperature in machining Ti-6AL-4V alloy using regression analysis and ANOVA. Int J Ind Eng Comput 4:427–436. https://doi.org/10.5267/j.ijiec.2013.03.002

Kumar U, Yadav I, Kumari S et al (2015) Defect identification in friction stir welding using discrete wavelet analysis. Adv Eng Softw 85:43–50. https://doi.org/10.1016/j.advengsoft.2015.02.001

Kumar S, Dhingra A, Singh B (2018) Lean-Kaizen implementation: a roadmap for identifying continuous improvement opportunities in Indian small and medium sized enterprise. J Eng Des Technol 16:143–160. https://doi.org/10.1108/JEDT-08-2017-0083

Kumar K, Majumdar A, Chandra MG et al (2020) TransFuse: a transform learning based multisensor fusion framework. IEEE Sensors Lett 4:1–4. https://doi.org/10.1109/LSENS.2020.3039300

Kumaran ST, Ko TJ, Kurniawan R et al (2017) ANFIS modeling of surface roughness in abrasive waterjet machining of carbon fiber reinforced plastics. J Mech Sci Technol 31:3949–3954. https://doi.org/10.1007/s12206-017-0741-9

Kumari S, Jain R, Kumar U et al (2016) Defect identification in friction stir welding using continuous wavelet transform. J Intell Manuf. https://doi.org/10.1007/s10845-016-1259-1

Kurada S, Bradley C (1997) A review of machine vision sensors for tool condition monitoring. Comput Ind 34:55–72. https://doi.org/10.1016/s0166-3615(96)00075-9

Lahoti G, Pratihar DK (2017) Recurrent neural networks to model input-output relationships of metal inert gas (MIG) welding process. Int J Data Anal Tech Strateg 9:248. https://doi.org/10.1504/IJDATS.2017.086629

Larkin RD, Lopez J, Butts JW, Grimaila MR (2014) Evaluation of security solutions in the SCADA environment. ACM SIGMIS Database DATABASE Adv Inf Syst 45:38–53. https://doi.org/10.1145/2591056.2591060

Lau HCW, Cheng ENM, Lee CKM, Ho GTS (2008) A fuzzy logic approach to forecast energy consumption change in a manufacturing system. Expert Syst Appl 34:1813–1824. https://doi.org/10.1016/j.eswa.2007.02.015

Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444. https://doi.org/10.1038/nature14539

Lee J, Noh S, Kim H-J, Kang Y-S (2018) Implementation of cyber-physical production systems for quality prediction and operation control in metal casting. Sensors 18:1428. https://doi.org/10.3390/s18051428

Leem C-W, Chen JJ-G (1996) Fuzzy-set-based machine-cell formation in cellular manufacturing. J Intell Manuf 7:355–364. https://doi.org/10.1007/BF00123911

Leo Kumar SP, Jerald J, Kumanan S, Prabakaran R (2014) A review on current research aspects in tool-based micromachining processes. Mater Manuf Process 29:1291–1337. https://doi.org/10.1080/10426914.2014.952037

Leung CW, Wong TN, Mak KL, Fung RYK (2010) Integrated process planning and scheduling by an agent-based ant colony optimization. Comput Ind Eng 59:166–180. https://doi.org/10.1016/j.cie.2009.09.003

Li X (2014) Operations management of logistics and supply chain: Issues and directions. Discret Dyn Nat Soc. https://doi.org/10.1155/2014/701938

Li Y, Zhang X (2014) Diffusion maps based k-nearest-neighbor rule technique for semiconductor manufacturing process fault detection. Chemom Intell Lab Syst 136:47–57. https://doi.org/10.1016/j.chemolab.2014.05.003

Li X, Mao W, Zeng D, Wang FY (2008) Agent-based social simulation and modeling in social computing. Lect Notes Comput Sci 5075:401–412. https://doi.org/10.1007/978-3-540-69304-8_41

Li Z, Zhang Z, Shi J, Wu D (2019) Prediction of surface roughness in extrusion-based additive manufacturing with machine learning. Robot Comput Integr Manuf 57:488–495. https://doi.org/10.1016/j.rcim.2019.01.004

Li G, Wang Y, He J et al (2020) Tool wear state recognition based on gradient boosting decision tree and hybrid classification RBM. Int J Adv Manuf Technol 110:511–522. https://doi.org/10.1007/s00170-020-05890-x

Li C, Chen Y, Shang Y (2022) A review of industrial big data for decision making in intelligent manufacturing. Eng Sci Technol an Int J 29:101021. https://doi.org/10.1016/j.jestch.2021.06.001

Liang YC, Li WD, Lu X, Wang S (2019) Fog computing and convolutional neural network enabled prognosis for machining process optimization. J Manuf Syst 52:32–42. https://doi.org/10.1016/j.jmsy.2019.05.003

Liao TW, Ting C-F, Qu J, Blau PJ (2007) A wavelet-based methodology for grinding wheel condition monitoring. Int J Mach Tools Manuf 47:580–592. https://doi.org/10.1016/j.ijmachtools.2006.05.008

Lim S, Hahn J (2005) Optimization of forecasting supply chain management sustainable collaboration using hybrid artificial neural network. In: Han M, Qin S, Zhang N (eds) Lecture notes in computer science. Springer International Publishing, Cham, pp 1052–1057

Lin K-P, Tseng M-L, Pai P-F (2018) Sustainable supply chain management using approximate fuzzy DEMATEL method. Resour Conserv Recycl 128:134–142. https://doi.org/10.1016/j.resconrec.2016.11.017

Lingitz L, Gallina V, Ansari F et al (2018) Lead time prediction using machine learning algorithms: a case study by a semiconductor manufacturer. Procedia CIRP 72:1051–1056. https://doi.org/10.1016/j.procir.2018.03.148

Litke A, Anagnostopoulos D, Varvarigou T (2019) Blockchains for supply chain management: architectural elements and challenges towards a global scale deployment. Logistics 3:5. https://doi.org/10.3390/logistics3010005

Liu X, Ahmad F, Yamazaki K, Mori M (2005) Adaptive interpolation scheme for NURBS curves with the integration of machining dynamics. Int J Mach Tools Manuf 45:433–444. https://doi.org/10.1016/j.ijmachtools.2004.09.009

Liu X, Ni Z, Qiu X (2015) Application of ant colony optimization algorithm in integrated process planning and scheduling. Int J Adv Manuf Technol 84:393–404. https://doi.org/10.1007/s00170-015-8145-4

Liu Q, Lu Y, Xie C (2006a) Optimal genetic fuzzy obstacle avoidance controller of autonomous mobile robot based on ultrasonic sensors. In: 2006a IEEE international conference on robotics and biomimetics. IEEE, pp 125–129

Liu Q, Lu Y-G, Xie C-X (2006b) Fuzzy obstacle-avoiding controller of autonomous mobile robot optimized by genetic algorithm under multi-obstacles environment. In: 2006b 6th world congress on intelligent control and automation. IEEE, pp 3255–3259

Liu W, Wang D, Long S et al (2019) Service supply chain management: a behavioural operations perspective. Mod Supply Chain Res Appl 1:28–53. https://doi.org/10.1108/mscra-01-2019-0003

Lu Y, Wang W, Xue L (2020) A hybrid CNN-LSTM architecture for path planning of mobile robots in unknow environments. In: 2020 Chinese control and decision conference (CCDC). IEEE, pp 4775–4779

Lu K-Y, Sy C-C (2009) A real-time decision-making of maintenance using fuzzy agent. Expert Syst Appl 36:2691–2698. https://doi.org/10.1016/j.eswa.2008.01.087

Luo Q, He Y (2016) A cost-effective and automatic surface defect inspection system for hot-rolled flat steel. Robot Comput Integr Manuf 38:16–30. https://doi.org/10.1016/j.rcim.2015.09.008

Luo Y, Zhou M, Caudill RJ (2001) An integrated e-supply chain model for agile and environmentally conscious manufacturing. IEEE/ASME Trans Mechatr 6:377–386. https://doi.org/10.1109/3516.974851

Luo Q, Fang X, Liu L et al (2020) Automated visual defect detection for flat steel surface: a survey. IEEE Trans Instrum Meas 69:626–644. https://doi.org/10.1109/tim.2019.2963555

Ma H, Wei S, Lin T et al (2010) Binocular vision system for both weld pool and root gap in robot welding process. Sens Rev 30:116–123. https://doi.org/10.1108/02602281011022706

Ma Y, Zhang Y, Luo X (2019) Automatic recognition of machining features based on point cloud data using convolution neural networks. ACM Int Conf Proc Ser. https://doi.org/10.1145/3349341.3349407

Macchiaroli R, Mole S, Riemma S (1999) Modelling and optimization of industrial manufacturing processes subject to no-wait constraints. Int J Prod Res 37:2585–2607. https://doi.org/10.1080/002075499190671

Maiyar LM, Singh S, Prabhu V, Tiwari MK (2019) Part segregation based on particle swarm optimisation for assembly design in additive manufacturing. Int J Comput Integr Manuf 32:705–722. https://doi.org/10.1080/0951192X.2019.1610577

Mak KL, Wong YS, Wang XX (2000) An adaptive genetic algorithm for manufacturing cell formation. Int J Adv Manuf Technol 16:491–497. https://doi.org/10.1007/s001700070057

Manoochehri M, Kolahan F (2014) Integration of artificial neural network and simulated annealing algorithm to optimize deep drawing process. Int J Adv Manuf Technol 73:241–249. https://doi.org/10.1007/s00170-014-5788-5

Marmolejo-Saucedo JA, Hurtado-Hernandez M, Suarez-Valdes R (2020) Digital twins in supply chain management: a brief literature review. Adv Intell Syst Comput 1072:653–661. https://doi.org/10.1007/978-3-030-33585-4_63

Martin C, Matthias H (2017) Supply chain 2.0 revisited: a framework for managing volatility-induced risk in the supply chain. Int J Phys Distrib Logist Manag 47:2–17. https://doi.org/10.1108/IJPDLM-09-2016-0245

Mauder T, Sandera C, Stetina J (2015) Optimal control algorithm for continuous casting process by using fuzzy logic. Steel Res Int 86:785–798. https://doi.org/10.1002/srin.201400213

Mckinnon A (2008) Integrated logistics strategies. Emerald Group Publishing, Bingley, pp 157–170

Melo MT, Nickel S, Saldanha-Da-Gama F (2012) A tabu search heuristic for redesigning a multi-echelon supply chain network over a planning horizon. Int J Prod Econ 136:218–230. https://doi.org/10.1016/j.ijpe.2011.11.022

Mery D (2020) Aluminum casting inspection using deep learning: a method based on convolutional neural networks. J Nondestruct Eval 39:55. https://doi.org/10.1007/s10921-020-0655-9

Meryem U (2016) A comprehensive insight into the Six Sigma DMAIC toolbox. Int J Lean Six Sigma 7:406–429. https://doi.org/10.1108/IJLSS-10-2015-0040

Metaxiotis KS, Askounis D, Psarras J (2002) Expert systems in production planning and scheduling: a state-of-the-art survey. J Intell Manuf 13:253–260. https://doi.org/10.1023/A:1016064126976

Mikolajczyk T, Wasiak P (2012) Machining with image recognition using industrial robot. Appl Mech Mater 186:50–57. https://doi.org/10.4028/www.scientific.net/amm.186.50

Milazzo M, Libonati F (2022) The synergistic role of additive manufacturing and artificial intelligence for the design of new advanced intelligent systems. Adv Intell Syst 4:2100278. https://doi.org/10.1002/aisy.202100278

Min H (2010) Artificial intelligence in supply chain management: theory and applications. Int J Logist Res Appl 13:13–39. https://doi.org/10.1080/13675560902736537

Mishra D, Gupta A, Raj P et al (2020) Real time monitoring and control of friction stir welding process using multiple sensors. CIRP J Manuf Sci Technol. https://doi.org/10.1016/j.cirpj.2020.03.004

Mishra D, Gupta A, Raj P et al (2021) Sensor based real-time information for monitoring and control of a manufacturing process. Eng Res Express 3:025040. https://doi.org/10.1088/2631-8695/ac0777

Mishra D, Pal SK, Chakravarty D (2021) Industry 4.0 in welding. Springer, Berlin, pp 253–298

Mishra D, Shree S, Gupta A et al (2021c) Weld defect localization in friction stir welding process. Weld World 65:451–461. https://doi.org/10.1007/s40194-020-01028-5

Mohanty CP, Mahapatra SS, Singh MR (2014) A particle swarm approach for multi-objective optimization of electrical discharge machining process. J Intell Manuf 27:1171–1190. https://doi.org/10.1007/s10845-014-0942-3

Moore PJ (1970) The factor of safety against undrained failure of a slope. Soils Found 10:81–91. https://doi.org/10.3208/sandf1960.10.3_81

Moriwaki T, Shirase K (2006) Intelligent machine tools: current status and evolutional architecture. Int J Manuf Technol Manag 9:204. https://doi.org/10.1504/ijmtm.2006.010055

Motlagh O, Nakhaeinia D, Tang SH et al (2014) Automatic navigation of mobile robots in unknown environments. Neural Comput Appl 24:1569–1581. https://doi.org/10.1007/s00521-013-1393-z

Mukherjee A, Das S (2021) A simple online tool condition monitoring system using artificial neural networks. IOP Conf Ser Mater Sci Eng 1080:12021. https://doi.org/10.1088/1757-899x/1080/1/012021

Myles AJ, Feudale RN, Liu Y et al (2004) An introduction to decision tree modeling. J Chemom 18:275–285. https://doi.org/10.1002/cem.873

Mypati O, Sahu S, Pal SK, Srirangam P (2022) An investigation of mechanical and electrical properties of friction stir welded Al and Cu busbar for battery pack applications. Mater Chem Phys 287:126373. https://doi.org/10.1016/j.matchemphys.2022.126373

Nain SS, Garg D, Kumar S (2017) Evaluation and analysis of cutting speed, wire wear ratio, and dimensional deviation of wire electric discharge machining of super alloy Udimet-L605 using support vector machine and grey relational analysis. Adv Manuf 6:225–246. https://doi.org/10.1007/s40436-017-0192-7

Narayanaswamy P, Bector CR, Rajamani D (1996) Fuzzy logic concepts applied to machine—component matrix formation in cellular manufacturing. Eur J Oper Res 93:88–97. https://doi.org/10.1016/0377-2217(95)00036-4

Narooei KD, Ramli R (2014) New approaches in tool path optimization of CNC machining: a review. Appl Mech Mater 663:657–661. https://doi.org/10.4028/www.scientific.net/amm.663.657

Nguyen T, Zhou L, Spiegler V et al (2018) Big data analytics in supply chain management: a state-of-the-art literature review. Comput Oper Res 98:254–264. https://doi.org/10.1016/j.cor.2017.07.004

Ni D, Xiao Z, Lim MK (2020) A systematic review of the research trends of machine learning in supply chain management. Int J Mach Learn Cybern 11:1463–1482. https://doi.org/10.1007/s13042-019-01050-0

Nikula R-P, Karioja K (2018) The effect of steel leveler parameters on vibration feature. In: Proceedings of The 9th {EUROSIM} congress on modelling and simulation, {EUROSIM} 2016, the 57th {sims} conference on simulation and modelling {SIMS} 2016. Linköping University Electronic Press, pp 433–438

Nizam MSH, Marizan S, Zaki SA, Mohd Zamzuri AR (2016) Vision based identification and classification of weld defects in welding environments: a review. Indian J Sci Technol 9:83–89. https://doi.org/10.17485/ijst/2016/v9i20/82779

Nti IK, Adekoya AF, Weyori BA, Nyarko-Boateng O (2022) Applications of artificial intelligence in engineering and manufacturing: a systematic review. J Intell Manuf 33:1581–1601. https://doi.org/10.1007/s10845-021-01771-6

Oh S, Jung Y, Kim S et al (2019) Deep generative design: Integration of topology optimization and generative models. J Mech Des Trans ASME 141:29. https://doi.org/10.1115/1.4044229

Omairi A, Ismail ZH (2021) Towards machine learning for error compensation in additive manufacturing. Appl Sci 11:1–27. https://doi.org/10.3390/app11052375

Oraon M, Sharma V (2018) Predicting force in single point incremental forming by using artificial neural network. Int J Eng. https://doi.org/10.5829/ije.2018.31.01a.13

Ortt R, Stolwijk C, Punter M (2020) Implementing Industry 4.0: assessing the current state. J Manuf Technol Manag 31:825–836. https://doi.org/10.1108/JMTM-07-2020-0284

Osakada K, Mori K, Altan T, Groche P (2011) Mechanical servo press technology for metal forming. CIRP Ann 60:651–672. https://doi.org/10.1016/j.cirp.2011.05.007

Pal S, Pal SK, Samantaray AK (2008a) Neurowavelet packet analysis based on current signature for weld joint strength prediction in pulsed metal inert gas welding process. Sci Technol Weld Join 13:638–645. https://doi.org/10.1179/174329308X299986

Pal S, Pal SK, Samantaray AK (2008b) Artificial neural network modeling of weld joint strength prediction of a pulsed metal inert gas welding process using arc signals. J Mater Process Technol 202:464–474. https://doi.org/10.1016/j.jmatprotec.2007.09.039

Pal S, Pal SK, Samantaray AK (2008c) Sensor based weld bead geometry prediction in pulsed metal inert gas welding process through artificial neural networks. Int J Knowl-Based Intell Eng Syst 12:101–114. https://doi.org/10.3233/KES-2008-12202

Pal K, Bhattacharya S, Pal SK (2009) Prediction of metal deposition from arc sound and weld temperature signatures in pulsed MIG welding. Int J Adv Manuf Technol 45:1113–1130. https://doi.org/10.1007/s00170-009-2052-5

Pal K, Bhattacharya S, Pal SK (2010a) Multisensor-based monitoring of weld deposition and plate distortion for various torch angles in pulsed MIG welding. Int J Adv Manuf Technol 50:543–556. https://doi.org/10.1007/s00170-010-2523-8

Pal K, Bhattacharya S, Pal SK (2010b) Investigation on arc sound and metal transfer modes for on-line monitoring in pulsed gas metal arc welding. J Mater Process Technol 210:1397–1410. https://doi.org/10.1016/j.jmatprotec.2010.03.029

Pal K, Bhattacharya S, Pal SK (2011) Optimisation of weld deposition efficiency in pulsed MIG welding using hybrid neuro-based techniques. Int J Comput Integr Manuf 24:198–210. https://doi.org/10.1080/0951192X.2010.542181

Pal SK, Mishra D, Pal A et al (2022) Digital twin: fundamental concepts to applications in advanced manufacturing. Springer International Publishing, Cham

Palanisamy D, Senthil P (2016) Development of ANFIS model and machinability study on dry turning of cryo-treated PH stainless steel with various inserts. Mater Manuf Process 32:654–669. https://doi.org/10.1080/10426914.2016.1221091

Panagariya A (2004) India in the 1980’s and 1990’s: a triumph of reforms. IMF Work Pap 04:1. https://doi.org/10.5089/9781451846355.001

Pandiyan V, Caesarendra W, Tjahjowidodo T, Tan HH (2018) In-process tool condition monitoring in compliant abrasive belt grinding process using support vector machine and genetic algorithm. J Manuf Process 31:199–213. https://doi.org/10.1016/j.jmapro.2017.11.014

Panigrahi SS, Bahinipati B, Jain V (2019) Sustainable supply chain management: a review of literature and implications for future research. Manag Environ Qual 30:1001–1049. https://doi.org/10.1108/MEQ-01-2018-0003

Papandrea PJ, Frigieri EP, Maia PR et al (2020) Surface roughness diagnosis in hard turning using acoustic signals and support vector machine: a PCA-based approach. Appl Acoust 159:107102. https://doi.org/10.1016/j.apacoust.2019.107102

Patra K, Pal SK, Bhattacharyya K (2007a) Artificial neural network based prediction of drill flank wear from motor current signals. Appl Soft Comput J 7:929–935. https://doi.org/10.1016/j.asoc.2006.06.001

Patra K, Pal SK, Bhattacharyya K (2007b) Application of wavelet packet analysis in drill wear monitoring. Mach Sci Technol 11:413–432. https://doi.org/10.1080/10910340701539908

Patra K, Jha AK, Szalay T et al (2017) Artificial neural network based tool condition monitoring in micro mechanical peck drilling using thrust force signals. Precis Eng 48:279–291. https://doi.org/10.1016/j.precisioneng.2016.12.011

Patwari AU, Arif MD, Chowdhury MSI, Chowdhury NA (2012) Identifications of machined surfaces using digital image processing. Int J Eng 1:213–218

Peng Y (2004) Intelligent condition monitoring using fuzzy inductive learning. J Intell Manuf 15:373–380. https://doi.org/10.1023/B:JIMS.0000026574.95637.36

Peng C, Wang L, Liao TW (2015) A new method for the prediction of chatter stability lobes based on dynamic cutting force simulation model and support vector machine. J Sound Vib 354:118–131. https://doi.org/10.1016/j.jsv.2015.06.011

Peres RS, Jia X, Lee J et al (2020) Industrial artificial intelligence in industry 4.0: systematic review, challenges and outlook. IEEE Access. https://doi.org/10.1109/ACCESS.2020.3042874

Peterson EWF (2017) The role of population in economic growth. SAGE Open. https://doi.org/10.1177/2158244017736094

Petty MD, Kim J, Barbosa SE, Pyun JJ (2014) Software frameworks for model composition. Model Simul Eng. https://doi.org/10.1155/2014/492737

Pinto MJA, Mendes JV (2017) Operational practices of lean manufacturing: potentiating environmental improvements. J Ind Eng Manag 10:550–580. https://doi.org/10.3926/jiem.2268

Pires JN, Loureiro A, Godinho T et al (2003) Welding robots. IEEE Robot Autom Mag 10:45–55. https://doi.org/10.1109/MRA.2003.1213616

Podder B, Banerjee P, Kumar KR, Hui NB (2017) Development of ANFIS model for flow forming of solution annealed H30 aluminium tubes. Solid State Phenom 261:378–385. https://doi.org/10.4028/www.scientific.net/ssp.261.378

Ponticelli GS, Guarino S, Giannini O (2017) A fuzzy logic-based model in laser-assisted bending springback control. Int J Adv Manuf Technol 95:3887–3898. https://doi.org/10.1007/s00170-017-1482-8

Pradhan SK, Parhi DR, Panda AK (2009) Fuzzy logic techniques for navigation of several mobile robots. Appl Soft Comput 9:290–304. https://doi.org/10.1016/j.asoc.2008.04.008

Purian FK, Sadeghian E (2013) Mobile robots path planning using ant colony optimization and Fuzzy Logic algorithms in unknown dynamic environments. In: 2013 international conference on control, automation, robotics and embedded systems (CARE). IEEE, pp 1–6

Qin J, Liu Y, Grosvenor R et al (2020) Deep learning-driven particle swarm optimisation for additive manufacturing energy optimisation. J Clean Prod. https://doi.org/10.1016/j.jclepro.2019.118702

Quatrini E, Costantino F, Di Gravio G, Patriarca R (2020) Machine learning for anomaly detection and process phase classification to improve safety and maintenance activities. J Manuf Syst 56:117–132. https://doi.org/10.1016/j.jmsy.2020.05.013

Raja P, Pugazhenthi S (2009) Path planning for mobile robots in dynamic environments using particle swarm optimization. In: 2009 International conference on advances in recent technologies in communication and computing. IEEE, pp 401–405

Rajesh R, Dev Anand M (2012) The optimization of the electro-discharge machining process using response surface methodology and genetic algorithms. Procedia Eng 38:3941–3950. https://doi.org/10.1016/j.proeng.2012.06.451

Ranjan R, Khan AR, Parikh C et al (2016) Classification and identification of surface defects in friction stir welding: an image processing approach. J Manuf Process 22:237–253. https://doi.org/10.1016/j.jmapro.2016.03.009

Rao AA, Sujatha K, Saragada N, et al (2015) Automation of metal charge calculations using support vector machine. In: 2015 International conference on man and machine interfacing (MAMI). IEEE, pp 1–5

Rathee G, Ahmad F, Iqbal R, Mukherjee M (2021) Cognitive automation for smart decision-making in industrial internet of things. IEEE Trans Ind Inform 17:2152–2159. https://doi.org/10.1109/TII.2020.3013618

Rauch E, Matt DT (2021) Status of the implementation of Industry 4.0 in SMEs and framework for smart manufacturing. In: Matt DT, Modrák V, Zsifkovits H (eds) Implementing Industry 4.0 in SMEs. Springer International Publishing, Cham, pp 3–26

Ravichandran KS, Chandra Sekhara Rao K (2001) A new approach to fuzzy part-family formation in cellular manufacturing systems. Int J Adv Manuf Technol 18:591–597. https://doi.org/10.1007/s001700170036

Ren L, Wang W, Du Z (2012) A new fuzzy intelligent obstacle avoidance control strategy for wheeled mobile robot. In: 2012 IEEE international conference on mechatronics and automation. IEEE, pp 1732–1737

Restrepo IM, Balakrishnan S (2008) Fuzzy-based methodology for multi-objective scheduling in a robot-centered flexible manufacturing cell. J Intell Manuf 19:421–432. https://doi.org/10.1007/s10845-008-0093-5

Riahi Y, Saikouk T, Gunasekaran A, Badraoui I (2021) Artificial intelligence applications in supply chain: a descriptive bibliometric analysis and future research directions. Expert Syst Appl 173:114702. https://doi.org/10.1016/j.eswa.2021.114702

Riegler M, Spangl B, Weigl M et al (2013) Simulation of a real-time process adaptation in the manufacture of high-density fibreboards using multivariate regression analysis and feedforward control. Wood Sci Technol 47:1243–1259. https://doi.org/10.1007/s00226-013-0571-6

Rifai AP, Aoyama H, Tho NH et al (2020) Evaluation of turned and milled surfaces roughness using convolutional neural network. Measurement 161:107860. https://doi.org/10.1016/j.measurement.2020.107860

Röhm B, Gögelein L, Kugler S, Anderl R (2021) AI-driven worker assistance system for additive manufacturing. Adv Intell Syst Comput 1213:22–27. https://doi.org/10.1007/978-3-030-51328-3_4

Rostamzadeh R, Sabaghi M, Esmaili A (2013) Evaluation of cost-effectiveness criteria in supply chain management: case study. Adv Decis Sci. https://doi.org/10.1155/2013/873534

Rowlands H, Wang LR (2000) An approach of fuzzy logic evaluation and control in SPC. Qual Reliab Eng Int 16:91–98. https://doi.org/10.1002/(SICI)1099-1638(200003/04)16:2%3c91::AID-QRE307%3e3.0.CO;2-9

Roy RB, Ghosh A, Bhattacharyya S et al (2018) Weld defect identification in friction stir welding through optimized wavelet transformation of signals and validation through X-ray micro-CT scan. Int J Adv Manuf Technol 99:623–633. https://doi.org/10.1007/s00170-018-2519-3

Roy RB, Mishra D, Pal SK et al (2020) Digital twin: current scenario and a case study on a manufacturing process. Int J Adv Manuf Technol 107:3691–3714. https://doi.org/10.1007/s00170-020-05306-w

Rubio EM, Teti R, Baciu IL (2006) Advanced signal processing in acoustic emission monitoring systems for machining technology. Intelligent production machines and systems. Elsevier, Amsterdam, pp 1–6

Sahoo AK (2014) Application of Taguchi and regression analysis on surface roughness in machining hardened AISI D2 steel. Int J Ind Eng Comput. https://doi.org/10.5267/j.ijiec.2013.11.001

Saikaew C, Wiengwiset S (2012) Optimization of molding sand composition for quality improvement of iron castings. Appl Clay Sci 67–68:26–31. https://doi.org/10.1016/j.clay.2012.07.005

Sanders DA, Lambert G, Graham-Jones J et al (2010) A robotic welding system using image processing techniques and a CAD model to provide information to a multi-intelligent decision module. Assem Autom 30:323–332. https://doi.org/10.1108/01445151011075780

Sang S (2013) Supply Chain contracts with multiple retailers in a fuzzy demand environment. Math Probl Eng 2013:1–12. https://doi.org/10.1155/2013/482353

Sangwan KS, Saxena S, Kant G (2015) Optimization of machining parameters to minimize surface roughness using integrated ANN}-{GA approach. Procedia CIRP 29:305–310. https://doi.org/10.1016/j.procir.2015.02.002

Sankar BR, Umamaheswarrao P (2018) Multi objective optimization of CFRP composite drilling using ant colony algorithm. Mater Today Proc 5:4855–4860. https://doi.org/10.1016/j.matpr.2017.12.061

Santhanakrishnan M, Sivasakthivel PS, Sudhakaran R (2015) Modeling of geometrical and machining parameters on temperature rise while machining Al 6351 using response surface methodology and genetic algorithm. J Braz Soc Mech Sci Eng 39:487–496. https://doi.org/10.1007/s40430-015-0378-5

Sari DY, Wu T-L, Lin B-T (2016) Preliminary study for online monitoring during the punching process. Int J Adv Manuf Technol 88:2275–2285. https://doi.org/10.1007/s00170-016-8956-y

Schaefer D (2014) Cloud-based design and manufacturing (CBDM). Springer International Publishing, Cham

Scime L, Singh A, Paquit V (2022) A scalable digital platform for the use of digital twins in additive manufacturing. Manuf Lett 31:28–32. https://doi.org/10.1016/j.mfglet.2021.05.007

Sen B, Mandal UK, Mondal SP (2017) Advancement of an intelligent system based on ANFIS for predicting machining performance parameters of Inconel 690: a perspective of metaheuristic approach. Measurement 109:9–17. https://doi.org/10.1016/j.measurement.2017.05.050

Seyedan M, Mafakheri F (2020) Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities. J Big Data. https://doi.org/10.1186/s40537-020-00329-2

Shabgard MR, Badamchizadeh MA, Ranjbary G, Amini K (2013) Fuzzy approach to select machining parameters in electrical discharge machining ({EDM}) and ultrasonic-assisted EDM processes. J Manuf Syst 32:32–39. https://doi.org/10.1016/j.jmsy.2012.09.002

Sharifzadeh M, Alirezaee S, Amirfattahi R, Sadri S (2008) Detection of steel defect using the image processing algorithms. In: 2008 IEEE international multitopic conference. IEEE, pp 125–127

Shaw M, Whinston A (1985) Automatic planning and flexible scheduling: a knowledge-based approach. In: Proceedings. 1985 IEEE International Conference on Robotics and Automation. IEEE, pp 890–894

Haiming Shen (2016) A study of welding robot path planning application based on Genetic Ant Colony Hybrid Algorithm. In: 2016 IEEE Advanced information management, communicates, electronic and automation control conference (IMCEC). IEEE, pp 1743–1746

Shettigar AK, Patel GCM, Chate GR et al (2020) Artificial bee colony, genetic, back propagation and recurrent neural networks for developing intelligent system of turning process. SN Appl Sci. https://doi.org/10.1007/s42452-020-2475-z

Shi T, Kong J, Wang X et al (2016) Improved Sobel algorithm for defect detection of rail surfaces with enhanced efficiency and accuracy. J Cent South Univ 23:2867–2875. https://doi.org/10.1007/s11771-016-3350-3

Shijing Wu, Qunh Li, Enyong Zhu, et al (2008) Fuzzy controller of pipeline robot navigation optimized by genetic algorithm. In: 2008 Chinese Control and Decision Conference. IEEE, pp 904–908

Silbernagel C, Aremu A, Ashcroft I (2020) Using machine learning to aid in the parameter optimisation process for metal-based additive manufacturing. Rapid Prototyp J 26:625–637. https://doi.org/10.1108/RPJ-08-2019-0213

Sindi CT, Najafabadi MA, Salehi M (2013a) Tribological behavior of sheet metal forming process using acoustic emission characteristics. Tribol Lett 52:67–79. https://doi.org/10.1007/s11249-013-0193-z

Sindi CT, Najafabadi MA, Salehi M (2013b) Wavelet-based acoustic emission characterization of surface damages during experimental simulation of sheet metal forming process. Proc Inst Mech Eng Part J J Eng Tribol 228:253–265. https://doi.org/10.1177/1350650113502470

Singh MK, Parhi DR (2011) Path optimisation of a mobile robot using an artificial neural network controller. Int J Syst Sci 42:107–120. https://doi.org/10.1080/00207720903470155

Singh AK, Debnath T, Dey V, Rai RN (2017) An approach to maximize weld penetration during TIG welding of P91 steel plates by utilizing image processing and taguchi orthogonal array. J Inst Eng Ser C 98:541–551. https://doi.org/10.1007/s40032-016-0268-3

Skrzat A (2011) Fuzzy logic application to strain-stress analysis in selected elastic-plastic material models. Arch Metall Mater. https://doi.org/10.2478/v10172-011-0060-6

Song K, Yan Y (2013) A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects. Appl Surf Sci 285:858–864. https://doi.org/10.1016/j.apsusc.2013.09.002

Song L, Huang W, Han X, Mazumder J (2017) Real-time composition monitoring using support vector regression of laser-induced plasma for laser additive manufacturing. IEEE Trans Ind Electron 64:633–642. https://doi.org/10.1109/TIE.2016.2608318

Soroor J, Tarokh MJ, Keshtgary M (2009) Preventing failure in IT-enabled systems for supply chain management. Int J Prod Res 47:6543–6557. https://doi.org/10.1080/00207540802314837

Spector L (2006) Evolution of artificial intelligence. Artif Intell 170:1251–1253. https://doi.org/10.1016/j.artint.2006.10.009

Subashini S, Kavitha V (2011) A survey on security issues in service delivery models of cloud computing. J Netw Comput Appl 34:1–11. https://doi.org/10.1016/j.jnca.2010.07.006

Subramanian P, Ramkumar N, Narendran TT, Ganesh K (2012) A technical note on Analysis of closed loop supply chain using genetic algorithm and particle swarm optimisation. Int J Prod Res 50:593–602. https://doi.org/10.1080/00207543.2011.593348

Sun H, Ma L (2020) Generative design by using exploration approaches of reinforcement learning in density-based structural topology optimization. Designs 4:1–20. https://doi.org/10.3390/designs4020010

Sutrisno A, Lee T (2012) Service reliability assessment using failure mode and effect analysis (FMEA): survey and opportunity roadmap. Int J Eng Sci Technol 3:25–38. https://doi.org/10.4314/ijest.v3i7.3s

Swift KG, Booker JD (2013) Casting processes. Manufacturing process selection handbook. Elsevier, Amsterdam, pp 61–91

Syafrudin M, Alfian G, Fitriyani N, Rhee J (2018) Performance analysis of IoT-based sensor, big data processing, and machine learning model for real-time monitoring system in automotive manufacturing. Sensors 18:2946. https://doi.org/10.3390/s18092946

Tan G-Z (2007) Ant colony system algorithm for real-time globally optimal path planning of mobile robots. ACTA Autom Sin 33:0279. https://doi.org/10.1360/aas-007-0279

Tan WC, Sidhu MS (2022) Review of RFID and IoT integration in supply chain management. Oper Res Perspect 9:100229. https://doi.org/10.1016/j.orp.2022.100229

Tang B, Kong J, Wang X, Chen L (2009) Surface inspection system of steel strip based on machine vision. In: 2009 first international workshop on database technology and applications. IEEE

Toorajipour R, Sohrabpour V, Nazarpour A et al (2021) Artificial intelligence in supply chain management: a systematic literature review. J Bus Res 122:502–517. https://doi.org/10.1016/j.jbusres.2020.09.009

Tootooni MS, Liu C, Roberson D et al (2016) Online non-contact surface finish measurement in machining using graph theory-based image analysis. J Manuf Syst 41:266–276. https://doi.org/10.1016/j.jmsy.2016.09.007

Torres-Treviño LM, Escamilla-Salazar IG, González-Ortíz B, Praga-Alejo R (2013) An expert system for setting parameters in machining processes. Expert Syst Appl 40:6877–6884. https://doi.org/10.1016/j.eswa.2013.06.051

Tsoukalas VD (2011) An adaptive neuro-fuzzy inference system ({ANFIS}) model for high pressure die casting. Proc Inst Mech Eng Part B 225:2276–2286. https://doi.org/10.1177/0954405411406054

Ubhayaratne I, Pereira MP, Xiang Y, Rolfe BF (2017) Audio signal analysis for tool wear monitoring in sheet metal stamping. Mech Syst Signal Process 85:809–826. https://doi.org/10.1016/j.ymssp.2016.09.014

Upadhyay R, Asi A, Nayak P et al (2022) Real-time deep learning–based image processing for pose estimation and object localization in autonomous robot applications. Int J Adv Manuf Technol. https://doi.org/10.1007/s00170-022-09994-4

Usamentiaga R, Garcia DF, Molleda J et al (2014) Vibrations in steel strips: effects on flatness measurement and filtering. IEEE Trans Ind Appl 50:3103–3112. https://doi.org/10.1109/tia.2014.2308359

Uyan T, Otto K, Silva MS et al (2022) Industry 4.0 foundry data management and supervised machine learning in low-pressure die casting quality improvement. Int J Met. https://doi.org/10.1007/s40962-022-00783-z

Vaissier B, Pernot JP, Chougrani L, Véron P (2019) Genetic-algorithm based framework for lattice support structure optimization in additive manufacturing. CAD Comput Aided Des 110:11–23. https://doi.org/10.1016/j.cad.2018.12.007

Varun A, Venkaiah N (2014) Simultaneous optimization of WEDM responses using grey relational analysis coupled with genetic algorithm while machining EN 353. Int J Adv Manuf Technol 76:675–690. https://doi.org/10.1007/s00170-014-6198-4

Vecchio VC, Fenu G, Pellegrino FA et al (2019) Support Vector Representation Machine for superalloy investment casting optimization. Appl Math Model 72:324–336. https://doi.org/10.1016/j.apm.2019.02.033

Verdier G, Ferreira A (2011) Adaptive mahalanobis distance and k-nearest neighbor rule for fault detection in semiconductor manufacturing. IEEE Trans Semicond Manuf 24:59–68. https://doi.org/10.1109/TSM.2010.2065531

Viet NQ, Behdani B, Bloemhof J (2020) Data-driven process redesign: anticipatory shipping in agro-food supply chains. Int J Prod Res 58:1302–1318. https://doi.org/10.1080/00207543.2019.1629673

Vimal KEK, Vinodh S (2013) Application of artificial neural network for fuzzy logic based leanness assessment. J Manuf Technol Manag 24:274–292. https://doi.org/10.1108/17410381311292340

Vishal V, Ramya R, Vinay Srinivas P, Vimal Samsingh R (2019) A review of implementation of artificial intelligence systems for weld defect classification. Mater Today Proc 16:579–583. https://doi.org/10.1016/j.matpr.2019.05.131

Vogel D, Balakrishnan R (2010) Direct pen interaction with a conventional graphical user interface. Hum-Comput Interact 25:324–388. https://doi.org/10.1080/07370024.2010.499839

Wang L, Mehrabi MG, Kannatey-Asibu E (2002) Hidden Markov model-based tool wear monitoring in turning. J Manuf Sci Eng 124:651–658. https://doi.org/10.1115/1.1475320

Wang G, Zhao K, Li X et al (2011) Arrangement optimization of hammers and fenders on Scrap Metal Shredder using ant colony algorithms. J Vib Control 18:659–670. https://doi.org/10.1177/1077546311405560

Wang RJ, Zeng J, Zhou D (2012) Determination of temperature difference in squeeze casting hot work tool steel. Int J Mater Form 5:317–324. https://doi.org/10.1007/s12289-011-1061-8

Wang GF, Yang YW, Zhang YC, Xie QL (2014) Vibration sensor based tool condition monitoring using $\upnu$ support vector machine and locality preserving projection. Sens Actuators A 209:24–32. https://doi.org/10.1016/j.sna.2014.01.004

Wang X, Xue L, Yan Y, Gu X (2017) Welding robot collision-free path optimization. Appl Sci 7:89. https://doi.org/10.3390/app7020089

Wang J, Ma Y, Zhang L et al (2018) Deep learning for smart manufacturing: methods and applications. J Manuf Syst 48:144–156. https://doi.org/10.1016/j.jmsy.2018.01.003

Wang Y, Fang Y, Lou P et al (2020) Deep reinforcement learning based path planning for mobile robot in unknown environment. J Phys Conf Ser 1576:012009. https://doi.org/10.1088/1742-6596/1576/1/012009

Wang D, Snooks R (2021) Artificial intuitions of generative design: an approach based on reinforcement learning. In: Yuan PF, Yao J, Yan C et al (eds) Procedings of the 2020 DigitalFUTURES. Springer Singapore, Singapore, pp 189–198

Wang C, Li S, Zeng D, Zhu X (2021) Quantification and compensation of thermal distortion in additive manufacturing: a computational statistics approach. Comput Methods Appl Mech Eng 375:113611. https://doi.org/10.1016/j.cma.2020.113611

Wang M, Wu Y, Chen B, Evans M (2021) Blockchain and supply chain management: a new paradigm for supply chain integration and collaboration. Oper Supply Chain Manag 14:111–122. https://doi.org/10.31387/oscm0440290

Wanke P, Alvarenga H, Correa H, Hadi-Vencheh A, Azad MAK (2017) Fuzzy inference systems and inventory allocation decisions: Exploring the impact of priority rules on total costs and service levels Expert Syst Appl 85:182–193. https://doi.org/10.1016/j.eswa.2017.05.043

Xiaochuan Wang, Yang SX (2003) A neuro-fuzzy approach to obstacle avoidance of a nonholonomic mobile robot. In: Proceedings 2003 IEEE/ASME international conference on advanced intelligent mechatronics (AIM 2003). IEEE, pp 29–34

Wei B, Gao W (2021) Image processing of Casting defects based on Convolutional neural network. J Phys Conf Ser. https://doi.org/10.1088/1742-6596/2137/1/012059

Wei S, Ma H, Lin T, Chen S (2010) Autonomous guidance of initial welding position with “single camera and double positions” method. Sens Rev 30:62–68. https://doi.org/10.1108/02602281011010808

Weichert D, Link P, Stoll A et al (2019) A review of machine learning for the optimization of production processes. Int J Adv Manuf Technol 104:1889–1902. https://doi.org/10.1007/s00170-019-03988-5

Wells LJ, Camelio JA, Williams CB, White J (2014) Cyber-physical security challenges in manufacturing systems. Manuf Lett 2:74–77. https://doi.org/10.1016/j.mfglet.2014.01.005

Wolfgang K, Thorsten B, Christian R (2019) Artificial intelligence and digital transformation in supply chain management

Wu D, Rosen DW, Wang L, Schaefer D (2015) Cloud-based design and manufacturing: a new paradigm in digital manufacturing and design innovation. CAD Comput Aided Des 59:1–14. https://doi.org/10.1016/j.cad.2014.07.006

Wu M, Zhou H, Lin LL et al (2017) Detecting attacks in cybermanufacturing systems: additive manufacturing example. MATEC Web Conf 108:8–11. https://doi.org/10.1051/matecconf/201710806005

Wu H, Yu Z, Wang Y (2019) Experimental study of the process failure diagnosis in additive manufacturing based on acoustic emission. Meas J Int Meas Confed 136:445–453. https://doi.org/10.1016/j.measurement.2018.12.067

Wu H, Yu Z, Wang Y (2016) A new approach for online monitoring of additive manufacturing based on acoustic emission, pp 1–8. https://doi.org/10.1115/msec2016-8551

Xia C, Pan Z, Zhang S et al (2020) Model-free adaptive iterative learning control of melt pool width in wire arc additive manufacturing. Int J Adv Manuf Technol 110:2131–2142. https://doi.org/10.1007/s00170-020-05998-0

Xie S, Guo Y (2012) Optimisation of machining parameters in multi-pass turnings using ant colony optimisations. Int J Mach Mach Mater 11:204–220. https://doi.org/10.1504/IJMMM.2012.045983

Xu K, Li Y (2018) Digital image approach to tool path generation for surface machining. Int J Adv Manuf Technol 101:2547–2558. https://doi.org/10.1007/s00170-018-3118-z

Xu G, Wen J, Wang C (2009) Zhang X (2009) Quality monitoring for resistance spot welding using dynamic signals. IEEE Int Conf Mechatr Autom ICMA 2009:2495–2499. https://doi.org/10.1109/ICMA.2009.5246513

Xu L, Huang C, Li C et al (2020) An improved case based reasoning method and its application in estimation of surface quality toward intelligent machining. J Intell Manuf 32:313–327. https://doi.org/10.1007/s10845-020-01573-2

Xu D, Wang L, Tan M (2004) Image processing and visual control method for arc welding robot. Proc - 2004 IEEE Int Conf Robot Biomimetics, IEEE ROBIO 2004 727–732. https://doi.org/10.1109/robio.2004.1521871

Yadav D, Chhabra D, Gupta RK et al (2020) Modeling and analysis of significant process parameters of FDM 3D printer using ANFIS. Mater Today Proc 21:1592–1604. https://doi.org/10.1016/j.matpr.2019.11.227

Yan R, Sun H, Qian Y (2013) Energy-aware sensor node design with its application in wireless sensor networks. IEEE Trans Instrum Meas 62:1183–1191. https://doi.org/10.1109/TIM.2013.2245181

Yanrong Hu, Yang SX, Li-Zhong Xu, Meng M-H (2004) A knowledge based genetic algorithm for path planning in unstructured mobile robot environments. In: 2004 IEEE international conference on robotics and biomimetics. IEEE, pp 767–772

Yao B, Imani F, Sakpal AS et al (2018) Multifractal analysis of image profiles for the characterization and detection of defects in additive manufacturing. J Manuf Sci Eng Trans ASME 140:1–21. https://doi.org/10.1115/1.4037891

Yazdani AA, Tavakkoli-Moghaddam R (2012) Integration of the fish bone diagram, brainstorming, and AHP method for problem solving and decision making: a case study. Int J Adv Manuf Technol 63:651–657. https://doi.org/10.1007/s00170-012-3916-7

Yeganefar A, Niknam SA, Asadi R (2019) The use of support vector machine, neural network, and regression analysis to predict and optimize surface roughness and cutting forces in milling. Int J Adv Manuf Technol 105:951–965. https://doi.org/10.1007/s00170-019-04227-7

Yoo M, Ham N (2020) Productivity analysis of documentation based on 3D model in plant facility construction project. Appl Sci. https://doi.org/10.3390/app10031126

Yoon J, He D, Van Hecke B (2014) A PHM approach to additive manufacturing equipment health monitoring, fault diagnosis, and quality control. Proc Annu Conf Progn Heal Manag Soc 2014:732–740

You D, Gao X, Katayama S (2014) Monitoring of high-power laser welding using high-speed photographing and image processing. Mech Syst Signal Process 49:39–52. https://doi.org/10.1016/j.ymssp.2013.10.024

Yousef N, Parmar C, Sata A (2022) Intelligent inspection of surface defects in metal castings using machine learning. Mater Today Proc 67:517–522. https://doi.org/10.1016/j.matpr.2022.06.474

Zainal N, Zain AM, Radzi NHM, Othman MR (2014) Glowworm swarm optimization ({GSO}) for optimization of machining parameters. J Intell Manuf 27:797–804. https://doi.org/10.1007/s10845-014-0914-7

Zaman DM, Zerin NH (2017) Applying DMAIC methodology to reduce defects of sewing section in RMG: a case study. Am J Ind Bus Manag 07:1320–1329. https://doi.org/10.4236/ajibm.2017.712093

Zhang W (2000) State-space search: algorithms, complexity, extensions, and applications. Comput Math with Appl 40:417. https://doi.org/10.1016/S0898-1221(00)90185-8

Zhang H, Hou Y, Zhang J et al (2015) A new method for nondestructive quality evaluation of the resistance spot welding based on the radar chart method and the decision tree classifier. Int J Adv Manuf Technol 78:841–851. https://doi.org/10.1007/s00170-014-6654-1

Zhang X, Wang S, Yi L et al (2016) An integrated ant colony optimization algorithm to solve job allocating and tool scheduling problem. Proc Inst Mech Eng Part B 232:172–182. https://doi.org/10.1177/0954405416636038

Zhang Z, Jaiswal P, Rai R (2018) FeatureNet: machining feature recognition based on 3D convolution neural network. CAD Comput Aided Des 101:12–22. https://doi.org/10.1016/j.cad.2018.03.006

Zhang X, Liu Y, Wu X, Niu Z (2019) Intelligent pulse analysis of high-speed electrical discharge machining using different {RNNs}. J Intell Manuf 31:937–951. https://doi.org/10.1007/s10845-019-01487-8

Zhang X, Kano M, Tani M et al (2020) Prediction and causal analysis of defects in steel products: handling nonnegative and highly overdispersed count data. Control Eng Pract 95:104258. https://doi.org/10.1016/j.conengprac.2019.104258

Zhao C, Wu Z (2000) A genetic algorithm for manufacturing cell formation with multiple routes and multiple objectives. Int J Prod Res 38:385–395. https://doi.org/10.1080/002075400189473

Zhao D, Wang Y, Liang D, Ivanov M (2020a) Performances of regression model and artificial neural network in monitoring welding quality based on power signal. J Mater Res Technol 9:1231–1240. https://doi.org/10.1016/j.jmrt.2019.11.050

Zhao J, Ji M, Feng B (2020b) Smarter supply chain: a literature review and practices. J Data, Inf Manag 2:95–110. https://doi.org/10.1007/s42488-020-00025-z

Zheng H, Cong M, Dong H et al (2017) CAD-based automatic path generation and optimization for laser cladding robot in additive manufacturing. Int J Adv Manuf Technol 92:3605–3614. https://doi.org/10.1007/s00170-017-0384-0

Zhou J, Wang B, Lin J, Fu L (2013) Optimization of an aluminum alloy anti-collision side beam hot stamping process using a multi-objective genetic algorithm. Arch Civ Mech Eng 13:401–411. https://doi.org/10.1016/j.acme.2013.01.008

Zhou Z, Wen C, Yang C (2015) Fault detection using random projections and k-nearest neighbor rule for semiconductor manufacturing processes. IEEE Trans Semicond Manuf 28:70–79. https://doi.org/10.1109/TSM.2014.2374339

Zhou Y, Sun B, Sun W (2020) A tool condition monitoring method based on two-layer angle kernel extreme learning machine and binary differential evolution for milling. Meas J Int Meas Confed 166:108186. https://doi.org/10.1016/j.measurement.2020.108186

Zhu L, Feng R, Li X et al (2019) A tree-shaped support structure for additive manufacturing generated by using a hybrid of particle swarm optimization and greedy algorithm. J Comput Inf Sci Eng. https://doi.org/10.1115/1.4043530

Xiong G, Helo P (2006) An application of cost-effective fuzzy inventory controller to counteract demand fluctuation caused by bullwhip effect. Int J Prod Res 44(24):5261–5277. https://doi.org/10.1080/00207540600600114