Predictive modeling of quality characteristics – A case study with the casting industry

Computers in Industry - Tập 146 - Trang 103855 - 2023
Janak Suthar1, Jinil Persis2, Ruchita Gupta1
1National Institute of Industrial Engineering (NITIE), 400087 Mumbai, India
2Quantitative Methods and Operations Management Area, Indian Institute of Management (IIM) Kozhikode, Kozhikode, 673570, Kerala, India

Tài liệu tham khảo

Akhyar, 2022, Evaluation of cast defects in ship propeller of recycled aluminum alloy, Metalurgija, 61, 309 Alaka, 2019, A Big Data analytics approach for construction firms failure prediction models, IEEE Trans. Eng. Manag., 66, 689, 10.1109/TEM.2018.2856376 Antony, 2021, Revisiting Ishikawa’s original seven basic tools of quality control: a global study and some new insights, IEEE Trans. Eng. Manag., 1 Ayoola, 2010, Effect of casting mould on mechanical properties of 6063 Aluminum alloy, TMS Annu. Meet., 1, 719 Bae, 2018, Effect of additives on the sand burning of inorganic binder in Al-Si7Mg alloy casting, J. Korean Inst. Met. Mater., 56, 327 Behera, 2022, Parametric appraisal of strength & hardness of resin compacted sand-castings using hybrid Taguchi-WASPAS-Material Generation Algorithm, Mater. Today Proc., 50, 1226, 10.1016/j.matpr.2021.08.104 Caiazzo, 2022, Towards Zero Defect Manufacturing paradigm: A review of the state-of-the-art methods and open challenges, Comput. Ind., 134, 10.1016/j.compind.2021.103548 Chokkalingam, 2021, Identification of the Root Causes for Blowhole Defect in Castings Using Quantitative Risk Ishikawa Diagrams, J. Adv. Manuf. Syst., 1 Christou, 2022, End-to-end industrial IoT platform for Quality 4.0 applications, Comput. Ind., 137, 10.1016/j.compind.2021.103591 Cruz, 2021, On the manufacture of naval propellers by using self-hardening sand molds made by robotic arms, Int. J. Adv. Manuf. Technol., 116, 1751, 10.1007/s00170-021-07492-7 Farhang Mehr, 2014, Effect of chill cooling conditions on cooling rate, microstructure and casting/chill interfacial heat transfer coefficient for sand-cast A319 alloy, Int. J. Cast. Met. Res., 27, 288, 10.1179/1743133614Y.0000000105 Foseco Ferrous Foundryman’s Handbook, 2000, Sands and green sand, 146 Giannetti, 2014, A novel variable selection approach based on co-linearity index to discover optimal process settings by analysing mixed data, Comput. Ind. Eng., 72, 217, 10.1016/j.cie.2014.03.017 Gigante, 2010, How can we become a practical green foundry industry, Int. J. Met., 4, 7 Guharaja, 2006, Optimization of green sand-casting process parameters by using Taguchi’s method, Int. J. Adv. Manuf. Technol., 30, 1040, 10.1007/s00170-005-0146-2 Guttman, 1945, A basis for analyzing test-retest reliability, Psychometrika, 10, 255, 10.1007/BF02288892 Hair, 2019, Multivariate data analysis, Prentice Hall. He, 2019, High-accuracy and high-performance WAAM propeller manufacture by cylindrical surface slicing method, Int. J. Adv. Manuf. Technol., 105, 4773, 10.1007/s00170-019-04558-5 Holtzer, 2016, Influence of a reclaimed sand addition to moulding sand with furan resin on its impact on the environment, Water Air. Soil Pollut., 227, 10.1007/s11270-015-2707-9 Hsia, 2020, Parameter selection for linear support vector regression, IEEE Trans. Neural Netw. Learn. Syst., 1 Ince, 2006, Non-parametric regression methods, Comput. Manag. Sci., 3, 161, 10.1007/s10287-005-0006-4 Ishak, 2017, Effect on the Mechanical Properties of Ship Propeller with Vibration Mold Castings, Adv. Sci. Lett., 23, 4378, 10.1166/asl.2017.8856 Jafari, 2010, Influence of gating system, sand grain size, and mould coating on microstructure and mechanical properties of thin-wall ductile iron, J. Iron Steel Res. Int., 17, 38, 10.1016/S1006-706X(10)60195-1 Jakubski, 2013, Ann modelling for the analysis of the green moulding sands properties, Arch. Metall. Mater., 58, 961, 10.2478/amm-2013-0110 Kalpakjian, 2009, 1205 Khandelwal, 2016, Effect of molding parameters on chemically bonded sand mold properties, J. Manuf. Process., 22, 127, 10.1016/j.jmapro.2016.03.007 Kopper, 2020, Model Selection and Evaluation for Machine Learning: Deep Learning in Materials Processing, Integr. Mater. Manuf. Innov., 9, 287, 10.1007/s40192-020-00185-1 Kovačević, 2014, Dependence of interfacial heat transfer coefficient on casting surface temperature during solidification of Al–Si alloy castings cast in CO2 sand mold, Heat. Mass Transf., 50, 1115, 10.1007/s00231-014-1326-0 Kulkarni, 2007, Prediction of solidification time during solidification of aluminum base alloy castings cast in CO2-sand mold, Int. J. Adv. Manuf. Technol., 34, 1098, 10.1007/s00170-006-0671-7 Kumar, 2011, Optimization of green sand-casting process parameters of a foundry by using Taguchi’s method, Int. J. Adv. Manuf. Technol., 55, 23, 10.1007/s00170-010-3029-0 Kumaravadivel, 2013, Application of Six-Sigma DMAIC methodology to sand-casting process with response surface methodology, Int. J. Adv. Manuf. Technol., 69, 1403, 10.1007/s00170-013-5119-2 Kumaravadivel, 2012, Determining the optimum green sand-casting process parameters using Taguchi’s method, J. Chin. Inst. Ind. Eng., 29, 148 Kumari, 2019, Single-measure and multi-measure approach of predictive manufacturing control: A comparative study, Comput. Ind. Eng., 127, 182, 10.1016/j.cie.2018.12.018 Li, 2022, A kernel regression approach for identification of first order differential equations based on functional data, Appl. Math. Lett., 127, 10.1016/j.aml.2021.107832 Lin, 2017, Random forests-based extreme learning machine ensemble for multi-regime time series prediction, Expert Syst. Appl., 83, 164, 10.1016/j.eswa.2017.04.013 Liu, 2022, Materials Studio simulation for the adsorption properties of CO2 molecules at the surface of sodium silicate and potassium silicate solution under different pressure conditions, Int. J. Met., 16, 242 Noorul Haq, 2009, Parameter optimization of CO2 casting process by using Taguchi method, Int. J. Interact. Des. Manuf., 3, 41, 10.1007/s12008-008-0054-4 Noorul Haq, 2009, Parameter optimization of CO2 casting process by using Taguchi method, Int. J. Interact. Des. Manuf., 3, 41, 10.1007/s12008-008-0054-4 Noyel, 2016, Reconfiguration process for neuronal classification models: Application to a quality monitoring problem, Comput. Ind., 83, 78, 10.1016/j.compind.2016.09.004 Papananias, 2019, A Bayesian framework to estimate part quality and associated uncertainties in multistage manufacturing, Comput. Ind., 105, 35, 10.1016/j.compind.2018.10.008 Parappagoudar, 2008, Forward and reverse mappings in green sand mould system using neural networks, Appl. Soft Comput. J., 8, 239, 10.1016/j.asoc.2007.01.005 Persis, 2020, Improving patient care at a multi-speciality hospital using lean six sigma, Prod. Plan. Control. Piaseczny, 2007, The effect of blade thickness on microstructure and mechanical properties of ship’s sand-cast propeller, Pol. Marit. Res., 14, 15, 10.2478/v10012-007-0034-9 Powell, 2022, Advancing zero defect manufacturing: A state-of-the-art perspective and future research directions, Comput. Ind., 136, 10.1016/j.compind.2021.103596 Pulisheru, 2020, Effect of pouring temperature on hot tearing susceptibility of Al-Cu cast Alloy: Casting simulation, Mater. Today Proc., 47, 7086, 10.1016/j.matpr.2021.06.182 Rabbii, 2001, Sodium silicate glass as an inorganic binder in foundry industry, Iran. Polym. J. (Engl. Ed.), 10, 229 Rajkumar, 2021, Experimental and simulation analysis on multi-gate variants in sand-casting process, J. Manuf. Process, 62, 119, 10.1016/j.jmapro.2020.12.006 Ramasubramanian, 2019, Machine Learning Using R: With Time Series and Industry-Based Use Cases in R, Front. Artif. Intell. Appl., 344 Ranade, 2021, Implementation of DMAIC methodology in green sand-casting process, Mater. Today Proc., 42, 500, 10.1016/j.matpr.2020.10.475 Rao, 2018, Optimization of green sand mould system using Taguchi based grey relational analysis, China Foundry, 15, 151 Revelle, 2019, Reliability from α to ω: A tutorial, Psychol. Assess., 31, 1395, 10.1037/pas0000754 Rocha, 2021, Collaborations for Digital Transformation: Case Studies of Industry 4.0 in Brazil, IEEE Trans. Eng. Manag, 1 Sahoo, 2021, Investigation of the Foundry Properties of the Locally Available Sands for Metal Casting, Silicon, 13, 3765, 10.1007/s12633-020-00677-x Saikaew, 2012, Optimization of molding sand composition for quality improvement of iron castings, Appl. Clay Sci. 67–, 68, 26, 10.1016/j.clay.2012.07.005 Shinde, 2013, Optimization of Mold Yield in MultiCavity Sand-castings, J. Mater. Eng. Perform., 22, 1574, 10.1007/s11665-012-0458-y Sikder, 2020, A synergistic Mahalanobis–Taguchi system and support vector regression based predictive multivariate manufacturing process quality control approach, J. Manuf. Syst., 57, 323, 10.1016/j.jmsy.2020.10.003 Silva, 2020, Lean green—the importance of integrating environment into lean philosophy—a case study, Lect. Notes Netw. Syst., 211, 10.1007/978-3-030-41429-0_21 Singh, 2022, Development and Implementation of Autonomous Quality Management System (AQMS) in an Automotive Manufacturing using Quality 4.0 Concept– A Case Study, Comput. Ind. Eng., 168, 10.1016/j.cie.2022.108121 Sinha, 2021, Influence of Mold Material on the Mold Stability for Foundry Use, Silicon Sipper, 2022, AddGBoost: A gradient boosting-style algorithm based on strong learners, Mach. Learn. Appl., 7 Surekha, 2012, Multi-objective optimization of green sand mould system using evolutionary algorithms, Int. J. Adv. Manuf. Technol., 58, 9, 10.1007/s00170-011-3365-8 J. Suthar, J. Persis, R. Gupta, Analytical modeling of quality parameters in casting process – Learning-based approach, Int. J. Qual. Reliab. Manag., n.d. Suthar, 2021, Critical parameters influencing the quality of metal castings: a systematic literature review, Int. J. Qual. Reliab. Manag. Ahead--p Sysoev, 2019, A smoothed monotonic regression via L2 regularization, Knowl. Inf. Syst., 59, 197, 10.1007/s10115-018-1201-2 Teixeira, 2021, Connecting lean and green with sustainability towards a conceptual model, J. Clean. Prod., 322, 10.1016/j.jclepro.2021.129047 Tiedje, 2010, Emission of organic compounds from mould and core binders used for casting iron, aluminium and bronze in sand moulds, J. Environ. Sci. Heal. - Part A Toxic. /Hazard. Subst. Environ. Eng., 45, 1866 G. Tsoumakas, I. Katakis, I. Vlahavas, Data Mining and Knowledge Discovery Handbook, 2010. https://doi.org/10.1017/CBO9781107415324.004. Vanli, 2009, Bayesian approaches for on-line robust parameter design, IIE Trans. (Inst. Ind. Eng., 41, 359 Vijayan, 2022, Adaptive non-linear soft sensor for quality monitoring in refineries using Just-in-Time Learning—Generalized regression neural network approach, Appl. Soft Comput., 119 Vijayaram, 2006, Foundry quality control aspects and prospects to reduce scrap rework and rejection in metal casting manufacturing industries, J. Mater. Process. Technol., 178, 39, 10.1016/j.jmatprotec.2005.09.027 Wang, 2013, Simulation study on three casting processes for a marine propeller hub body, China Foundry, 10, 360 Wang, 2011, Hazardous air pollutant formation from pyrolysis of typical Chinese casting materials, Environ. Sci. Technol., 45, 6539, 10.1021/es200310p Yang, 2017, A regression tree approach using mathematical programming, Expert Syst. Appl., 78, 347, 10.1016/j.eswa.2017.02.013 Youn, 2003, Interference-free tool path generation in five-axis machining of a marine propeller, Int. J. Prod. Res., 41, 4383, 10.1080/0020754031000153342 Yurdakul, 2014, A decision support system for selection of net-shape primary manufacturing processes, Int. J. Prod. Res., 52, 1528, 10.1080/00207543.2013.848489 Zhao, 2017, Effect of binder system on accuracy and property of micro-jetting and bonding formed sand molds, Zhuzao/Foundry, 66, 223 Zheng, 2022, An influence modelling and analysis method of reducing carbon emissions for mould forming processes in patternless sand-casting, Int. J. Prod. Res., 1