Machining process parameters optimization using soft computing technique
Tóm tắt
This work introduces an approach for optimization machinability measures of power consumption, machining time, and the surface roughness (PMS). This approach is starting with market customer’s demands, passing by optimizing the machinability measures (PMS), and ending by the optimized cutting conditions. The fuzzy logic was used to define the weights of each of required machinability measurement using method through expert rules depending on factory requirements. Genetic algorithm was formulated for giving optimum output values based on the customer’s demands. A neural network was designed for controlling the input cutting conditions with the PMS output parameters. The proposed soft computing technique creates reasonable results compared to experimental results and gives rich investigations for optimizing the output parameters not only for increasing productivity and quality demands but also for saving power consumed. The variation of consumed power, machining time, and surface roughness was calculated based on different customer demand levels. When the machining time and power consumed importance increased, the proposed technique reduced them by about 20% and 10% for the testes case.
Từ khóa
Tài liệu tham khảo
Cus U, Zuperl F (2006) Approach to optimization of cutting conditions by using artificial neural networks. J Mater Process Technol 173(3):281–290
Usca ÜA et al (2022) Estimation, optimization and analysis based investigation of the energy consumption in machinability of ceramic-based metal matrix composite materials. J Mater Res Technol 17:2987–2998. https://doi.org/10.1016/j.jmrt.2022.02.055
Dote Y, Ovaska SJ (2001) Industrial applications of soft computing: a review. Proc IEEE 89(9):1243–1265
Abdelkawy A, El H (2021) Experimental and statistical study for measurements of surface roughness and hole geometry of ultrasonic - assisted drilling of soda glass. J Braz Soc Mech Sci Eng 5. https://doi.org/10.1007/s40430-021-03172-5
Amiolemhen AOA, Ibhadode PE (2004) Application of genetic algorithms— determination of the optimal machining parameters in the conversion of a cylindrical bar stock into a continuous finished profile. Int J Mach Tools Manuf 44:1403–1412
Abdelkawy A (2022) Modelling of cutting force and surface roughness of ultrasonic-assisted drilling using artificial neural network. J Eng Appl Sci 69(50):1–18
Li R, Li HX, Guan XP, Du R (2004) Fuzzy estimation of feed-cutting force from current measurement—a case study on intelligent tool wear condition monitoring. IEEE Trans Syst Man Cybern C Appl Rev 34(4):506–512
Ramesh K, Karunamoorthy S, Palanikumar L (2008) Fuzzy modeling and analysis of machining parameters in machining titanium alloy. Mater Manuf Process 23:439–447
Sivarajan S, Elango M, Sasikumar M, Doss ASA (2022) Prediction of surface roughness in hard machining of EN31 steel with TiAlN coated cutting tool using fuzzy logic. Mater Today Proc 65:35–41. https://doi.org/10.1016/j.matpr.2022.04.161
Baseri H (2011) Design of adaptive neuro-fuzzy inference system for estimation of grinding performance. Mater Manuf Process 26:757–763
Rao GKM, Rangajanardhaa G, Rao DH, Rao MS (2009) Development of hybrid model and optimization of surface roughness in electric discharge machining using artificial neural networks and genetic algorithm. J Mater Process Technol 209(3):1512–1520
Jiao ES, Lei Y, Pei S, Lee ZJ (2004) Fuzzy adaptive networks in machining process modeling: surface roughness prediction for turning operations. Int J Mach Tools Manuf 44(15):1643–1651
Rao MS, Janardhana GKM, Rao GR, Rao DH (2008) Development of hybrid model and optimization of metal removal rate in electric discharge machining using artificial neural networks and genetic algorithm. ARPN J Eng Appl Sci 3(1):2008
Sada SO (2021) Improving the predictive accuracy of artificial neural network ( ANN ) approach in a mild steel turning operation. Int J Adv Manuf Technol 112:2389–2398
Ozel Y, Karpat T (2005) Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. Int J Mach Tools Manuf 45:467–479
Vasanth XA, Paul PS, Varadarajan AS (2020) A neural network model to predict surface roughness during turning of hardened SS410 steel. Int J Syst Assur Eng Manag 11(3):704–715. https://doi.org/10.1007/s13198-020-00986-9
Mohd Adnan MRH, Sarkheyli A, Mohd Zain A, Haron H (2015) Fuzzy logic for modeling machining process: a review. Artif Intell Rev 43(3):345–379. https://doi.org/10.1007/s10462-012-9381-8
Bobyr MV, Kulabukhov SA (2017) Simulation of control of temperature mode in cutting area on the basis of fuzzy logic. J Mach Manuf Reliab 46(3):288–295. https://doi.org/10.3103/S1052618817030049
Chakraborty S, Das PP (2019) Fuzzy modeling and parametric analysis of non-traditional machining processes. Manag Prod Eng Rev 10(3):111–123. https://doi.org/10.24425/mper.2019.130504
Jegaraj NR, Babu JJR (2007) A soft computing approach for controlling the quality of cut with abrasive waterjet cutting system experiencing orifice and focusing tube wear. J Mater Process Technol 185:217–722
Das B, Roy S, Rai RN, Saha SC (2016) Application of grey fuzzy logic for the optimization of CNC milling parameters for Al–4.5%Cu–TiC MMCs with multi-performance characteristics. Eng Sci Technol Int J 19(2):857–865. https://doi.org/10.1016/j.jestch.2015.12.002
Chowdhury SR, Das PP, Chakraborty S (2022) Optimization of CNC turning of aluminium 6082-T6 alloy using fuzzy multi-criteria decision making methods: a comparative study. Int J Interact Des Manuf. https://doi.org/10.1007/s12008-022-01049-y
Zain S, Haron AM, Sharif H (2011) Optimization of process parameters in the abrasive waterjet machining using integrated SA–GA. Appl Soft Comput 11(8):5350–5359
Roy SS (2006) Design of genetic-fuzzy expert system for predicting surface finish in ultra-precision diamond turning of metal matrix composite. J Mater Process Technol 173:337–344
Rashad RM, El-Hossainy TM (2006) Machinability of 7116 structural aluminum alloy. Mater Manuf Process 21(1):23–27. https://doi.org/10.1080/AMP-200060603
Pandey S, Hindoliya DA, Mod R (2012) Artificial neural networks for predicting indoor temperature using roof passive cooling techniques in buildings in different climatic conditions. Appl Soft Comput J 12(3):1214–1226. https://doi.org/10.1016/j.asoc.2011.10.011
Das PP, Chakraborty S (2022) SWARA-CoCoSo method-based parametric optimization of green dry milling processes. J Eng Appl Sci 69(1):1–21. https://doi.org/10.1186/s44147-022-00087-3