Machining process parameters optimization using soft computing technique

被引：1
作者：
El Hossainy T.M. [1 ,2 ]
Zeyada Y. [3 ]
Abdelkawy A. [1 ]
机构：
[1] Mechanical Design and Production Department, Faculty of Engineering, Cairo University, Giza
[2] Industrial Systems Engineering Department, Faculty of Engineering, MSA University
[3] Projects and Services Europe & Africa, Schneider Electric, Cairo
来源：
Journal of Engineering and Applied Science | 2023年 / 70卷 / 01期
关键词：
Fuzzy logic; Genetic algorithm; Machining; Machining power; Machining time; Neural network; Optimization; Surface quality; Surface roughness;
D O I：
10.1186/s44147-023-00174-z
中图分类号：
学科分类号：
摘要：
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. © 2023, The Author(s).
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  • [1] Cus U., Zuperl F., Approach to optimization of cutting conditions by using artificial neural networks, J Mater Process Technol, 173, 3, pp. 281-290, (2006)
  • [2] Usca U.A., Et al., Estimation, optimization and analysis based investigation of the energy consumption in machinability of ceramic-based metal matrix composite materials, J Mater Res Technol, 17, pp. 2987-2998, (2022)
  • [3] Dote Y., Ovaska S.J., Industrial applications of soft computing: a review, Proc IEEE, 89, 9, pp. 1243-1265, (2001)
  • [4] Abdelkawy A., El H., 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, (2021)
  • [5] Amiolemhen A.O.A., Ibhadode P.E., 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, pp. 1403-1412, (2004)
  • [6] Abdelkawy A., Modelling of cutting force and surface roughness of ultrasonic-assisted drilling using artificial neural network, J Eng Appl Sci, 69, 50, pp. 1-18, (2022)
  • [7] Li R., Li H.X., Guan X.P., Du R., 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, pp. 506-512, (2004)
  • [8] Ramesh K., Karunamoorthy S., Palanikumar L., Fuzzy modeling and analysis of machining parameters in machining titanium alloy, Mater Manuf Process, 23, pp. 439-447, (2008)
  • [9] Sivarajan S., Elango M., Sasikumar M., Doss A.S.A., Prediction of surface roughness in hard machining of EN31 steel with TiAlN coated cutting tool using fuzzy logic, Mater Today Proc, 65, pp. 35-41, (2022)
  • [10] Baseri H., Design of adaptive neuro-fuzzy inference system for estimation of grinding performance, Mater Manuf Process, 26, pp. 757-763, (2011)
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