New approach for robust multi-objective optimization of turning parameters using probabilistic genetic algorithm

Sahali, Mohand Akli; Belaidi, Idir; Serra, Roger

doi:10.1007/s00170-015-7526-z

Cited by 12 publications

(2 citation statements)

References 29 publications

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“…Isight software is used for multi-objective optimization. Isight [46,47] is engineering design development system software developed by the Ph.D. of the Massachusetts Institute of Technology. It is recognized by the world as a "software robot" that integrates CAD/CAM/CAE and PDM systems.…”

Section: Spin Forming Response Surface Model and Optimizationmentioning

confidence: 99%

Study on optimization of thermal spinning process of accumulator shell

Zhu

et al. 2020

Mechanics & Industry

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In view of the shortcomings of the existing hot spinning process technology of the accumulator shell, a method for optimizing the multi-spinning process parameters is proposed. The Johnson-Cook constitutive model of the accumulator shell material – 34CrMo4 alloy steel − was established with its parameters obtained experimentally. The finite element simulation was carried out for the hot spinning and closing process. Based on which, three parameters with the greatest influence on the spinning formation were studied: spinning temperature, spindle speed and friction coefficient. Combined with the central composite test, the response surface model and the mapping relationship between the three parameters and the maximum mises stress as well as the maximum wall thickness increment of the shell were established. The Pareto optimized solution set was obtained through multi-objective optimization. Under the condition of not affecting product quality, the optimized solution with low spinning temperature and high spindle speed is selected to reduce energy loss and improve work efficiency. The results indicate that the optimized process is experimentally verified to reduce the process temperature by nearly 30 °C, and the efficiency is increased by 25%.

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Section: Spin Forming Response Surface Model and Optimizationmentioning

confidence: 99%

Study on optimization of thermal spinning process of accumulator shell

Zhu

et al. 2020

Mechanics & Industry

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“…To address the optimization problem, they utilized the non-dominated sorting genetic algorithm (NSGA-II) with three objectives: production cost, operation time, and tool wear, and three cutting parameters: depth of cut, feed, and spindle speed. Sahali, Belaidi [28] conducted research on the optimization of turning mild steel using a carbide cutting tool using the probabilistic non-dominated sorting genetic algorithm (P-NSGA-II) with two objectives: production cost and production rate, and three variables: depth of cut, feed, and cutting speed. Abbas, Pimenov [29] employed an ANN algorithm with the Edgeworth-Pareto method to optimize the technological parameters used for milling on a CNC machine.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective Optimization of CNC Milling Parameters of 7075 Aluminium Alloy Using Response Surface Methodology

2023

jjmie

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Optimizing the impact of technological parameters during CNC milling remains a highly practical research direction, hence there have been numerous published research works in this area. This article introduces the results of a multi-parameter optimization study when milling aluminum alloy 7075 on a CNC machine using the Response Surface Methodology (RSM). The experiments were conducted based on the Taguchi L18 orthogonal array with machining parameters including coolant condition, spindle speed, feed rate, and depth of cut. The response parameters in these experiments were surface roughness (Ra) and Material Removal Rate (MRR) measurements.The research results showed that regression models developed for Ra and MRR using the RSM method have high coefficient of determination (R 2 ) values of 97.67% and 99.36%, respectively, indicating that the developed models, coefficient models are significant. The ANOVA analysis results indicate that machining parameters have a direct impact on both Ra and MRR. Ra is affected by various factors including spindle speed, feed rate, coolant, and depth of cut. Among these factors, spindle speed has the highest impact with a percentage of 37.12%, followed by feed rate at 12.56%, coolant at 12.07%, and depth of cut at 10.13%. On the other hand, the material removal rate (MRR) is mostly influenced by feed rate and depth of cut, with percentages of 41.68% and 47.29%, respectively. Multiobjective optimization using RSM showed that under the conditions of coolant on, spindle speed of 5500 rpm, feed rate of 450 mm/min and depth of cut of 0.369 mm, the optimum values of Ra and MRR obtained are 0.159 µm and 32.019 g/min, respectively.According to the results of the confirmation experiment conducted to determine the optimal values for Ra and MRR, it was found that the deviation did not exceed 5%. This result is completely acceptable in practical production, thereby affirming the accuracy of the RSM method in solving multi-objective optimization problems in the aluminum alloy CNC miling.

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