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Predictive Modeling of Surface Roughness and Feed Force in Al-50wt% Si Alloy Milling Based on Response Surface Method and Various Optimal Algorithms

  • Research Article-Mechanical Engineering
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Abstract

Al-50wt% Si alloy is considered as a difficult-to-machine material and is lack of precision machining research. In this paper, the response surface methodology (RSM), artificial neural network (ANN) and genetic algorithm (GA) are coupled to determine the optimum cutting conditions leading to the minimum surface roughness Ra and feed force Ft in Al-50wt% Si alloy precision milling. The purpose is to address the problem of machining parameters optimization in precision milling high Si-Al alloy. The Ra and Ft were considered as two process responses and cutting speed (vc), feed per tooth (fz), radial cutting depth (ae) and axial cutting depth (ap) were the process parameters. Using the rotatable orthogonal central composite design, 31 experiments were conducted. Based on RSM and analysis of variance (ANOVA), the influence of milling parameters on Ra and Ft was studied. The ANN was also employed for developing Ra and Ft predictive models, and its predictive capability was more accurate compared with RSM. Parameter optimizations were performed for minimizing Ra and Ft in single-objective and multi-objective cases using GA. In multi-objective optimization, the entropy weight method (EWM) was also implemented. Finally, the optimal parameter combination for precision milling Al-50wt% Si alloy was obtained as vc = 105 m/min, fz = 0.013 mm/z, ae = 3.909 mm and ap = 0.14 mm. The prediction errors were found as 3.27% and 4.65% for Ra and Ft, respectively. The results showed the effectiveness of the predictive model and the optimization method.

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Funding

This work was supported by the National Natural Science Foundation of China (No. 52075168, 51605161), the Project of Department of Education of Hunan Province (No. 19B190), and the Scientific Research Fund of Hunan University of Science and Technology (No. KJ-2042).

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Authors and Affiliations

  1. School of Mechanical Engineering, Hunan University of Science and Technology, Xiangtan, 411201, People’s Republic of China

    Lu Jing, Qiulin Niu, Dilei Zhan, Shujian Li & Wenhui Yue

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  2. Qiulin Niu
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Contributions

LJ was involved in conceptualization, methodology, investigation, data curation, writing—original draft. QN helped in conceptualization, methodology, supervision, funding acquisition, writing—review and editing. DZ contributed to investigation, methodology. SL was involved in validation, writing—review and editing. WY helped in supervision.

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Correspondence to Qiulin Niu.

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We would like to submit the manuscript entitled ‘Predictive modeling of surface roughness and feed force in Al-50wt% Si alloy milling based on response surface method and various optimal algorithms’ by Lu Jing, Qiulin Niu, Dilei Zhan, Shujian Li, Wenhui Yue, and we wish to be considered for publication in the ‘Arabian Journal for Science and Engineering.’

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Jing, L., Niu, Q., Zhan, D. et al. Predictive Modeling of Surface Roughness and Feed Force in Al-50wt% Si Alloy Milling Based on Response Surface Method and Various Optimal Algorithms. Arab J Sci Eng 48, 3209–3225 (2023). https://doi.org/10.1007/s13369-022-07114-8

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