Abstract
This study achieved the goal of guiding bed design and optimization by conducting multi-objective optimization research on the performance of CNC lathe beds. In this study, Morris analysis was first performed on the sensitivity of the parameters, and then out to optimize the parameters using a combination of neural network and genetic algorithm. The loss function value, RMSE error accumulation, recall, sensitivity and specificity of the ASSGA-BP optimization model were better. The maximum error between the predicted and true values of the ASSGA-BP model was 0.28 mm. In the performance study of the multi-objective optimization method based on the Morris sensitivity analysis and the improved GA algorithm, the average MAE value is 0.91 %. The average RMSE value is 0.59 %. Also, the new model is significantly better than the NSGA-II, EGA, and FGA algorithms in terms of both the number of final non-dominated solutions and the speed of reaching convergence. The above results demonstrate that the model proposed in this study has high performance, can achieve faster convergence and has the best stability of the convergence state. The innovation of this article lies in the use of the Morris method to screen and evaluate numerous parameters in order to improve the accuracy of the calculation results and ensure the effectiveness of the optimization results. The improved algorithm overcomes the problems of BP neural network and can effectively improve the generalization performance of the neural network, thereby improving the prediction accuracy of the model.
Subject
Mechanical Engineering,General Materials Science
Reference26 articles.
1. D. Gu, Y. Zhong, Z. Xu, B. Chen, and Z. Wang, “An importance measure of a CNC lathe considering failure correlations,” Quality and Reliability Engineering International, Vol. 38, No. 3, pp. 1367–1379, Apr. 2022, https://doi.org/10.1002/qre.2963
2. P. Gupta, B. Singh, and Y. Shrivastava, “Grey relational analysis for optimal process variables during turning on CNC lathe,” Materials Today: Proceedings, Vol. 51, No. 1, pp. 228–233, 2022, https://doi.org/10.1016/j.matpr.2021.05.259
3. M. S. Ismail, B. H. T. Baharudin, and K. K. B. Hon, “Analytical model prediction: micro-cutting tool forces with the effect of friction on machining titanium alloy (Ti-6Al-4V),” Zenodo, Vol. 6, No. 8, pp. 177–192, Aug. 2012, https://doi.org/10.5281/zenodo.1058429
4. Z. Y. Nan, J. Li, and M. J. Tan, “Influential factors of inclined fracture induction logging based on numerical simulation and physical experiment,” Exploration Geophysics, Vol. 53, No. 1, pp. 26–37, Jan. 2022, https://doi.org/10.1080/08123985.2021.1885293
5. S. Jia et al., “Multi-objective optimization of CNC turning process parameters considering transient-steady state energy consumption,” Sustainability, Vol. 13, No. 24, p. 13803, Dec. 2021, https://doi.org/10.3390/su132413803