Abstract
This work aims to improve the surface quality of commercially pure titanium (CP-Ti) with free alumina lapping fluid and establish the relationship between the main process parameters of lapping and roughness. On this basis, the optimal process parameters were searched by performing particle swarm optimization with mutation. First, free alumina lapping fluid was used to perform an L9(33) orthogonal experiment on CP-Ti to acquire data samples to train the neural network. At the same time, a BP neural network was created to fit the nonlinear functional relation among the lapping pressure P, spindle speed n, slurry flow Q and roughness Ra. Then, the range of the neuron numbers in the hidden layer of the neural network was determined by empirical formulas and the Kolmogorov theorem. On this basis, particle swarm optimization with mutation was used to search for the optimal process parameter configurations for lapping CP-Ti. The optimal process parameter configurations were used in the neural network to calculate the prediction value. Finally, the accuracy of the prediction was verified experimentally. The optimum process parameter configurations found by particle swarm optimization were as follows: the lapping pressure was 5 kPa, spindle speed was 60 r·min−1 and slurry flow was 50 ml·min−1. Then, the configurations were applied to a neural network to simulate prediction: the roughness was 0.1127 μm. The roughness obtained by experiments was 0.1134 μm. The error was 0.62%, which indicates that the well-trained neural network can achieve a good prediction when experimental data are missing. Applying the particle swarm optimization (PSO) algorithm with mutation to a neural network will obtain the optimal process parameter configurations, which can effectively improve the surface quality of CP-Ti lapped with free abrasive.
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The datasets used or analysed during the current study are available from the corresponding author on reasonable request.
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Funding
The author(s) received the financial support of the Key Program in the Excellent Young Talents Support Plan in Universities of Anhui Province (grant no. gxyqZD2019051), the Young and Middle-aged Talent Training Program of 2018 of Anhui Polytechnic University, the Collaborative Innovation Project of Anhui Provincial University (grant no. GXXT-2019-021), the Science and Technology Planning Project of Wuhu City (2020yf20) and Open Research Project of Anhui Simulation Design and Modern Manufacture Engineering Technology Research Center (HuangShan University) (grant no. SGCZXYB1804) for the research, authorship and/or publication of this article.
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Guarantor of integrity of entire study: Jianbin Wang; Hong Gao
Study concepts: Jianbin Wang; Kaiqiang Ye
Study design: Kaiqiang Ye; Liu Yang
Literature research: Kaiqiang Ye
Experimental studies: Liu Yang; Kaiqiang Ye
Data acquisition: Liu Yang; Jianbin Wang
Data analysis/interpretation: Kaiqiang Ye
Statistical analysis: Kaiqiang Ye
Manuscript preparation: Kaiqiang Ye
Manuscript definition of intellectual content: Kaiqiang Ye; Ping Xiao
Manuscript editing: Hong Gao; Kaiqiang Ye
Manuscript revision/review: Jianbin Wang; Hong Gao; Kaiqiang Ye
Manuscript final version approval: Jianbin Wang
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Ye, K., Wang, J., Gao, H. et al. Optimization of lapping process parameters of CP-Ti based on PSO with mutation and BPNN. Int J Adv Manuf Technol 117, 2859–2866 (2021). https://doi.org/10.1007/s00170-021-07862-1
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DOI: https://doi.org/10.1007/s00170-021-07862-1