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Modeling for a small-hole drilling process of engineering plastic PEEK by Taguchi-based neural network method

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Abstract

Engineering plastics have specific properties in strength, hardness, impact resistance, and aging persistence, often used for structural plates and electronic components. However, the holes made by the drilling process always shrink after the cutting heat dispersion due to their high thermal expansion coefficient. Drilling parameters must be discussed thoughtfully especially in the small-hole fabrication to acquire a stable hole quality. This study developed parameter models by the Taguchi-based neural network method to save the experimental resources on the drilling of engineering plastic, polyetheretherketone (PEEK). A three-level full-factorial orthogonal array experiment, L27, was first conducted for minimizing the thrust force, hole shrinkage in diameter, and roundness error. In terms of the network modeling, four variables were designated to the input layer neurons included the three drilling parameters (spindle speed, depth of peck-drilling, feed rate) and the thrust force detected, and that of the output layer neurons were two hole characteristics of diameter shrinkage and roundness. The models were trained by a stepped-learning procedure to expand the network’s field information stage by stage. After three stages of training, the models developed can provide precise simulations for the network’s training sets. For the non-trained cases, the prediction accuracy of the hole’s characteristics discussed was below 1 μm in the drilling of a 1-mm-diameter hole.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Chinese Culture University, Taipei, Taiwan (Republic of China)

    Dar-Yuan Chang, Chien-Hung Lin & Xing-Yao Wu

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  1. Dar-Yuan Chang
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  2. Chien-Hung Lin
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  3. Xing-Yao Wu
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All authors participated in the writing, read and approved the final manuscript.

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Correspondence to Chien-Hung Lin.

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Appendices

Appendix 1

Fig. 12
figure 12

Models’ performance presented by correlation coefficient in the first-stage training: (a) 4 × 5 × 5 × 2, (b) 4 × 10 × 10 × 2. (c) 4 × 15 × 15 × 2, and (d) 4 × 20 × 20 × 2

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Appendix 2

Fig. 13
figure 13

Models’ performance presented by correlation coefficient in the second-stage training: (a) 4 × 10 × 10 × 2, (b) 4 × 15 × 15 × 2, and (c) 4 × 20 × 20 × 2

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Chang, DY., Lin, CH. & Wu, XY. Modeling for a small-hole drilling process of engineering plastic PEEK by Taguchi-based neural network method. Int J Adv Manuf Technol 119, 5777–5795 (2022). https://doi.org/10.1007/s00170-021-08431-2

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