The demand for the mass production of micro-lens arrays (MLAs) is increasing. An MLA is fabricated through an injection molding process, and its mold is manufactured by a five-axis high-precision machine tool using a small diameter endmill. A visual examination is not available to judge the quality of the mold while machining. Therefore, an effective process monitoring technology must be developed. A promising approach is to apply a servomotor current to in-process monitoring because as long as the servomotor works well, no external sensors, capital investment, or maintenance processes are required. From this perspective, a machine learning-based shape error estimation method using only the servomotor current is proposed. To explore the relationship between the motor current generated during micro-milling and the shape error of the mold, the servomotor current in X-, Y-, and Z-axes was recorded, and the corresponding shape error of the MLA mold was measured after machining. Input data were prepared by converting time-domain servomotor current data to frequency-domain data using short-time Fourier transform and reducing the dimensions of the data via principal component analysis. In terms of a meaningful label for the output data, the average shape error in the machined area corresponding to each window was provided. The input/output relationships were used to train five different machine learning models, and the accuracy of shape error estimation using each model was evaluated. In addition, the estimation accuracies using the X-, Y-, and Z-axes were compared to find the axis that senses the shape error with the highest accuracy. The results show that the non-linear method using the X-axis servomotor current information closest to the machining point achieved the highest shape error estimation accuracy.

1. [1] W. Yuan, L. H. Li, W. B. Lee, and C. Y. Chan, “Fabrication of Micro-lens Array and its Application: A Review,” Chin. J. Mech. Eng., Vol.31, No.1, pp. 1-9, 2018.
2. [2] ams OSRAM, “Projection Lighting.” https://ams.com/projection-lighting [Accessed July 14, 2022]
3. [3] T. T. Nguyen, K. Holländer, M. Hoggenmueller, C. Parker, and M. Tomitsch, “Designing for Projection-based Communication between Autonomous Vehicles and Pedestrians,” Proc. of the 11th Int. Conf. on Automotive User Interfaces and Interactive Vehicular Applications, pp. 284-294, 2019.
4. [4] J. Yan, Z. Zhang, T. Kuriyagawa, and H. Gonda, “Fabricating micro-structured surface by using single-crystalline diamond endmill,” Int. J. Adv. Manuf. Technol., Vol.51, No.9, pp. 957-964, 2010.
5. [5] S. Ibaraki and I. Yoshida, “A Five-Axis Machining Error Simulator for Rotary-Axis Geometric Errors Using Commercial Machining Simulation Software,” Int. J. Automation Technol., Vol.11, No.2, pp. 179-187, 2017.
6. [6] L. F. Robles, L. S. González, J. D. González, M. C. Limas, and H. Pérez, “Use of image processing to monitor tool wear in micro-milling,” Neurocomputing, Vol.452, pp. 333-340, 2020.
7. [7] A. Caggiano and L. Nele, “Artificial Neural Networks for Tool Wear Prediction Based on Sensor Fusion Monitoring of CFRP/CFRP Stack Drilling,” Int. J. Automation Technol., Vol.12, No.3, pp. 275-281, 2018.
8. [8] Q. An, Z. Tao, X. Xu, M. E. Mansori, and M. Chen, “A data-driven model for milling tool remaining useful life prediction with convolutional and stacked LSTM network,” Measurement, Vol.154, 107461, 2020.
9. [9] F. Aghazadeh, A. Tahan, and M. Thomas, “Tool condition monitoring using spectral subtraction and convolutional neural networks in milling process,” Int. J. Adv. Manuf. Technol., Vol.98, No.9, pp. 3217-3227, 2018.
10. [10] M. Malekian, S. S. Park, and M. B. G. Jun, “Tool wear monitoring of micro-milling operations,” J. of Materials Processing Technology, Vol.209, No.10, pp. 4903-4914, 2009.
11. [11] W. Arai, F. Tanaka, and M. Onosato, “Error Estimation of Machined Surfaces in Multi-Axis Machining with Machine Tool Errors Including Tool Self-Intersecting Motion Based on High-Accuracy Tool Swept Volumes,” Int. J. Automation Technol., Vol.12, No.5, pp. 680-687, 2018.
12. [12] Y. Zhou and W. Xue, “Review of tool condition monitoring methods in milling processes,” Int. J. Adv. Manuf. Technol., Vol.96, No.5, pp. 2509-2523, 2018.
13. [13] E. H. E. Bouchikhi, V. Choqueuse, and M. E. H. Benbouzid, “Current Frequency Spectral Subtraction and Its Contribution to Induction Machines’ Bearings Condition Monitoring,” IEEE Trans. on Energy Conversion, Vol.28, No.1, pp. 135-144, 2013.
14. [14] V. N. Vapnik and A. Y. Lerner, “Pattern recognition using generalized portrait method,” Automation and Remote Control, Vol.24, pp. 774-780, 1963.
15. [15] F. Rosenblatt, “The perceptron: A probabilistic model for information storage and organization in the brain,” Psychological Review, Vol.65, No.6, pp. 386-408, 1958.
16. [16] X. Glorot, A. Bordes, and Y. Bengio, “Deep Sparse Rectifier Neural Networks,” Proc. of the 14th Int. Conf. on Artificial Intelligence and Statistics, Vol.15, pp. 315-323, 2011.
17. [17] D. Rummelhart, G. Hinton, and R. Williams, “Learning representations by back-propagating errors,” Nature, Vol.323, No.6088, pp. 533-536, 1986.
18. [18] D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” Int. Conf. on Learning Representations, 2015.
19. [19] T. Akiba, S. Sano, T. Yanase, T. Ohta, and M. Koyama, “Optuna: A Next-generation Hyperparameter Optimization Framework,” Proc. of the 25th ACM SIGKDD Int. Conf. on Knowledge Discovery & Data Mining, pp. 2623-2631, 2019.