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IJAT Vol.17 No.2 pp. 92-102
doi: 10.20965/ijat.2023.p0092
(2023)

Research Paper:

Machine Learning-Based Shape Error Estimation Using the Servomotor Current Generated During Micro-Milling of a Micro-Lens Mold

Kenta Mizuhara*,†, Daisuke Nakamichi**, Wataru Yanagihara***, and Yasuhiro Kakinuma* ORCID Icon

*Department of System Design Engineering, Keio University
3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa 223-8522, Japan

Corresponding author

**Megaro Kako Co., Ltd.
Yaizu-shi, Japan

***Industrial Research Institute of Shizuoka Prefecture
Shizuoka-shi, Japan

Received:
November 13, 2022
Accepted:
December 27, 2022
Published:
March 5, 2023
Keywords:
shape error estimation, sensorless, machine learning, servomotor current, micro-lens array
Abstract

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.

Cite this article as:
K. Mizuhara, D. Nakamichi, W. Yanagihara, and Y. Kakinuma, “Machine Learning-Based Shape Error Estimation Using the Servomotor Current Generated During Micro-Milling of a Micro-Lens Mold,” Int. J. Automation Technol., Vol.17 No.2, pp. 92-102, 2023.
Data files:
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