光学レンズの研磨条件に対する機械学習に基づく数値データ分析

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2022/07/01

タイトル(英語): Numerical Data Analysis with Machine Learning for Optical Lens Polishing Conditions

著者名: 山下　智泰（広島工業大学工学部電子情報工学科），竹本　和輝（広島工業大学工学部電子情報工学科），前田　俊二（広島工業大学工学部電子情報工学科），坪井　裕明（京セラSOC（株）），池田　竜二（京セラSOC（株））

著者名(英語): Tomoyasu Yamashita (Faculty of Engineering, Department of Electronics and Information, Hiroshima Institute of Technology), Kazuki Takemoto (Faculty of Engineering, Department of Electronics and Information, Hiroshima Institute of Technology), Shunji Maeda (Faculty of Engineering, Department of Electronics and Information, Hiroshima Institute of Technology), Hiroaki Tsuboi (KYOCERA SOC Corporation), Ryuji Ikeda (KYOCERA SOC Corporation)

キーワード: 光学レンズ，研磨，因果分析，予測　　optical lens，polishing，causal analysis，prediction

要約(英語): In the manufacturing industry, operations such as polishing and welding, wherein sensation and experience account for much of the work, rely on the skills of skilled technicians. However, automation has been sought in such processes. We proposed a method for the evaluation of the polishing conditions of an Oscar-type polishing system, which is used for high-mix low-volume production of lenses with various curvatures, by determining the surface pressure distribution of the lens through the preliminary examination of the lens polishing conditions. In particular, (1) a judgment index focusing on the number of Newtonian rings, which decreases as polishing progresses, is adopted. (2) Extraction of important types of polishing conditions is conducted through standard analysis. (3) By using the numerical values of the polishing conditions, the number of Newton rings is predicted with high accuracy. Through the evaluation based on the above-mentioned factors, the prediction based on the newly introduced surface-pressure-distribution-based method contributed to the improvement of accuracy, and the predicted values were sufficient to cover the ability of the operator’s evaluation.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.142 No.7 （2022） 特集：2021年電子・情報・システム部門大会

本誌掲載ページ: 737-745 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/142/7/142_737/_article/-char/ja/