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Prediction of Deformation of Small Plastic Lens using Machine Learning Algorithm

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한국기계기술학회지 (Journal of the Korean Society of Mechanical Technology)
한국기계기술학회 (Korean Society of Mechanical Technology)
초록

For a plastic diffusion lens to uniformly diffuse light, it is important to minimize deformation that may occur during injection molding and to minimize deformation. It is essential to control the injection molding condition precisely. In addition, as the number of meshes increases, there is a limitation in that the time required for analysis increases. Therefore, We applied machine learning algorithms for faster and more precise control of molding conditions. This study attempts to predict the deformation of a plastic diffusion lens using the Decision Tree regression algorithm. As the variables of injection molding, melt temperature, packing pressure, packing time, and ram speed were set as variables, and the dependent variable was set as the deformation value. A total of 256 injection molding analyses were conducted. We evaluated the prediction model's performance after learning the Decision Tree regression model based on the result data of 256 injection molding analyses. In addition, We confirmed the prediction model's reliability by comparing the injection molding analysis results.

저자
  • 유민지(공주대학교) | Min-ji Yoo
  • 김범수(공주대학교) | Bum-soo Kim
  • 김승수(공주대학교 광공학 금형공학과 대학원) | Seung-soo Kim
  • 한석기(공주대학교) | HAN SEOKGI
  • 한성렬(공주대학교) | Han Seong Ryeol Corresponding author