Determination of Optimal Adhesion Conditions for FDM Type 3D Printer Using Machine Learning
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In this study, optimal adhesion conditions to alleviate defects caused by heat shrinkage with FDM type 3D printers with machinelearning are researched. Machine learning is one of the “statistical methods of extracting the law from data” and can be classified assupervised learning, unsupervised learning and reinforcement learning. Among them, a function model for adhesion between the bedand the output is presented using supervised learning specialized for optimization, which can be expected to reduce output defectswith FDM type 3D printers by deriving conditions for optimum adhesion between the bed and the output. Machine learning codesprepared using Python generate a function model that predicts the effect of operating variables on adhesion using data obtainedthrough adhesion testing. The adhesion prediction data and verification data have been shown to be very consistent, and the potentialof this method is explained by conclusions.
Abstract
In this study, optimal adhesion conditions to alleviate defects caused by heat shrinkage with FDM type 3D printers with machinelearning are researched. Machine learning is one of the “statistical methods of extracting the law from data” and can be classified assupervised learning, unsupervised learning and reinforcement learning. Among them, a function model for adhesion between the bedand the output is presented using supervised learning specialized for optimization, which can be expected to reduce output defectswith FDM type 3D printers by deriving conditions for optimum adhesion between the bed and the output. Machine learning codesprepared using Python generate a function model that predicts the effect of operating variables on adhesion using data obtainedthrough adhesion testing. The adhesion prediction data and verification data have been shown to be very consistent, and the potentialof this method is explained by conclusions.
참고문헌 (14)
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