적층제조시편 성능 평가를 위한 전산해석 모델링 도출에 관한 연구
A study on computational analysis modeling for evaluating the performance of additive manufacturing specimen
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초록
Additive manufacturing grants control over both the shape and properties of the product. Therefore, validating product operation necessitates predicting its properties. In this study, an optical fiber-based temperature sensor was inserted into an additively manufactured specimen, and the actual temperature was collected. A machine learning model was constructed using the collected temperature data for calibration, enabling accurate prediction of physical properties. These predicted properties were then integrated into structural analysis to assess the performance of the specimen.
Abstract
Additive manufacturing grants control over both the shape and properties of the product. Therefore, validating product operation necessitates predicting its properties. In this study, an optical fiber-based temperature sensor was inserted into an additively manufactured specimen, and the actual temperature was collected. A machine learning model was constructed using the collected temperature data for calibration, enabling accurate prediction of physical properties. These predicted properties were then integrated into structural analysis to assess the performance of the specimen.
참고문헌 (9)
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