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J. H. Kim(김정환),Y. J. Yoon(윤용진) Korean Society for Precision Engineering 2021 한국정밀공학회 학술발표대회 논문집 Vol.2021 No.11월
Previous research using a tBA-co-DEGDA photo-resin has shown promising results of more than 20 shape memory cycles. However, due to the cost-extensive material characterization, their work does not consider all possible material ratios and their effects on desired properties such as glass transition temperature (Tg) which is activation temperature for 4D printed object. Therefore, this study identifies the relationship between Tg and 4D printing process parameters to propose a machine learning model for predicting Tg. The effect of individual process parameters on Tg has been analyzed following the OFAT design of experiments methodology, and a model for predicting Tg was created following Graeco-Latin Square design of experiments methodology and machine learning. The amount of photoinitiator, monomers ratio, UV post curing time and post curing temperature affect the Tg of the polymer. Furthermore, various algorithms such as SVM, elastic net, artificial neural network, gradient boosting and random forest were evaluated based on several preprocessing methods such as MinMaxScaler and StandardScaler. Amongst the various algorithms, SVM results in the highest accuracy of Tg prediction with a mean absolute error of 1.04 and a mean squared deviation of 2.36.