Oakyoung Han, Journal of Internet Computing and Services, Vol. 24, No. 3, pp. 99-106, Jun. 2023
10.7472/jksii.2023.24.3.99, Full Text:  HTML
Keywords: 3D Printing, Additive manufacturing, Deep Learning, voxelization, adaptive histogram equalization

Abstract

This paper presents a novel approach to improve the accuracy of additive manufacturing processes using deep learning prediction models. It provides a review of the existing literature on additive manufacturing and deep learning, highlighting the potential of these technologies to enhance the manufacturing process. The proposed method involves collecting log data from the additive manufacturing process, voxelizing the data, and using adaptive histogram equalization to enhance the quality of the data. This data is then used to train a deep learning model to predict the material properties of the printed object. The results of the study demonstrate that the proposed method improves the accuracy of the additive manufacturing process, reducing errors in material property prediction. This paper concludes by discussing the implications of this study for advancing additive manufacturing technology and contributing to the economic and social development of various industries. The results of this study suggest that deep learning prediction models have the potential to significantly improve the accuracy and efficiency of additive manufacturing processes, which could lead to cost savings, increased productivity, and improved product quality.

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