Machine learning applied to property prediction of metal additive manufacturing products with textural features extraction

Lien Kai Chang, Ri Sheng Chen, Mi Ching Tsai, Rong Mao Lee, Ching Chih Lin, Jhih Cheng Huang, Tsung Wei Chang, Ming Huwi Horng

研究成果: Article同行評審


Laser powder bed fusion (LPBF) is one of the common metal additive manufacturing technologies, which has been increasingly applied across various industries, including healthcare, manufacturing, and aerospace, owing to its advantages in customization and faster prototyping. However, acquiring accurate product properties necessitates repetitive and time-consuming measurements, which risk damaging the product. Thus, there is a pressing need to develop an automated method for predicting product properties. In this study, to forecast these properties, we documented details related to metal additive manufacturing products, encompassing both the process parameters and textural features. These features were extracted from layer-by-layer images using the gray-level co-occurrence matrix (GLCM). Subsequently, we employed machine learning (ML) models, such as support vector regression (SVR), XGBoost, and LightGBM, to predict product properties and compare their performance. The experimental results reveal stronger correlations between process parameters and texture features of three-dimensional co-occurrence matrices of the product images, compared to two-dimensional ones. Additionally, the models exhibit high predictive accuracy, especially XGBoost, and LightGBM, with R2 scores approaching 0.9 for all properties. These findings highlight the superiority and feasibility of the proposed approach. Moreover, this proposed approach holds promise in accurately predicting diverse product properties, meeting the demands of multiple application contexts.

頁(從 - 到)83-98
期刊International Journal of Advanced Manufacturing Technology
出版狀態Published - 2024 5月

All Science Journal Classification (ASJC) codes

  • 控制與系統工程
  • 軟體
  • 機械工業
  • 電腦科學應用
  • 工業與製造工程


深入研究「Machine learning applied to property prediction of metal additive manufacturing products with textural features extraction」主題。共同形成了獨特的指紋。