In situ monitoring in laser melt injection based on fusion of infrared thermal and high-speed camera images

作者：Xu, Hongmeng; Huang, Haihong^*

来源：Journal of Manufacturing Processes, 2023, 92: 466-478.

DOI：10.1016/j.jmapro.2023.02.059

• Online monitoring
• Features fusion
• Quality classification
• Laser melt injection
• Metal matrix composite

Unstable reliability and poor repeatability in the production of metal matrix composite (MMC) parts are primary challenges currently faced by laser melt injection (LMI). In situ monitoring of LMI during the part fabrication process provides a sound solution. This research presents promising results that aim to bridge the knowledge gap by determining an innovative technique to correlate the combined features of infrared thermal and high-speed cameras to state monitoring of LMI and the forming quality identification of MMC. The combined features of the molten pool and spatter were extracted based on the understanding of LMI physical mechanisms and then were selected by the fisher score algorithm with the introduction of weighting factor (WF-FS). The support vector machine (SVM) prediction models based on single-sensor features, multi-sensor features, and fusion features decomposed with different methods were developed to identify the quality of MMC. The results indicated that the multi-sensor features exhibit better classification performance. The empirical mode decomposition (EMD) method can improve classification performance by mining the depth and subtle feature information of multi -source features. The optimal feature subset with 9 EMD features was selected by principal component analysis to balance computational period and classification accuracy. This work provides an effective and novel approach to monitor the LMI process and evaluate the MMC part fabrication quality.