Selective laser melting (SLM) is a multidisciplinary manufacturing technology in which information technology, new material technology, and manufacturing technology are integrated. It is considered one of the most promising additive manufacturing technologies due to its capability for flexible customization and its advantages in forming complex components, and it has found wide application in the fields of aerospace, rapid prototyping, and medical equipment, among others. However, there are certain challenges that need to be addressed for the further development and application of SLM technology. These challenges include insufficient manufacturing process stability, difficulty in real-time monitoring of manufacturing quality, and the immaturity of the technology regarding the real-time adjustment of process parameters. The occurrence of defects during the SLM process is a major factor contributing to these challenges. To ensure adequate quality control, it is essential that defects are continuously monitored in real time, with the provision of timely feedback to the SLM manufacturing system. Thus, a significant point of focus in this field of research is to explore the adjustment of process parameters based on defect monitoring information toward effectively controlling the occurrence of defects. In this literature review, we review the common defect types and their generation mechanisms in the SLM process, also providing a detailed description of the signals generated by the SLM process, such as acoustic, optical, and thermal signals, as well as methods for their monitoring. The signal data processing methods based on machine learning techniques have been summarized. In conclusion, this review provides a summary of the research conducted in this field and suggestions for future research directions for machine learning in the field of SLM intelligent monitoring.
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