Research Progress on Offline Defect-Detection Technologies for Selective Laser-Melting Parts

Abstract

Selective laser melting (SLM) is recognized as one of the most promising metal additive manufacturing technologies for industrial deployment and has been applied to fabricate precision components for high-end equipment in sectors including aerospace and biomedicine. Nevertheless, ensuring reliable component quality and consistent performance remain a core challenge for SLM, which is widely acknowledged as a major barrier restricting the large-scale industrial application of the technology. Multi-scale and multi-type defects, which are difficult to fully suppress during the forming process, are the primary contributing factors to this issue. Accordingly, research on offline defect-detection technologies for SLM components is a critical pathway to break through the quality bottleneck and has emerged as a prominent research focus in both academia and industry. Based on a systematic review of relevant literature published in the past five years, this paper summarizes the morphological characteristics and formation mechanisms of typical defects in SLM-fabricated parts, including porosity, cracks, inclusions, and surface-forming defects. It elaborates on mainstream offline detection methods such as ultrasonic testing, X-ray computed tomography (XCT), and deep learning-enabled intelligent detection, and clarifies the respective performance advantages and inherent limitations of each technology. A quantitative comparison of these technologies is conducted from three core dimensions, detection accuracy, detection efficiency, and deployment cost, and differentiated application scenarios are defined corresponding to defect characteristics. Finally, the paper concludes with a full summary and prospects future in-depth research directions and industrial development trends in the field of SLM offline defect detection.