Opportunities and Challenges for Predicting the Service Status of SLM Metal Parts Under Big Data and Artificial Intelligence.

Abstract

Selective laser melting (SLM) technology is a high-end dual-use technology that is implemented in aerospace and medical equipment, as well as the automotive industry and other military and civilian industries, and is urgently needed for major equipment manufacturing and national defense industries. This paper examines the challenges of uncontrollable service states and the inability to ensure service safety of SLM metal parts under nonlinear and complex operating conditions. An overview of the prediction of the service status of SLM metal parts was introduced, and an effective approach solving the problem was provided in this paper. In this approach, the cross-scale coupling mechanism between mesoscopic damage evolution and macroscopic service state evolution is clarified by tracking the mesoscopic damage evolution process of SLM metal parts based on ultrasonic nonlinear responses. The failure mechanism is organically integrated with hidden information from monitoring big data, and a "chimeric" model to accurately evaluate the service status of SLM metal parts is constructed. Combining nonlinear ultrasound technology with big data and artificial intelligence to construct a "chimeric" model and consummate the corresponding methods and theories for evaluating the service status of SLM metal parts is an effective way to reveal the mesoscopic damage evolution and service status evolution mechanisms of SLM metal parts under complex factor coupling, and to accurately describe and characterize the service status of parts under complex operating conditions. The proposed approach will provide a theoretical basis and technical guarantee for the precise management of SLM parts' service safety in key equipment fields such as aerospace, medical equipment, and the automotive industry.