选区激光熔化成形过程监测技术研究进展EI北大核心CSCD

选区激光熔化(SLM)作为现代工业构件制造的一种主流技术,广泛应用于汽车、航空航天及医学等领域,对SLM工艺的监测及闭环控制方式进行系统梳理变得极为重要。针对SLM技术原理及熔池变化,从SLM成形过程中的熔池温度和形貌特征综述选区激光熔化监测技术发展进程及不足,分析闭环反馈技术的研究现状。研究表明:SLM加工过程中熔池的变化状态是影响成形件质量的重要因素,通过光信号、声信号或多信号传感器可对熔池状态进行有效监测,而闭环控制需要算法分析、机器学习及传感器的协同配合才能实现实时反馈及控制。根据当前监测技术的实时性较差及系统反馈控制不够完善等问题,提出未来智能监测技术与实时闭环控制等发展方向,可为未来SLM成形高质量零件提供参考借鉴。

Progress of In-process Monitoring Techniques for Selective Laser Melting

Selective laser melting(SLM) is a prominent technology in modern industrial-component manufacturing, and it is widely used in automotive, aerospace, medical, and other fields. However, its shortcomings, such as limited stability and defects, hinder its potential industrial applications. Hence, a systematic review of in-process monitoring techniques and closed-loop control methods for SLM is crucial. The SLM system should be equipped with in-situ monitoring devices that can measure relevant quantities during the machining process. Furthermore, automated detection and localization of defects should be examined via in-process data analytics and statistical monitoring techniques. The SLM process involves rapid melting and solidification of the material, creating molten pool flows that can form defects such as porosities, incomplete fusion holes, and cracks. Monitoring techniques can effectively address these challenges by observing the melt-pool status and defects over time. Moreover, monitoring and sensing processes are widely employed in various industries for quality assurance, which can enhance machine uptime and reliability. Process monitoring is increasingly being adopted in SLM through the use of process sensors that record a broad range of optical, acoustic, and thermal signals. Consequently, the capability to acquire these signals holistically, combined with intelligence-based machine control, has the potential to enable SLM technology to replace traditional fabrication techniques. This review examines the research status of SLM technology principles and characteristics of the SLM process. Furthermore, the development process and limitations of monitoring technology for SLM are reviewed based on the melt pool temperature and morphology in the machining process, and the research status of closed-loop feedback is analyzed. The review suggests that the changing state of the molten pool is a crucial factor affecting the quality of the formed part in the SLM process. The molten pool state can be effectively monitored using optical signals, acoustic signals, or multisignal sensors. Meanwhile, closed-loop control requires algorithm analysis, machine learning, and sensor coordination to realize real-time feedback and control. To enable real-time feedback and reduce feedback transmission time, a comprehensive real-time feedback system can be established by integrating multiple sensors that can accurately monitor the interior of the molten pool. Currently, there are challenges with the poor real-time performance of existing monitoring technologies and imperfect system feedback control. The research status and future development directions of intelligent monitoring techniques and real-time closed-loop control are proposed. As monitoring technology continues to develop, machine learning can be leveraged to extract new features from input data distributions (such as images and videos), laying the foundation for future intelligent SLM process monitoring. To enhance the intelligence and automation level of the forming system, real-time monitoring and processing technology should be combined with SLM technology to minimize feedback and response times. This is vital for reducing resource waste in secondary processing and remanufacturing. Notably, ultrasonic monitoring has significant potential in monitoring systems that can dynamically analyze internal changes during the forming process and predict part quality. This study addresses a gap in the field of SLM monitoring techniques and offers a reference for producing high-quality parts using SLM technology in the future.