Textural Analysis-based Online Closed-Loop Quality Control for Additive Manufacturing Processes

摘要

Additive manufacturing (AM) is a powerful technology for fabrication of components with complex geometries using a variety of materials. One of the major challenges in the AM industry is how to guarantee product quality and consistency by minimizing the defects. Although AM quality improvement can be achieved by optimizing machine parameter settings offline, and/or post-processing of AM components, the effects may be limited, particularly for parts with complex internal structure. Various defects severely deteriorate layer surface quality of AM components. The objective of this work is to develop an image textural analysis-based real-time diagnosis and closed-loop feedback control system for the fused deposition modeling (FDM) process. This system consists of a real-time image acquisition device (digital microscope), a high accuracy image classification algorithm to monitor the status of the printing process, and a PID controller for closed-loop control. The case study shows that this system is able to identify the types and severity of defects effectively, and the adjustment of process parameters can be implemented to mitigate the defects using a feedback control strategy.