In situ infrared temperature sensing for real-time defect detection in additive manufacturing

Abstract

Melt pool temperature is a critical parameter for the majority of additive manufacturing processes. Monitoring of the melt pool temperature can facilitate the real-time detection of various printing defects such as voids, over-extrusion, filament breakage, clogged nozzle, etc. that occur either naturally or as the result of malicious hacking activity. This study uses an in situ, multi-sensor approach for monitoring melt pool temperature in which non-contact infrared temperature sensors with customized field of view move along with the extruder of a fused deposition modeling-based printer and sense melt pool temperature from a very short working distance regardless of its X-Y translational movements. A statistical method for defect detection is developed and utilized to identify temperature deviations caused by intentionally implemented defects. Effective detection for multiple defect types and sizes is demonstrated using both a simple L-shaped test geometry and a more complex industry standard test article. Strengths and limitations of this approach are presented, and the potential for expansion via more advanced data analysis techniques such as machine learning are discussed.