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In situ infrared temperature sensing for real-time defect detection in additive manufacturing
Additive Manufacturing ( IF 11.0 ) Pub Date : 2021-09-20 , DOI: 10.1016/j.addma.2021.102328
Rifat-E-Nur Hossain 1 , Jerald Lewis 1, 2 , Arden L. Moore 1, 2
Affiliation  

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.



中文翻译:

用于增材制造中实时缺陷检测的原位红外温度传感

熔池温度是大多数增材制造工艺的关键参数。熔池温度监测有助于实时检测各种印刷缺陷,例如自然发生或恶意黑客活动导致的空洞、过度挤压、断丝、喷嘴堵塞等。本研究使用原位、多传感器方法来监测熔池温度,其中具有定制视场的非接触式红外温度传感器与基于熔融沉积建模的打印机的挤出机一起移动,并从非常接近的位置感测熔池温度。无论其 XY 平移运动如何,工作距离都很短。缺陷检测的统计方法被开发并用于识别由有意实施的缺陷引起的温度偏差。L形测试几何形状和更复杂的行业标准测试物品。介绍了这种方法的优点和局限性,并讨论了通过更先进的数据分析技术(如机器学习)进行扩展的潜力。

更新日期:2021-09-22
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