首页|Findings in the Area of Machine Learning Reported from University of Waterloo (Optical Tomography and Machine Learning for In-situ Defects Detection In Laser Powder Bed Fusion: a Self-organizing Map and U-net Based Approach)
Findings in the Area of Machine Learning Reported from University of Waterloo (Optical Tomography and Machine Learning for In-situ Defects Detection In Laser Powder Bed Fusion: a Self-organizing Map and U-net Based Approach)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators discuss new findings in Machine Learning. According to news reportingoriginating from Waterloo, Canada, by NewsRx correspondents, research stated, “The inherently stochasticnature of the laser powder bed fusion (LPBF) process presents a significant challenge in developingdependable and efficient defect detection algorithms that can flexibly adjust to a variety of machine configurationsand process parameters. The present study proposes a machine learning-based approach thatemploys the optical tomography (OT) data acquired during LPBF for identifying defects, specifically lackof fusion and keyhole porosity.”
WaterlooCanadaNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningUniversity of Waterloo
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