首页|New Findings from Carnegie Mellon University in the Area of Machine Learning Rep orted (Thermopore: Predicting Part Porosity Based On Thermal Images Using Deep L earning)
New Findings from Carnegie Mellon University in the Area of Machine Learning Rep orted (Thermopore: Predicting Part Porosity Based On Thermal Images Using Deep L earning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Current study results on Machine Learn ing have been published. According to newsreporting out of Pittsburgh, Pennsylv ania, by NewsRx editors, research stated, “Part qualification is oftena critica l and labor-intensive process in additive manufacturing, particularly in the det ection of defectssuch as porosity, which stands to benefit significantly from a dvancements in machine learning. We presenta deep learning approach for quantif ying and localizing ex-situ porosity within Laser Powder Bed Fusionfabricated s amples utilizing in-situ thermal image monitoring data.”
PittsburghPennsylvaniaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningCa rnegie Mellon University
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