摘要
由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-关于机器学习的最新研究结果已经发表。据新闻报道NewsRx编辑从Pennsylv Ania匹兹堡报道，研究称，“零件资格通常是一种在添加剂制造中，特别是在缺陷检测中的关键和劳动密集型过程例如孔隙度，这将从机器学习的进步中显著受益。我们呈现激光粉末床熔合过程中孔隙度定量定位的深度学习方法利用现场热图像监测数据制作S样品。
Abstract
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.”
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