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新闻-关于机器学习的研究结果将在一份新报告中讨论。根据新闻报道NewsRx记者起源于加拿大滑铁卢，研究称，“传统方法，如机械测试和x射线ct(CT)在激光等离子体融合中的质量评估(LPBF)是一类添加剂制造(AM)，是资源密集型的，并进行后期生产。原位监测的最新进展，特别是利用光学层析成像(OT)探测近红外过程中的光发射为原位缺陷检测提供了机会。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews – Research findings on Machine Learning are discuss ed in a new report. According to news reportingoriginating in Waterloo, Canada, by NewsRx journalists, research stated, “Traditional methods such asmechanical testing and x-ray computed tomography (CT), for quality assessment in laser pow der-bed fusion(LPBF), a class of additive manufacturing (AM), are resource-inte nsive and conducted post-production.Recent advancements in in-situ monitoring, particularly using optical tomography (OT) to detect nearinfraredlight emissio ns during the process, offer an opportunity for in-situ defect detection.”
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