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-一项关于机器学习的新研究现在可用。根据NewsRx记者从中国淄博发回的消息，研究表明，“高斯过程”采用机器学习方法的回归(GPR)模型识别最优过程激光粉末床熔合用高性能CoCrMo合金DOW(LPBF)，考虑环形密度（>=99）以表面粗糙度(≤7μm)为关键参数。此外，该研究还揭示了这种影响LPBF参数对缺陷形貌、分布及表面粗糙度的影响。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – A new study on Machine Learning is now available. According to news originating from Zibo, People’s Republic of China, by NewsRx correspondents, research stated, “Gaussian processregression (GPR) m odel of machine learning method was employed to identify the optimal process window for high-performance CoCrMo alloy in laser powder bed fusion (LPBF), conside ring density (>= 99%) and surface roughness (<= 7 mu m) as key parameters. Additionally, the study exa mined the impactof LPBF parameters on morphology and distribution of defect and surface roughness.”
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