首页|New Findings from University of New South Wales Describe Advances in Machine Lea rning (Interpretable Machine Learning Approach for Exploring Process-structure-p roperty Relationships In Metal Additive Manufacturing)
New Findings from University of New South Wales Describe Advances in Machine Lea rning (Interpretable Machine Learning Approach for Exploring Process-structure-p roperty Relationships In Metal Additive Manufacturing)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ma chine Learning. According to news reporting originating in Sydney, Australia, by NewsRx journalists, research stated, “Process -structure -property (PSP) relati onships are critical to the optimization of manufacturing processes, but establi shing these relationships typically involves time- and cost- consuming experimen ts, especially for additive manufacturing (AM) due to the large number of proces s parameters involved. In this study, we develop a novel and interpretable machi ne learning approach for predicting, optimizing, and expanding the process windo w of laser powder bed fusion (LPBF) while simultaneously establishing PSP relati onships, using AlSi10Mg as an example.”
SydneyAustraliaAustralia and New Zea landCyborgsEmerging TechnologiesMachine LearningUniversity of New South Wales
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