首页|Data from University of Queensland Provide New Insights into Machine Learning (M achine Learning Driven Instance Segmentation Providing New Porosity Insights Into Wire Arc Directed Energy Deposited Ti-22v-4al)
Data from University of Queensland Provide New Insights into Machine Learning (M achine Learning Driven Instance Segmentation Providing New Porosity Insights Into Wire Arc Directed Energy Deposited Ti-22v-4al)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Researchers detail new data in Machine Learning. According to news originating fromBrisbane, Australia, by NewsRx cor respondents, research stated, “Non-destructive x-ray methods suchas micro-compu ted tomography (micro-CT) are useful for investigating porosity defects in addit ivelymanufactured products. Understanding the porosity knowledge accessible to microCT technologies relieson quantifying the spatial and morphological charact eristics of porosity instances.”
BrisbaneAustraliaAustralia and New Z ealandCyborgsEmerging TechnologiesMachine LearningUniversity of Queensla nd
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