首页|Study Findings from University of Leuven (KU Leuven) Broaden Understanding of Ma chine Learning (Combined Effect of Random Porosity and Surface Defect On Fatigue Lifetime of Additively Manufactured Micro-sized Ti6al4v Components: an ...)
Study Findings from University of Leuven (KU Leuven) Broaden Understanding of Ma chine Learning (Combined Effect of Random Porosity and Surface Defect On Fatigue Lifetime of Additively Manufactured Micro-sized Ti6al4v Components: an ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Machine Learning is the subject o f a report. According to news originating from Leuven, Belgium, by NewsRx corres pondents, research stated, “Surface defects and internal porosities resulting fr om the additive manufacturing process contribute to a scatter in fatigue lifetim e, increasing uncertainty in applications such as aerospace engineering. This st udy proposes a combined approach of machine learning and finite element modellin g to explore the interaction between random porosity distribution and high surfa ce roughness on the fatigue lifetime of micro -sized additively manufactured par ts.” Financial support for this research came from China Scholarship Council.
LeuvenBelgiumEuropeAerospace Resea rchAlgorithmsCyborgsEmerging TechnologiesEngineeringMachine LearningMathematicsNumerical AnalysisUniversity of Leuven (KU Leuven)
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