首页|Findings from Silesian University of Technology Provides New Data about Machine Learning (Alloymanufacturingnet for Discovery and Design of Hardness-elongation Synergy In Multi-principal Element Alloys)
Findings from Silesian University of Technology Provides New Data about Machine Learning (Alloymanufacturingnet for Discovery and Design of Hardness-elongation Synergy In Multi-principal Element Alloys)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Machine Learning. According to news originating from Gliwice, Poland, by NewsRx correspondents, research stated, “Located around the center of multicomponent ph ase space, multi -principal element alloys (MPEAs) are often characterized with a unique blend of contrasting physico-chemical properties, and have a good prosp ective of presenting hardness -ductility synergy. A datasets of MPEAs fabricated by casting, wrought, sintering, annealing procedures, was collected and the mea n values for hardness and elongation was determined as 495.3 HV and 22.16 % respectively.” Financial supporters for this research include National Science Centre, Poland, University Grants Commission, India, European Research Council (ERC).
GliwicePolandEuropeAlloysCyborgsEmerging TechnologiesMachine LearningSilesian University of Technology
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