首页|New Machine Learning Findings Reported from University of Shanghai for Science a nd Technology (Recent Advances In Machine Learning-assisted Fatigue Life Predict ion of Additive Manufactured Metallic Materials: a Review)
New Machine Learning Findings Reported from University of Shanghai for Science a nd Technology (Recent Advances In Machine Learning-assisted Fatigue Life Predict ion of Additive Manufactured Metallic Materials: a Review)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-Investigators discuss new findings in Machine Learning. According to news reportingoriginating in Shanghai, People's Republic of China, by NewsRx journalists, research stated, "Additivemanufacturi ng features rapid production of complicated shapes and has been widely employed in biomedical,aeronautical and aerospace applications. However, additive manufa ctured parts generally exhibitdeteriorated fatigue resistance due to the presen ce of random defects and anisotropy, and the predictionof fatigue properties re mains challenging."Financial supporters for this research include National Natural Science Foundati on of China (NSFC),National Key Laboratory Foundation of Science and Technology on Materials under Shock and Impact,Natural Science Foundation of Shenyang, Op ening Project of National Key Laboratory of Shock Wave andDetonation Physics, A eronautical Science Foundation of China, Shanghai Engineering Research Center ofHigh-Performance Medical Device Materials.
ShanghaiPeople's Republic of ChinaAs iaCyborgsEmerging TechnologiesMachine LearningUniversity of Shanghai for Science and Technology
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