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[null,"1.","Record Nr.",null,"UNINA9910637782503321"]
[null,null,"Author",null,"Prates Pedro"]
[null,null,"Title",null,"Recent Advances and Applications of Machine Learning in Metal Forming Processes"]
[null,null,"Publication",null,"Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022"]
[null,null,"ISBN",null,"3-0365-5772-5"]
[null,null,"Physical description",null,"1 electronic resource (210 p.)"]
[null,null,"Subjects",null,"Technology: general issues"]
[{"$nulls":4},"History of engineering & technology"]
[{"$nulls":4},"Mining technology & engineering"]
[null,null,"Language",null,"English"]
[null,null,"Format",null,"Language material"]
[null,null,null,"Bibliographic level","Monograph"]
[null,null,null,"Summary, etc","Machine learning (ML) technologies are emerging in Mechanical Engineering, driven by the increasing availability of datasets, coupled with the exponential growth in computer performance. In fact, there has been a growing interest in evaluating the capabilities of ML algorithms to approach topics related to metal forming processes, such as: Classification, detection and prediction of forming defects; Material parameters identification; Material modelling; Process classification and selection; Process design and optimization. The purpose of this Special Issue is to disseminate state-of-the-art ML applications in metal forming processes, covering 10 papers about the abovementioned and related topics."]