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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.

Technology: general issues --- History of engineering & technology --- Mining technology & engineering --- sheet metal forming --- uncertainty analysis --- metamodeling --- machine learning --- hot rolling strip --- edge defects --- intelligent recognition --- convolutional neural networks --- deep-drawing --- kriging metamodeling --- multi-objective optimization --- FE (Finite Element) AutoForm robust analysis --- defect prediction --- mechanical properties prediction --- high-dimensional data --- feature selection --- maximum information coefficient --- complex network clustering --- ring rolling --- process energy estimation --- metal forming --- thermo-mechanical FEM analysis --- artificial neural network --- aluminum alloy --- mechanical property --- UTS --- topological optimization --- artificial neural networks (ANN) --- machine learning (ML) --- press-brake bending --- air-bending --- three-point bending test --- sheet metal --- buckling instability --- oil canning --- artificial intelligence --- convolution neural network --- hot rolled strip steel --- defect classification --- generative adversarial network --- attention mechanism --- deep learning --- mechanical constitutive model --- finite element analysis --- plasticity --- parameter identification --- full-field measurements --- n/a