The increasing availability of data, which becomes a continually increasing trend in multiple fields of application, has given machine learning approaches a renewed interest in recent years. Accordingly, manufacturing processes and sheet metal forming follow such directions, having in mind the efficiency and control of the many parameters involved, in processing and material characterization. In this article, two applications are considered to explore the capability of machine learning modeling through shallow artificial neural networks (ANN). One consists of developing an ANN to identify the constitutive model parameters of a material using the force–displacement curves obtained with a standard bending test. The second one concentrates on the springback problem in sheet metal press-brake air bending, with the objective of predicting the punch displacement required to attain a desired bending angle, including additional information of the springback angle. The required data for designing the ANN solutions are collected from numerical simulation using finite element methodology (FEM), which in turn was validated by experiments.

Press brake air bending, a process of obtaining products by sheet metal forming, can be considered at first sight a simple geometric problem. However the accuracy of the obtained geometries involves the combination of multiple parameters directly associated with the tools and the processing parameters, as well as with the sheet metal materials and dimensions. The main topic herein presented deals with the capability of predicting the punch displacement process parameter that enables the product to be accurately shaped to a desired bending angle, in press brake air bending. In our approach, it is considered separately the forming process and the elastic recovery (i.e. the springback effect). Current solutions in press brake numerical control (computer numerical control) are normally configured by analytical models developed from geometrical analysis and including correcting factors. In our approach, it is proposed to combine the use of a learning tool, artificial neural networks, with a simulation and data generation tool (finite element analysis). This combination enables modeling the complex nonlinear behavior of the forming process and springback effect, including the validation of results obtained. A developed model taking into account different process parameters and tool geometries allow extending the range of applications with practical interest in industry. The final solution is compatible with its incorporation in a computer numerical control press brake controller. It was concluded that, using this methodology, it is possible to predict efficient and accurate final geometries after bending, being also a step forward to a “first time right” solution. In addition, the developed models, methodologies and obtained results were validated by comparison with experimental tests.

In this study, direct laser deposition (DLD) of nickel-based superalloy powders (Inconel 625) on structural steel (42CrMo4) was analysed. Cladding layers were produced by varying the main processing conditions: laser power, scanning speed, feed rate, and preheating. The processing window was established based on conditions that assured deposited layers without significant structural defects and a dilution between 15 and 30%. Scanning electron microscopy, energy dispersive spectroscopy, and electron backscatter diffraction were performed for microstructural characterisation. The Vickers hardness test was used to analyse the mechanical response of the optimised cladding layers. The results highlight the influence of preheating on the microstructure and mechanical responses, particularly in the heat-affected zone. Substrate preheating to 300 °C has a strong effect on the cladding/substrate interface region, affecting the microstructure and the hardness distribution. Preheating also reduced the formation of the deleterious Laves phase in the cladding and altered the martensite microstructure in the heat-affected zone, with a substantial decrease in hardness.

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