Recent Advances and Applications of Machine Learning in Metal Forming Processes

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

Recent advances and applications of machine learning in metal forming processes

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Properties and bending behavior of Nickel coated Mild steel sheet during air bending

Springback refers to the elastic recovery which subject to a geometrical change when the metal undergoes deformation during the forming process. The experimental investigation of this paper is focused to analyze the behavior of spring back of nickel coated mild steel (NCMS) sheets during the air bending process. The hardeness and surface roughness was measured after coating. Experimental investigation have been conducted to resolve the influence of control parameters such as Orientations (θ), Width of the sheet (Ws), Punch travel (Tp), Holding time (Ht) and Punch Velocity (v) on spring back behavior. As a results, the incraese in Orientations, Width of the sheet, Punch travel andd Punch Velocity incraese the springback angle.&nbsp

Integration of feedforward neural network and finite element in the draw-bend springback prediction

To achieve accurate results, current nonlinear elastic recovery applications of finite element (FE) analysis have become more complicated for sheet metal springback prediction. In this paper, an alternative modelling method able to facilitate nonlinear recovery was developed for springback prediction. The nonlinear elastic recovery was processed using back-propagation networks in an artificial neural network (ANN). This approach is able to perform pattern recognition and create direct mapping of the elasticallydriven change after plastic deformation. The FE program for the sheet metal springback experiment was carried out with the integration of ANN. The results obtained at the end of the FE analyses were found to have improved in comparison to the measured data

Advances in Plastic Forming of Metals

The forming of metals through plastic deformation comprises a family of methods that produce components through the re-shaping of input stock, oftentimes with little waste. Therefore, forming is one of the most efficient and economical manufacturing process families available. A myriad of forming processes exist in this family. In conjunction with their countless existing successful applications and their relatively low energy requirements, these processes are an indispensable part of our future. However, despite the vast accumulated know-how, research challenges remain, be they related to the forming of new materials (e.g., for light-weight transportation applications), pushing the boundaries of what is doable, reducing the intermediate steps and/or scrap, or further enhancing the environmental friendliness. The purpose of this book is to collect expert views and contributions on the current state-of-the-art of plastic forming, thus highlighting contemporary challenges and offering ideas and solutions

Closed-loop control of product properties in metal forming

Metal forming processes operate in conditions of uncertainty due to parameter variation and imperfect understanding. This uncertainty leads to a degradation of product properties from customer specifications, which can be reduced by the use of closed-loop control. A framework of analysis is presented for understanding closed-loop control in metal forming, allowing an assessment of current and future developments in actuators, sensors and models. This leads to a survey of current and emerging applications across a broad spectrum of metal forming processes, and a discussion of likely developments.Engineering and Physical Sciences Research Council (Grant ID: EP/K018108/1)This is the final version of the article. It first appeared from Elsevier via https://doi.org/10.1016/j.cirp.2016.06.00

The Sustainability Of Neural Network Applications Within Finite Element Analysis In Sheet Metal Forming: A Review

The prediction of springback in sheet metal is vital to ensure economical metal forming. The latest nonlinear recovery in finite element analysis is used to achieve accurate results, but this method has become more complicated and requires complex computational programming to develop a constitutive model. Having the potential to assist the complexity, computational intelligence approach is often regarded as a statistical method that does not contribute to the development of a constitutive model. To provide a reference for researchers who are studying the potential application of computational intelligence in springback research, a review of studies into the development of sheet metal forming and the application of neural network to predict springback is presented in this research paper. It can be summarized as: (1) Springback is influenced by various factors that are involved in the sheet metal forming process. (2) The main complexity in FE analysis is the development of a constitutive model of a material that has the potential to be solved by using the computational intelligence approach. (3) The existing neural network approach for solving springback predictions is unable to represent all the factors that affect the results ofthe analysi

Neural Network Model and Finite Element Simulation of Spring back in Plane-Strain Metallic Beam Bending

Bending has significant importance in the sheet metal product industry. Moreover, the spring back of sheet metal should be taken into consideration in order to produce bent sheet metal parts within acceptable tolerance limits and to solve geometrical variation for the control of manufacturing process. Nowadays, the importance of this problem increases because of the use of sheet-metal parts with high mechanical characteristics. This research proposes a novel approach to predict springback in the air bending process. In this approach the finite element method is combined with metamodeling techniques to accurately predict the springback. Two metamodeling techniques namely the neural network and the response surface methodology are used and compared to approximate two multidimensional functions. The first function predicts the springback amount for a given material, geometrical parameters, and the bend angle before springback. The second function predicts the punch displacement for a given material, geometrical parameters, and the bend angle after springback. The training data required to train the two-metamodeling techniques were generated using a verified nonlinear finite element algorithm developed in the current research. The algorithm is based on the updated Lagrangian formulation, which takes into consideration geometrical, material nonlinearity, and contact. To validate the finite element model physical experiments were conducted. A neural network algorithm based on the backpropagation algorithm has been developed. This research utilizes computer generated D-optimal designs to select training examples for both metamodeling techniques so that a comparison between the two techniques can be considered as fair. Results from this research showed that finite element prediction of springback is in good agreement with the experimental results. The standard deviation is 1.213 degree. It has been found that the neural network metamodels give more accurate results than the response surface metamodels. The standard deviation between the finite element method and the neural network metamodels for the two functions are 0.635 degree and 0.985 mm respectively. The standard deviation between the finite element method and the response surface methodology are 1.758 degree and 1.878 mm for both functions, respectively

A numerical investigation on the springback in air v-bending of aluminum 1050 A

Bending is a sheet forming operation in excess of the elasticity limit of the material. Currently, in the industry, bending operation is carried out by a successive test method in order to have the geometry of the part, which generates the operation quite long and too expensive. In fact, springback brings about geometric changes in the folded parts. This phenomenon affects the angle and radius of curvature and can be primarily influenced by multiple factors. In this work, we predict the springback during the air v-bending procedure with the finite element calculation software ABAQUS to pass the test on the first try. The simulation parameters followed the real setting taking into account the characteristics of the punch and the die of the hydraulic press. The simulation was then checked using experimental tests and analytical models, we study this particular springback in 1050A Aluminum specimens through the analytical models of Gardiner and Queener. As a final result, the springback comparison effect between simulation and experiment is presented, and the evaluation of the experimental results with those of the simulation and theoretical models is conclusive. The simulated data show good agreement with the experimental and the analytical models the Finite Element Method (FEM) is a reliable tool for the analysis and simulation of the air v-bending process of Aluminum 1050 A sheet