Abstract
In a quest for higher productivity, sheet metal manufacturing industry is undergoing significant development in the field of sensing and automation. One of the sheet metal operations is blanking, which is affected by an uneven crack which leads to a loss in productivity. In present work, an experiment is carried using the uni-punch tool on power press for varied punch penetration, to observe crack initiation and to find optimum clearance for IS 513 cold-rolled steel. The crack initiation is measured using shear angle, fracture angle and punch penetration. As the blanking process is complex and nonlinear, artificial neural network (ANN) is employed to predict clearance for input parameters. The predicted values are well within the experimental values.
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Patil, P.P., Patil, V.P., Ramaswamy, R. (2020). Prediction of Optimum Sheet Metal Blanking Clearance for IS513CR Steel Using Artificial Neural Network. In: Vasudevan, H., Kottur, V., Raina, A. (eds) Proceedings of International Conference on Intelligent Manufacturing and Automation. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-4485-9_23
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DOI: https://doi.org/10.1007/978-981-15-4485-9_23
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