Applications of Evolutionary Algorithms to Sheet Metal Forming Processes: A Review

Abstract

Metal forming processes are compression-tension processes involving wide spectrum of operations and flow conditions. The result of the process depends on the large number of parameters and their interdependence. The selection and optimization of various parameters is still based on trial and error methods. In this paper the authors presents a compressive study of application of evolutionary strategies to optimize the geometry parameters such as die design and punch design, process parameters such as forming load, blank holder pressure and coefficient of friction, the spring back, hammering sequence etc. Evolutionary algorithms offer many advantages over traditional methods. These are widely used now days for sheet metal industry.

Application of machine learning algorithm in the sheet metal industry: an exploratory case study

“…For instance, linear programming can be used to minimize the cost of sheet metal punching when batching orders (Herrmann and Delalio 2001). Kakandikar, Darade, and Nandedkar (2009) deployed genetic algorithm to optimize the geometry parameters (e.g. die design, hammering sequence, blank holder pressure, etc.)…”