Preform optimization for hot forging processes using genetic algorithms

• Published in: International journal of advanced manufacturing technology Vol. 89; no. 5-8; pp. 1623 - 1634
• Main Authors: Knust, Johannes, Podszus, Florian, Stonis, Malte, Behrens, Bernd-Arno, Overmeyer, Ludger, Ullmann, Georg
• Format: Journal Article
• Language: English
• Published: London Springer London 01.03.2017; Springer Nature B.V
• Subjects: CAE) and Design; Computer-Aided Engineering (CAD; Connecting rods; Engineering; Evolutionary algorithms; Finite element method; Fitness; Genetic algorithms; Hot forging; Industrial and Production Engineering; Mass distribution; Mechanical Engineering; Media Management; Multiple objective analysis; Optimization; Original Article; Preforms; Cross-wedge rolling; Evolutionary algorithms; Preforming optimization; Hot forging
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-016-9209-9

In multi-stage hot forging processes, the preform shape is the parameter mainly influencing the final forging result. Nevertheless, the design of multi-stage hot forging processes is still a trial and error process and therefore time-consuming. The quality of developed forging sequences strongly depends on the engineer’s experience. To overcome these obstacles, this paper presents an algorithm for solving the multi-objective optimization problem when designing preforms. Cross-wedge-rolled (CWR) preforms were chosen as subject of investigation. An evolutionary algorithm is introduced to optimize the preform shape taking into account the mass distribution of the final part, the preform volume, and the shape complexity. The developed algorithm is tested using a connecting rod as a demonstration part. Based on finite element analysis, the implemented fitness function is evaluated, and thus the progressive optimization can be traced.