Optimal Design of Pre-Forging for Gear Blank Using BP Neural Network and Genetic Algorithm

Abstract:

The design of pre-forging is very important during multistage forging of producing gear blank. It directly affects the behavior of metal flowing, filled situation of die cavity of finish forging, quality of products and die life. Most designs of pre-forging for gear blank are based on trial and error method. This paper presents a suitable method for practical designation of pre-forging for gear blank by proposing an improved algorithm, which combines Back Propagation Neural Network and Genetic Algorithm. Firstly, the mathematical model between the size parameters of pre-forging and forming force and maximum die stress of finish forging was established by using Back Propagation Neural Network which has the feature of processing highly nonlinear problems. Secondly, the established model was set as the fitness function of Genetic Algorithm. At last, the most superior pre-forging shape and the size parameters were solved by using the Genetic Algorithm with the function of overall situation optimization. These can lead to lower cost and time in the stages of designing pre-forging for gear blank.

• Periodical: Advanced Materials Research (Volumes 179-180)
• Pages: 801-806
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.179-180.801
• Online since: January 2011
• Authors: Ming Yue Zhang, Xin Yun Wang, Ju Chen Xia
• Keywords: BP Neural Network (BPNN), Gear Blank, Genetic Algorithm (GA), Optimization Design

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