Optimisation of flexible forming processes using multilayer perceptron artificial neural networks and genetic algorithms : a generalised approach for advanced high-strength steels

Sevšek, Luka (author), Pepelnjak, Tomaž (author)

Flexibility is crucial in forming processes as it allows the production of different product shapes without changing equipment or tooling. Single-point incremental forming (SPIF) provides this flexibility, but often results in excessive sheet metal thinning. To solve this problem, a pre-forming phase can be introduced to ensure a more uniform thickness distribution. This study represents advances in this field by developing a generalised approach that uses a multilayer perceptron artificial neural network (MLP ANN) to predict thinning results from the input parameters and employs a genetic algorithm (GA) to optimise these parameters. This study specifically addresses advanced high-strength steels (AHSSs) and provides insights into their formability and the optimisation of the forming process. The results demonstrate the effectiveness of the proposed method in minimising sheet metal thinning and represent a significant advance in flexible forming technologies applicable to a wide range of materials and industrial applications.

• Language: English
• Keywords: single point incremental sheet metal forming, sheet metal bulging, hybrid two-step forming, finite element method, multilayer perceptron artificial neural network, genetic algorithm
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FS - Faculty of Mechanical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2024
• Number of pages: Str. 1-36
• Numbering: Vol. 17, iss. 22, [art. no.] 5459
• PID: 20.500.12556/RUL-164767
• UDC: 004.032.26:621.9
• ISSN on article: 1996-1944
• DOI: 10.3390/ma17225459
• COBISS.SI-ID: 214479619
• Publication date in RUL: 11.11.2024

Record is a part of a journal

• Title: Materials
• Shortened title: Materials
• Publisher: Molecular Diversity Preservation International
• ISSN: 1996-1944
• COBISS.SI-ID: 33588485

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: enotočkovno inkrementalno preoblikovanje, izbočevanje pločevine, hibridno dvostopenjsko preoblikovanje, metoda končnih elementov, večplastna perceptronska umetna nevronska mreža, genetski algoritem

Projects

• Funder: ARIS - Slovenian Research and Innovation Agency
• Project number: P2-0248