ǂa ǂgeneralised approach for advanced high-strength steels
Luka Sevšek (Author), Tomaž Pepelnjak (Author)

Abstract

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.

Keywords

enotočkovno inkrementalno preoblikovanje;izbočevanje pločevine;hibridno dvostopenjsko preoblikovanje;metoda končnih elementov;večplastna perceptronska umetna nevronska mreža;genetski algoritem;single point incremental sheet metal forming;sheet metal bulging;hybrid two-step forming;finite element method;multilayer perceptron artificial neural network;genetic algorithm;

Data

Language: English
Year of publishing:
Typology: 1.01 - Original Scientific Article
Organization: UL FS - Faculty of Mechanical Engineering
UDC: 004.032.26:621.9
COBISS: 214479619 Link will open in a new window
ISSN: 1996-1944
Views: 221
Downloads: 35
Average score: 0 (0 votes)
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Other data

Secondary language: Slovenian
Secondary keywords: enotočkovno inkrementalno preoblikovanje;izbočevanje pločevine;hibridno dvostopenjsko preoblikovanje;metoda končnih elementov;večplastna perceptronska umetna nevronska mreža;genetski algoritem;
Type (COBISS): Article
Pages: str. 1-36
Volume: ǂVol. ǂ17
Issue: ǂiss. ǂ22, [art. no.] 5459
Chronology: 2024
DOI: 10.3390/ma17225459
ID: 25391005