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

Sevšek, Luka (avtor), Pepelnjak, Tomaž (avtor)

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.

• Jezik: Angleški jezik
• Ključne besede: single point incremental sheet metal forming, sheet metal bulging, hybrid two-step forming, finite element method, multilayer perceptron artificial neural network, genetic algorithm
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FS - Fakulteta za strojništvo
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2024
• Št. strani: Str. 1-36
• Številčenje: Vol. 17, iss. 22, [art. no.] 5459
• PID: 20.500.12556/RUL-164767
• UDK: 004.032.26:621.9
• ISSN pri članku: 1996-1944
• DOI: 10.3390/ma17225459
• COBISS.SI-ID: 214479619
• Datum objave v RUL: 11.11.2024

Gradivo je del revije

• Naslov: Materials
• Skrajšan naslov: Materials
• Založnik: Molecular Diversity Preservation International
• ISSN: 1996-1944
• COBISS.SI-ID: 33588485

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: enotočkovno inkrementalno preoblikovanje, izbočevanje pločevine, hibridno dvostopenjsko preoblikovanje, metoda končnih elementov, večplastna perceptronska umetna nevronska mreža, genetski algoritem

Projekti

• Financer: ARIS - Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije
• Številka projekta: P2-0248