Maurizio Calabrese, Antonio Del Prete, Teresa Primo
{"title":"Sheet metal forming processes: Development of an innovative methodology for the integration of the metal forming and structural analysis","authors":"Maurizio Calabrese, Antonio Del Prete, Teresa Primo","doi":"10.1007/s12289-024-01868-2","DOIUrl":null,"url":null,"abstract":"<div><p>Sheet metal forming is essential in automotive and aerospace industries, where accurate simulations are crucial for optimizing material deformation and tool design. Finite Element Analysis (FEA) is a key tool for predicting stresses, strains, and material flow in these processes. Recent advancements in artificial intelligence (AI) and machine learning have further enhanced these simulations, improving toolpath planning and overall process efficiency Appl Mech 1:97-110, 2020, ASME J Manuf Sci Eng 144(2):021012, 2021. A critical aspect of sheet metal forming is the development of forming tools, which must withstand high forces and ensure precision. Traditionally, tool design has relied on a trial-and-error approach, heavily dependent on manufacturer expertise. This paper introduces an innovative methodology that integrates sheet metal forming simulations with the structural analysis of forming tools, facilitated by a specialized connector. The connector enables integrated analysis of the forming process and tool structural behaviour, providing feedback on tool performance under operational loads. The output of the forming simulation (contact pressures between workpiece and tools) feeds the structural model. Additionally, the methodology incorporates AI-driven what-if analysis to streamline decision-making in the early design stages. This modular solution is designed to integrate with a Digital Twin framework, offering continuous optimization. The proposed methodology enhances manufacturing efficiency by reducing simulation time and improving tool structural behaviour predictions, enabling faster, more accurate tool development and ultimately minimizing trial-and-error in tool design.</p></div>","PeriodicalId":591,"journal":{"name":"International Journal of Material Forming","volume":"18 1","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Material Forming","FirstCategoryId":"88","ListUrlMain":"https://link.springer.com/article/10.1007/s12289-024-01868-2","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
引用次数: 0
Abstract
Sheet metal forming is essential in automotive and aerospace industries, where accurate simulations are crucial for optimizing material deformation and tool design. Finite Element Analysis (FEA) is a key tool for predicting stresses, strains, and material flow in these processes. Recent advancements in artificial intelligence (AI) and machine learning have further enhanced these simulations, improving toolpath planning and overall process efficiency Appl Mech 1:97-110, 2020, ASME J Manuf Sci Eng 144(2):021012, 2021. A critical aspect of sheet metal forming is the development of forming tools, which must withstand high forces and ensure precision. Traditionally, tool design has relied on a trial-and-error approach, heavily dependent on manufacturer expertise. This paper introduces an innovative methodology that integrates sheet metal forming simulations with the structural analysis of forming tools, facilitated by a specialized connector. The connector enables integrated analysis of the forming process and tool structural behaviour, providing feedback on tool performance under operational loads. The output of the forming simulation (contact pressures between workpiece and tools) feeds the structural model. Additionally, the methodology incorporates AI-driven what-if analysis to streamline decision-making in the early design stages. This modular solution is designed to integrate with a Digital Twin framework, offering continuous optimization. The proposed methodology enhances manufacturing efficiency by reducing simulation time and improving tool structural behaviour predictions, enabling faster, more accurate tool development and ultimately minimizing trial-and-error in tool design.
期刊介绍:
The Journal publishes and disseminates original research in the field of material forming. The research should constitute major achievements in the understanding, modeling or simulation of material forming processes. In this respect ‘forming’ implies a deliberate deformation of material.
The journal establishes a platform of communication between engineers and scientists, covering all forming processes, including sheet forming, bulk forming, powder forming, forming in near-melt conditions (injection moulding, thixoforming, film blowing etc.), micro-forming, hydro-forming, thermo-forming, incremental forming etc. Other manufacturing technologies like machining and cutting can be included if the focus of the work is on plastic deformations.
All materials (metals, ceramics, polymers, composites, glass, wood, fibre reinforced materials, materials in food processing, biomaterials, nano-materials, shape memory alloys etc.) and approaches (micro-macro modelling, thermo-mechanical modelling, numerical simulation including new and advanced numerical strategies, experimental analysis, inverse analysis, model identification, optimization, design and control of forming tools and machines, wear and friction, mechanical behavior and formability of materials etc.) are concerned.