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<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&#xa0;what-if analysis&#xa0;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>

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Sheet metal forming processes: Development of an innovative methodology for the integration of the metal forming and structural analysis

  • 期刊论文

  • 发表日期：

    2025年1月16日

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  • Maurizio Calabrese,
  • Antonio Del Prete,
  • Teresa Primo

摘要

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.