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<p>The Sheet Molding Compound (SMC) process is essential in high-volume manufacturing of composite structures due to its scalability and efficiency. A primary challenge, however, lies in determining the initial charge shape that ensures complete mold filling without excessive overflow, typically resolved through labor-intensive trial and error. While simulations can anticipate the mold filling outcome, they often lack the capability to fine-tune the initial preform configuration, leading to inefficiencies in both time and material. This study presents an innovative, simulation-driven approach for accurately predicting initial charge shapes for two-dimensional (2D) mold designs. By employing Darcy’s Law and a fixed mesh grid framework, the methodology simulates a reverse material flow to trace the optimal preform shape. A complementary machine learning (ML) model was then developed to predict the preform shapes based on mold geometry, final thickness, and initial charge thickness. Serving as a digital twin of the SMC process, this ML model delivers results with comparable accuracy to simulations, significantly enhancing computational efficiency and avoiding common convergence issues in traditional simulations. This ML-driven digital twin approach also provides a robust proof of concept for addressing initial charge shapes in complex three-dimensional (3D) molds, where the computational demands of reverse flow simulations may present challenges. This combined simulation and ML framework equips manufacturers with a more precise and efficient tool for optimizing SMC processes, minimizing material waste, and reducing production time.</p>

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Integrating simulation and machine learning for accurate preform charge prediction in Sheet Molding Compound manufacturing

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月3日

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  • Mikhael Tannous,
  • Sebastian Rodriguez,
  • Chady Ghnatios,
  • Francisco Chinesta

摘要

The Sheet Molding Compound (SMC) process is essential in high-volume manufacturing of composite structures due to its scalability and efficiency. A primary challenge, however, lies in determining the initial charge shape that ensures complete mold filling without excessive overflow, typically resolved through labor-intensive trial and error. While simulations can anticipate the mold filling outcome, they often lack the capability to fine-tune the initial preform configuration, leading to inefficiencies in both time and material. This study presents an innovative, simulation-driven approach for accurately predicting initial charge shapes for two-dimensional (2D) mold designs. By employing Darcy’s Law and a fixed mesh grid framework, the methodology simulates a reverse material flow to trace the optimal preform shape. A complementary machine learning (ML) model was then developed to predict the preform shapes based on mold geometry, final thickness, and initial charge thickness. Serving as a digital twin of the SMC process, this ML model delivers results with comparable accuracy to simulations, significantly enhancing computational efficiency and avoiding common convergence issues in traditional simulations. This ML-driven digital twin approach also provides a robust proof of concept for addressing initial charge shapes in complex three-dimensional (3D) molds, where the computational demands of reverse flow simulations may present challenges. This combined simulation and ML framework equips manufacturers with a more precise and efficient tool for optimizing SMC processes, minimizing material waste, and reducing production time.