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<p>In the Industry 4.0 era, digitalization and automation of large-scale data analysis create new opportunities to enhance productivity, quality, and process efficiency. This paper proposes an integrated framework combining Design of Experiments (DoE), Machine Learning (ML), and Explainable AI (XAI) in a single tool for structured and interpretable data planning, processing, and optimization. A key innovation is a biplot representation that visualizes interactions among process parameters, enabling the identification of optimal variable ranges for ideal process configurations. The framework’s predictions were experimentally validated in laser ablation of glass and polymethylmethacrylate, achieving errors between 0% and 5%. Its modular architecture ensures adaptability across diverse manufacturing processes, offering a generalizable and interpretable approach for intelligent manufacturing. This work demonstrates how combining DoE, ML, and XAI can support data-driven decision-making and advance process understanding within the Industry 4.0 paradigm.</p>

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An explainable data-driven framework for material processing: integrating design of experiment and machine learning in laser ablation

  • 期刊论文

  • 发表日期：

    2026年1月24日

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  • Mariachiara Grande,
  • Tommaso Gallingani,
  • Romolo Laurita,
  • Matteo Gherardi

摘要

In the Industry 4.0 era, digitalization and automation of large-scale data analysis create new opportunities to enhance productivity, quality, and process efficiency. This paper proposes an integrated framework combining Design of Experiments (DoE), Machine Learning (ML), and Explainable AI (XAI) in a single tool for structured and interpretable data planning, processing, and optimization. A key innovation is a biplot representation that visualizes interactions among process parameters, enabling the identification of optimal variable ranges for ideal process configurations. The framework’s predictions were experimentally validated in laser ablation of glass and polymethylmethacrylate, achieving errors between 0% and 5%. Its modular architecture ensures adaptability across diverse manufacturing processes, offering a generalizable and interpretable approach for intelligent manufacturing. This work demonstrates how combining DoE, ML, and XAI can support data-driven decision-making and advance process understanding within the Industry 4.0 paradigm.