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<p>Hot forming of friction-stir-welded tailored blanks (FSW-TWBs) in industrial ramp-up poses unique challenges: a high number of coupled joining and forming parameters, stochastic trial-and-error data, and strict requirements on crack prevention and dimensional tolerances. Unlike conventional studies that rely on designed experiments or focus on weld behavior in isolation, this work introduces a coupled process analysis (CPA)–based workflow that directly mines routinely collected ramp-up data to derive multivariate, predictive process models. We demonstrate its application to an automotive B-pillar TWB, building separate models for weld-seam crack probability and part geometry accuracy. Without a formal design of experiment, CPA automatically filters out irrelevant inputs, resolves multicollinearities, and provides robust cause–effect relationships. The resulting surrogate models support in-silico optimization, delivering crack-free components within tight forming tolerances and dramatically reducing additional experimental effort. The findings establish a practical route toward data-driven process control in early production phases and lay the groundwork for future integration of CPA models into closed-loop manufacturing systems.</p>

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Modeling and optimization of a hot forming process for friction stir-welded tailored blanks with an industrially suitable number of test specimens

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月26日

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  • Patrick Ackert,
  • Mohamad Idriss,
  • Christian Schwarz

摘要

Hot forming of friction-stir-welded tailored blanks (FSW-TWBs) in industrial ramp-up poses unique challenges: a high number of coupled joining and forming parameters, stochastic trial-and-error data, and strict requirements on crack prevention and dimensional tolerances. Unlike conventional studies that rely on designed experiments or focus on weld behavior in isolation, this work introduces a coupled process analysis (CPA)–based workflow that directly mines routinely collected ramp-up data to derive multivariate, predictive process models. We demonstrate its application to an automotive B-pillar TWB, building separate models for weld-seam crack probability and part geometry accuracy. Without a formal design of experiment, CPA automatically filters out irrelevant inputs, resolves multicollinearities, and provides robust cause–effect relationships. The resulting surrogate models support in-silico optimization, delivering crack-free components within tight forming tolerances and dramatically reducing additional experimental effort. The findings establish a practical route toward data-driven process control in early production phases and lay the groundwork for future integration of CPA models into closed-loop manufacturing systems.