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Spatial Correlation Analysis Between Punch and Scrap Web Sheared Surface Roughness for Indirect Punch Wear Assessment in Sheet-Metal Forming

Lecture notes in production engineering, pp. 581–588

Abstract

Abstract Sheet-metal forming operations would benefit from continuous punch condition monitoring to optimize maintenance scheduling and prevent unexpected failures, yet closed tool designs in high-precision processes prevent visual observation during production runs. While process signals such as force and acoustic emissions enable indirect tool monitoring, current unsupervised learning-based approaches using these process signals lack interpretability. Moreover, a recent supervised learning approach using a proxy for punch wear lacks quantitative validation of the relationship between punch wear characteristics and scrap web surface evolution, limiting reliable wear prediction model development. This study establishes a quantitative spatial correlation between punch and scrap web sheared surface roughness using high-resolution 3D profilometry. Results demonstrate that the wear of the punch surface induces surface roughness in the scrap web and, in turn, provide evidence that the scrap web shearing surface roughness can be used as an indicator for progressing punch wear in fine blanking.

Authors 4

  1. RWTH Aachen University

    Affiliation as printed

    Manufacturing Technology Institute MTI of RWTH Aachen University, Aachen, Germany

  2. RWTH Aachen University

    Affiliation as printed

    Manufacturing Technology Institute MTI of RWTH Aachen University, Aachen, Germany

  3. RWTH Aachen University

    Affiliation as printed

    Manufacturing Technology Institute MTI of RWTH Aachen University, Aachen, Germany

  4. RWTH Aachen University · Fraunhofer Institute for Production Technology IPT

    Affiliation as printed

    Fraunhofer Institute for Production Technology IPT, Aachen, Germany

    Manufacturing Technology Institute MTI of RWTH Aachen University, Aachen, Germany

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