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<p>This study investigates whether contact acoustic emission (AE) sensing can provide complementary or surrogate information to conventional electrical signals for monitoring process stability in Wire Arc Additive Manufacturing (WAAM). With this aim, established welding stability indices obtained from welding electrical signals, namely the Dip Consistency Index (DCI), Transfer Index (TI), and Transfer Stability Index (TSI), were compared with a novel AE-based descriptor, the Root Mean Square Deviation (RMSD) of the frequency spectrum. Correlation analysis revealed a strong association between RMSD and DCI, suggesting its suitability as a surrogate indicator, whereas its lack of correlation with other indices highlights its complementary contribution in a multi-sensor framework. Based on these indices, a clustering-based method was employed as a potential solution for process monitoring, showing that when only welding electrical signal indices are used, the method achieves an F1-score of 59.2%, whereas inclusion of RMSD increases performance to 87.5%, eliminating false alarms and improving reliability in assessing process stability at the end of each deposited layer. These results demonstrate that multi-sensor fusion can significantly enhance layer-wise monitoring performance in WAAM.</p>

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Process stability monitoring in wire arc additive manufacturing using welding stability indices and a novel contact acoustic emission damage index

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月20日

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  • Giulio Mattera,
  • Thiago Glissoi Lopes,
  • Alessandra Caggiano,
  • Pedro Oliveira Conceição,
  • Paulo Roberto de Aguiar,
  • Luigi Nele

摘要

This study investigates whether contact acoustic emission (AE) sensing can provide complementary or surrogate information to conventional electrical signals for monitoring process stability in Wire Arc Additive Manufacturing (WAAM). With this aim, established welding stability indices obtained from welding electrical signals, namely the Dip Consistency Index (DCI), Transfer Index (TI), and Transfer Stability Index (TSI), were compared with a novel AE-based descriptor, the Root Mean Square Deviation (RMSD) of the frequency spectrum. Correlation analysis revealed a strong association between RMSD and DCI, suggesting its suitability as a surrogate indicator, whereas its lack of correlation with other indices highlights its complementary contribution in a multi-sensor framework. Based on these indices, a clustering-based method was employed as a potential solution for process monitoring, showing that when only welding electrical signal indices are used, the method achieves an F1-score of 59.2%, whereas inclusion of RMSD increases performance to 87.5%, eliminating false alarms and improving reliability in assessing process stability at the end of each deposited layer. These results demonstrate that multi-sensor fusion can significantly enhance layer-wise monitoring performance in WAAM.