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<p>During the metal additive manufacturing (AM) process, each deposited layer undergoes rapid solidification driven by extremely high cooling rates. Directed Energy Deposition (DED) is a method within metal AM that allows the high deposition rates necessary for large-scale metallic manufacturing. During and after deposition, the resulting cooling behavior governs thermal and structural transitions that influence material evolution and defect formation. Acoustic emission (AE) provides a passive, high-frequency sensing modality for monitoring these evolving dynamics through stress-wave activity generated during and after deposition. Delineating the corresponding cooling transition zones is important for separating active process behavior from post-deposition cooling and for enabling physically interpretable analysis of AE signal evolution. In this study, we present a data-driven framework for delineating transient zones between the operational and cooling phases of powder-based metal DED using only AE data. An adaptive unsupervised time-domain detection method was developed to automatically identify the onset and termination of the transitional zone (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\varvec{T}_{\varvec{s}}\)</EquationSource> </InlineEquation>) using only AE data, leveraging a combination of root mean square (RMS) peak tracking, Hilbert envelope smoothing, and curvature-based stabilization via unsupervised clustering. This approach enabled the segmentation of the AE signal into three physically interpretable periods: the operational zone (<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\varvec{T}_{\varvec{o}}\)</EquationSource> </InlineEquation>), the transition zone (<InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\varvec{T}_{\varvec{s}}\)</EquationSource> </InlineEquation>), and the cooling zone (<InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(\varvec{T}_{\varvec{c}}\)</EquationSource> </InlineEquation>), without manual intervention. Quantitative statistical analysis revealed distinct behaviors across these zones. In particular, <InlineEquation ID="IEq5"> <EquationSource Format="TEX">\(\varvec{T}_{\varvec{o}}\)</EquationSource> </InlineEquation> maintained compact, stable features across all metrics, consistent with steady-state deposition. Meanwhile, <InlineEquation ID="IEq6"> <EquationSource Format="TEX">\(\varvec{T}_{\varvec{s}}\)</EquationSource> </InlineEquation> exhibited elevated standard deviation and entropy, reflecting dynamic thermal and structural changes. Finally, <InlineEquation ID="IEq7"> <EquationSource Format="TEX">\(\varvec{T}_{\varvec{c}}\)</EquationSource> </InlineEquation> demonstrated high kurtosis values (up to 20.31), indicating heavy-tailed behavior and isolated bursts of energy near the end of the cooling process. These results show that AE-based adaptive segmentation can support automated detection of cooling transitions in DED and provide a foundation for process-aware monitoring of post-deposition dynamics.</p>

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Adaptive time-domain acoustic emission framework for post-deposition cooling transition detection in directed energy deposition

  • 期刊论文

  • 发表日期：

    2026年6月27日

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  • Steven C. Hespeler,
  • Anusuya Vellingiri,
  • Ehsan Dehghan-Niri

摘要

During the metal additive manufacturing (AM) process, each deposited layer undergoes rapid solidification driven by extremely high cooling rates. Directed Energy Deposition (DED) is a method within metal AM that allows the high deposition rates necessary for large-scale metallic manufacturing. During and after deposition, the resulting cooling behavior governs thermal and structural transitions that influence material evolution and defect formation. Acoustic emission (AE) provides a passive, high-frequency sensing modality for monitoring these evolving dynamics through stress-wave activity generated during and after deposition. Delineating the corresponding cooling transition zones is important for separating active process behavior from post-deposition cooling and for enabling physically interpretable analysis of AE signal evolution. In this study, we present a data-driven framework for delineating transient zones between the operational and cooling phases of powder-based metal DED using only AE data. An adaptive unsupervised time-domain detection method was developed to automatically identify the onset and termination of the transitional zone ( \(\varvec{T}_{\varvec{s}}\) ) using only AE data, leveraging a combination of root mean square (RMS) peak tracking, Hilbert envelope smoothing, and curvature-based stabilization via unsupervised clustering. This approach enabled the segmentation of the AE signal into three physically interpretable periods: the operational zone ( \(\varvec{T}_{\varvec{o}}\) ), the transition zone ( \(\varvec{T}_{\varvec{s}}\) ), and the cooling zone ( \(\varvec{T}_{\varvec{c}}\) ), without manual intervention. Quantitative statistical analysis revealed distinct behaviors across these zones. In particular, \(\varvec{T}_{\varvec{o}}\) maintained compact, stable features across all metrics, consistent with steady-state deposition. Meanwhile, \(\varvec{T}_{\varvec{s}}\) exhibited elevated standard deviation and entropy, reflecting dynamic thermal and structural changes. Finally, \(\varvec{T}_{\varvec{c}}\) demonstrated high kurtosis values (up to 20.31), indicating heavy-tailed behavior and isolated bursts of energy near the end of the cooling process. These results show that AE-based adaptive segmentation can support automated detection of cooling transitions in DED and provide a foundation for process-aware monitoring of post-deposition dynamics.