Defect Detection and Monitoring in Metal Additive Manufactured Parts through Deep Learning of Spatially Resolved Acoustic Spectroscopy Signals

Jacob M. Williams, Paul R. Dryburgh, Adam Thomas Clare, Prahalada Rao, Ashok K. Samal

Smart and Sustainable Manufacturing Systems 2018 article cited by 34

importance 6/10 ai assisted Additive Manufacturing Materials and Processes additive manufacturing stage: research

Problem

Laser powder bed fusion 3D-printed metal parts contain defects unacceptable for safety-critical applications, requiring in-process monitoring.

Why it matters

Defective additively manufactured parts cannot be used in safety-critical components without reliable integrity assessment.

What the AI did

Deep learning is applied to Spatially Resolved Acoustic Spectroscopy signals to detect and monitor defects in metal additive manufactured parts during fabrication.

Results

not stated in source

Impact

Abstract (truncated) positions the method for simultaneous part-integrity assessment during fabrication.

Record

Venue	Smart and Sustainable Manufacturing Systems
Published	2018
Work type	article
Authors	Jacob M. Williams, Paul R. Dryburgh, Adam Thomas Clare, Prahalada Rao, Ashok K. Samal
Citations	34 (OpenAlex, at import time)
References	48
Model or method	not stated in source
Organization	not stated in source
Primary source	https://doi.org/10.1520/ssms20180035
Open access	closed
Keywords	additive manufacturing, defect detection, acoustic spectroscopy, deep learning, LPBF

Importance rating

6 / 10

significant — thousands to millions affected

Abstract states LPBF defects block use in safety-critical applications and positions in-process monitoring as enabling a 'new age in manufacturing'; broad industry relevance but no quantified scale.

Basis: stated. The source itself states the scale. Stated scale: not stated in source. Method and limits.

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