Process monitoring and machine learning for defect detection in laser-based metal additive manufacturing
importance 5/10
ai assisted
Additive Manufacturing Materials and Processes additive manufacturing stage: research
Problem
Laser-based metal additive manufacturing (PBF-LB/M, DED-LB/M) frequently forms defects and existing machines lack closed-loop control, making processes hard to control.
Why it matters
Defects threaten part quality for aerospace, automotive, biomedical, and energy applications.
What the AI did
The paper reviews the application of various machine learning methods for process monitoring and defect detection in laser-based metal AM.
Results
not stated in source
Impact
The abstract is truncated and states no measured outcomes.
Record
| Venue | Journal of Intelligent Manufacturing |
|---|---|
| Published | 2023 |
| Work type | article |
| Authors | Timothy Herzog, Milan Brandt, Adrian Trinchi, Antonella Sola, Andrey Molotnikov |
| Citations | 239 (OpenAlex, at import time) |
| References | 164 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1007/s10845-023-02119-y |
| Open access | hybrid |
| Keywords | additive manufacturing, defect detection, machine learning, process monitoring, laser, review |
Importance rating
5
real but narrower advance
AM defects threaten part quality for aerospace, automotive, biomedical, energy; review-level, no measured outcomes, narrower process-control advance.
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