# In-situ crack and keyhole pore detection in laser directed energy deposition through acoustic signal and deep learning

arXiv: https://arxiv.org/abs/2304.04598 · HTML: https://mlsanity.com/paper/2304.04598/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2023.

## Built on by (most cited first, 6 of 6)

- [Deep Learning for Melt Pool Depth Contour Prediction From Surface Thermal Images via Vision Transformers](https://mlsanity.com/paper/2404.17699/) (2024)
- [Unsupervised Welding Defect Detection Using Audio And Video](https://mlsanity.com/paper/2409.02290/) (2024)
- [Physics-Informed Machine Learning of Argon Gas-Driven Melt Pool Dynamics](https://mlsanity.com/paper/2307.12304/) (2023)
- [Advancing Additive Manufacturing through Deep Learning: A Comprehensive Review of Current Progress and Future Challenges](https://mlsanity.com/paper/2403.00669/) (2024)
- [Audio-visual cross-modality knowledge transfer for machine learning-based in-situ monitoring in laser additive manufacturing](https://mlsanity.com/paper/2408.05307/) (2024)
- [In-situ surface porosity prediction in DED (directed energy deposition) printed SS316L parts using multimodal sensor fusion](https://mlsanity.com/paper/2304.08658/) (2023)

## Abstract (arXiv)

Cracks and keyhole pores are detrimental defects in alloys produced by laser directed energy deposition (LDED). Laser-material interaction sound may hold information about underlying complex physical events such as crack propagation and pores formation. However, due to the noisy environment and intricate signal content, acoustic-based monitoring in LDED has received little attention. This paper proposes a novel acoustic-based in-situ defect detection strategy in LDED. The key contribution of this study is to develop an in-situ acoustic signal denoising, feature extraction, and sound classification pipeline that incorporates convolutional neural networks (CNN) for online defect prediction. Microscope images are used to identify locations of the cracks and keyhole pores within a part. The defect locations are spatiotemporally registered with acoustic signal. Various acoustic features corresponding to defect-free regions, cracks, and keyhole pores are extracted and analysed in time-domain, frequency-domain, and time-frequency representations. The CNN model is trained to predict defect occurrences using the Mel-Frequency Cepstral Coefficients (MFCCs) of the lasermaterial interaction sound. The CNN model is compared to various classic machine learning models trained on the denoised acoustic dataset and raw acoustic dataset. The validation results shows that the CNN model trained on the denoised dataset outperforms others with the highest overall accuracy (89%), keyhole pore prediction accuracy (93%), and AUC-ROC score (98%). Furthermore, the trained CNN model can be deployed into an in-house developed software platform for online quality monitoring. The proposed strategy is the first study to use acoustic signals with deep learning for insitu defect detection in LDED process.