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Machine learning augmented X-ray computed tomography features for volumetric defect classification in laser beam powder bed fusion

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Abstract 

This study proposes a data-driven framework to augment low-resolution X-ray computed tomography (LR-XCT) scanning with machine learning (ML) for efficient defect inspection and classification for laser beam powder bed fusion (L-PBF) process. The framework leverages the efficiency of LR-XCT scanning and improves defect classification accuracy with data-driven augmentation. Since volumetric defects can severely influence the usability and durability of L-PBF parts, it is critical to accurately classify defect types (i.e., keyhole, lack of fusion, and gas-entrapped pore) and understand their fabrication conditions and their impacts on the part performance. Additionally, it is reported that each type of defects has distinct morphological features, which can be creatively used for defect classification. In the proposed framework, the distinct morphological features of different types of defects are extracted from the LR-XCT, and they are augmented based on their relationships with the morphological features from high-resolution XCT (HR-XCT) scans. These augmented LR-XCT morphological features are used in ML-based defect classifiers, among which the k-nearest neighbor classifier has achieved the highest defect classification accuracy of 90.6%, with an improvement of 7.7% over directly using the LR-XCT morphological features. Moreover, defect classification with augmented LR-XCT morphological features saves up to 75% of the scanning time compared to HR-XCT scanning.

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Funding

This work was supported by the Federal Aviation Administration (FAA) under grant No. FAA-12-C-AM-AU-A2 and National Institute of Standards and Technology (NIST) under grant No. NIST-70NANB22H084.

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Authors and Affiliations

  1. Department of Industrial and Systems Engineering, Auburn University, Auburn, AL, 36849, USA

    Jiafeng Ye, Jia (Peter) Liu, Aleksandr Vinel & Daniel Silva

  2. Department of Mechanical Engineering, Auburn University, Auburn, AL, 36849, USA

    Arun Poudel, Shuai Shao & Nima Shamsaei

  3. National Center for Additive Manufacturing Excellence (NCAME), Auburn University, Auburn, AL, 36849, USA

    Arun Poudel, Jia (Peter) Liu, Aleksandr Vinel, Daniel Silva, Shuai Shao & Nima Shamsaei

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  1. Jiafeng Ye
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  3. Jia (Peter) Liu
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  4. Aleksandr Vinel
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Contributions

All the authors contributed to the study conception and paper development. Experiments and data collection were performed by Arun Poudel under the supervision of Shuai Shao and Nima Shamsaei. Methodology development and data analysis were performed by Jiafeng Ye under the supervision of Jia Liu, Aleksandr Vinel and Daniel Silva. The first draft of the manuscript was written by Jiafeng Ye, and all the authors reviewed and commented on previous versions of the manuscript. All the authors read and approved the final manuscript.

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Correspondence to Jia (Peter) Liu.

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Appendix

Appendix

The discussion of incorrect defect matches by the algorithmic defect matching model.

The incorrect matches computed by the algorithmic defect matching model to the target defects (TDs) are investigated individually for potential improvement of the model. Since the reasons (e.g., unable to be verified, different scanned areas, scanning problems) for the mismatched pairs are not directly caused by the algorithmic defect matching model, we will temporarily stick to the current model and leave the potential improvement in future work.

Only one pair of defects is incorrectly matched for the defects in part L. As shown in Fig. 8 (a), two possible matches have similar positions in the Z-axis and relative positions to reference defect 1 (RD 1) and reference defect 2 (RD 2) with the TD. Hence, they cannot be verified by manual inspection and are deemed as incorrect matches.

Besides, six pairs of defects are incorrectly matched for the defects in part K due to three different reasons. First, four TDs are at the edge of the LR-XCT scan, and their HR counterparts may not be in the scanned area of the HR-XCT scan. For example, as shown in Fig. 8 (b), no possible match is found to have a similar position in the Z-axis and relative positions to three reference defects. Therefore, the matches to these four TDs are deemed incorrect. Second, as shown in Fig. 8 (c), one TD in the LR-XCT scan is a connected defect of two separate defects in the HR-XCT scan. Therefore, the match to this TD is deemed incorrect. Another mismatched pair is caused by a close distance between two defects. As shown in Fig. 8 (d), the RD 1 in HR-XCT is simultaneously matched to the RD 1 and TD, which are close to each other in the LR-XCT scan, leading to one mismatched pair.

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Ye, J., Poudel, A., Liu, J.(. et al. Machine learning augmented X-ray computed tomography features for volumetric defect classification in laser beam powder bed fusion. Int J Adv Manuf Technol 126, 3093–3107 (2023). https://doi.org/10.1007/s00170-023-11281-9

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