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Investigation of industrial die-cast Al-alloys using X-ray micro-computed tomography and machine learning approach for CT segmentation

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Abstract

The die-casting process creates the opportunity to design and manufacture complex parts in different industrial fields. However, the presence of micro-porosity and cracks can significantly impact the functionality and lifetime of the components. The impregnation technique improves the defect and upgrades the alloy's usability. Hence, identifying the impregnation technique's usefulness in sealing porosity is vital for enhancing the quality of the die casting products. It is hard to detect low atomic number impregnation resin located in the casting defects due to low X-ray attenuation. In this study, microfocus X-ray computed tomography (XCT) with advanced direct conversion detectors could effectively be employed to visualize casting defects in 3D. Also, to recognize the impregnated resin in Al-alloy both qualitatively and quantitatively. Dual-energy XCT recognized the resin material as P601 super sealant quantitatively. Casting defects could be identified in 2D CT images, and it is not easy to detect the impregnated resin with simple intensity-based image processing algorithms. Hence, an approach to improve resin detection through machine learning was studied. With a random forest classifier, trainable weka segmentation was used with three pre-defined classes. It precisely segmented the casting pore, Al-alloy, and resin material after well-trained the known data set.

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Acknowledgements

The authors extend their appreciation to the members of ANSeeN Inc for their technical and instrument support to the XCT.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Hamamatsu TSC, Chuo Hatsumei Institute, 322 lida-cho, Minami-ku, Hamamatsu, 435-0028, Japan

    Katanaga Yusuke, Ajith Bandara, Natsuto Soga & Koichi Kan

  2. Graduate School of Medical Photonics, Shizuoka University, Johoku 3-5-1, Hamamatsu, Shizuoka, 432-8561, Japan

    Katanaga Yusuke, Natsuto Soga & Toru Aoki

  3. ANSeeN Inc, Johoku 3-5-1, Hamamatsu, Shizuoka, 432-8561, Japan

    Akifumi Koike & Toru Aoki

  4. Research Institute of Electronics, Shizuoka University, Johoku 3-5-1, Hamamatsu, Shizuoka, 432-8561, Japan

    Toru Aoki

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  1. Katanaga Yusuke
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Correspondence to Ajith Bandara.

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Yusuke, K., Bandara, A., Soga, N. et al. Investigation of industrial die-cast Al-alloys using X-ray micro-computed tomography and machine learning approach for CT segmentation. Prod. Eng. Res. Devel. 17, 291–305 (2023). https://doi.org/10.1007/s11740-022-01147-6

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