Hangai Yoshihiko, Ozawa So, Okada Kenji, Tanaka Yuuki, Amagai Kenji, Suzuki Ryosuke
Graduate School of Science and Technology, Gunma University, Kiryu 376-8515, Japan.
Materials (Basel). 2023 Feb 24;16(5):1894. doi: 10.3390/ma16051894.
Owing to its lightweight and excellent shock-absorbing properties, aluminum foam is used in automotive parts and construction materials. If a nondestructive quality assurance method can be established, the application of aluminum foam will be further expanded. In this study, we attempted to estimate the plateau stress of aluminum foam via machine learning (deep learning) using X-ray computed tomography (CT) images of aluminum foam. The plateau stresses estimated by machine learning and those actually obtained using the compression test were almost identical. Consequently, it was shown that plateau stress can be estimated by training using the two-dimensional cross-sectional images obtained nondestructively via X-ray CT imaging.
由于泡沫铝的轻质和出色的减震性能，它被用于汽车零部件和建筑材料。如果能够建立一种无损质量保证方法，泡沫铝的应用将得到进一步拓展。在本研究中，我们尝试通过机器学习（深度学习）利用泡沫铝的X射线计算机断层扫描（CT）图像来估计泡沫铝的平台应力。通过机器学习估计的平台应力与使用压缩试验实际获得的平台应力几乎相同。因此，结果表明可以通过使用通过X射线CT成像无损获得的二维横截面图像进行训练来估计平台应力。
