Metal Additive Manufacturing Parts Inspection Using Convolutional Neural Network

Applied Sciences

Yunlu Zhang¹  Wenyuan Cui¹  Lan Li¹  Xinchang Zhang¹  Frank Liou¹ 

[1] Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA;

关键词: additive manufacturing (am);    metal defects;    quality inspection;    deep learning;    convolutional neural network (cnn);    defect classification;   

DOI  :  10.3390/app10020545

来源: DOAJ

【 摘 要 】

Metal additive manufacturing (AM) is gaining increasing attention from academia and industry due to its unique advantages compared to the traditional manufacturing process. Parts quality inspection is playing a crucial role in the AM industry, which can be adopted for product improvement. However, the traditional inspection process has relied on manual recognition, which could suffer from low efficiency and potential bias. This study presented a convolutional neural network (CNN) approach toward robust AM quality inspection, such as good quality, crack, gas porosity, and lack of fusion. To obtain the appropriate model, experiments were performed on a series of architectures. Moreover, data augmentation was adopted to deal with data scarcity. L2 regularization (weight decay) and dropout were applied to avoid overfitting. The impact of each strategy was evaluated. The final CNN model achieved an accuracy of 92.1%, and it took 8.01 milliseconds to recognize one image. The CNN model presented here can help in automatic defect recognition in the AM industry.

【 授权许可】

Unknown