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 theAMindustry, 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.


Mechanical and Aerospace Engineering

Keywords and Phrases

Additive manufacturing (AM); Convolutional neural network (CNN); Deep learning; Defect classification; Metal defects; Quality inspection

International Standard Serial Number (ISSN)


Document Type

Article - Journal

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Final Version

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This work is licensed under a Creative Commons Attribution 4.0 License.

Publication Date

11 Jan 2020