Selective laser melting is the most commonly used additive manufacturing technique for fabricating metal components. However, the SLMed part quality still largely suffered from the porosity defects that can significantly affect the mechanical properties. Recently, in situ monitoring based on machine learning has been recognized as an effective method to overcome this challenge. In this work, a deep learning method is developed for in situ part quality inspection. The layer-wise visual images are used as the inputs without manual feature extraction and a deep transfer learning (DTL) model combining deep convolutional neural network and transfer learning is creatively applied. First, an off-axial in situ monitoring system by a high-resolution digital camera is developed to capture the images of each deposited layer. Then, samples with different part quality levels are produced by varying process parameters. Thereafter, based on the porosity measurement results obtained by optical microscopy, each captured visual image is labeled. An image dataset associated with a label of three categories of poor, medium, and high quality is created. Finally, the proposed DTL is employed to perform the classification tasks, aiming to identify the part quality based on the layer-wise visual images. Results show that a 99.89% classification accuracy of the developed DTL was obtained, revealing the feasibility and effectiveness of using layer-wise visual images without manual feature extraction to realize quality inspection. Overall, the proposed DTL method provides a promising solution to monitor part quality and reduce porosity defects during the printing process.
