Applying Neural-Network-Based Machine Learning to Additive Manufacturing: Current Applications, Challenges, and Future Perspectives

Additive manufacturing (AM), also known as three-dimensional printing, is gaining increasing attention from academia and industry due to the unique advantages it has in comparison with traditional subtractive manufacturing. However, AM processing parameters are difficult to tune, since they can exert a huge impact on the printed microstructure and on the performance of the subsequent products. It is a difficult task to build a process–structure–property–performance (PSPP) relationship for AM using traditional numerical and analytical models. Today, the machine learning (ML) method has been demonstrated to be a valid way to perform complex pattern recognition and regression analysis without an explicit need to construct and solve the underlying physical models. Among ML algorithms, the neural network (NN) is the most widely used model due to the large dataset that is currently available, strong computational power, and sophisticated algorithm architecture. This paper overviews the progress of applying the NN algorithm to several aspects of the AM whole chain, including model design, in situmonitoring, and quality evaluation. Current challenges in applying NNs to AM and potential solutions for these problems are then outlined. Finally, future trends are proposed in order to provide an overall discussion of this interdisciplinary area.