Application of Machine Learning Methods to Improve Dimensional Accuracy in Additive Manufacturing

Adoption of additive manufacturing for producing end-use products faces a range of limitations. For instance, quality of AM-fabricated parts varies from run to run and from machine to machine. There is also a lack of standards developed for AM processes. Another limitation is inconsistent dimensional accuracy error, which is often out of the standard tolerancing range. To tackle these challenges, this work aims at predicting scaling ratio for each part separately depending on its placement, orientation and CAD characteristics. Recent attention to machine learning techniques as a tool for data analysis in additive manufacturing shows that such methods as classical artificial neural networks (ANN), such as multi-layer perceptron (MLP), and convolutional neural networks (CNN) have a great potential. For the data collected on polymer powder bed fusion system (EOS P395), MLP outperformed CNN based on accuracy of prediction and mean squared error. The predicted scaling ratio can be used to adjust size of the parts before fabrication.