Prediction of geometry deviations in additive manufactured parts: comparison of linear regression with machine learning algorithms

Dimensional accuracy in additive manufacturing (AM) is still an issue compared with the tolerances for injection molding. In order to make AM suitable for the medical, aerospace, and automotive industries, geometry variations should be controlled and managed with a tight tolerance range. In the previously published article, the authors used statistical analysis to develop linear models for the prediction of dimensional features of laser-sintered specimens. Two identical builds with the same material, process, and build parameters were produced, resulting in 434 samples for mechanical testing (ISO 527-2 1BA). The developed linear models had low accuracy, and therefore needed an application of more advanced data analysis techniques. In this work, machine learning techniques are applied for the same data, and results are compared with the previously reported linear models. The linear regression model is the best for width. Multilayer perceptron and gradient boost regressor models have outperformed other for thickness and length. The recommendations on how the developed models can be used in the future are proposed.

Automated summary

The study investigates how to improve dimensional accuracy in additive manufacturing for better applications in medical, aerospace, and automotive industries through statistical analysis and machine learning techniques. Results show that linear regression model performs the best in width, while multilayer perceptron and gradient boost regressor models outperform others in thickness and length.