Predicting the dimensional variation of geometries produced through FDM 3D printing employing supervised machine learning
Prairit Sharma, Harshal Vaid, Ritam Vajpeyi, Pritish Shubham, Krishna Mohan Agarwal, Dinesh Bhatia
Abstract
One of the most popular techniques of Additive Manufacturing (AM) being used currently is Fused Deposition Modelling (FDM). FDM is currently a nascent technology and has significant scope for improvement, particularly when it comes to the dimensional accuracy of the printed parts. The dimensional accuracy of the parts governs the usability of the product. These products when used in assemblies need to have very tight tolerances. These tolerances play a vital role in the quality of the finished product. For FDM to become commercially viable, the ability to predict this dimensional variation caused by different print parameters is essential, it would result in improved quality of the fabricated product and saving of time and resources. In this research, we studied the impact of significant parameters on the dimensional accuracy for different geometries such as cylindrical shafts, holes and rectangular slots. Predicting the dimensional variation is accomplished by using Decision Tree Machine Learning Algorithm. This algorithm's result is most suitable and provides accurate predictions; furthermore, the effectiveness of the model developed is validated by the R2 score of 0.67, the model can be further developed to establish industry-friendly functionality.