Volume 18, No. 6, 2021

Applications Of Supervised Machine Learning In FDM Manufacturing: A Review


Naveen Kumar Suniya , Abhisek Gour

Abstract

Additive manufacturing (AM), also commonly known as 3D Printing, has emerged as a revolutionary technique for manufacturing components having complex geometries. Fused Deposition Modelling (FDM) is one of the most commonly used methods in additive manufacturing due to multiple factors namely, availability of compatible materials, design ease, reduced wastage etc. However, FDM-based additive manufacturing suffers from some critical issues such as increased build time, need for the support structure, lower mechanical strength, sub-optimal dimensional accuracy, surface roughness etc. In recent times, researchers have attempted to improve these output characteristics and overcome some of these limitations with the help of computational intelligence-based methods. Specifically, supervised machine learning-based techniques have proven to be quite useful in model design, quality evaluation as well as in-situ monitoring. This paper explores and outlines opportunities for such applications and reports specific learnings published over the past few years. Analytical insights into the selection of ML models, model training, and current challenges have been presented to guide further research and experimentation in this interdisciplinary area.


Pages: 6199-6218

Keywords: FDM, Machine Learning, Artificial Neural Network, Review, In-situ monitoring.

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