Machine Learning Perspective of Predictive Modelling for Additive Manufacturing

DOI

10.2991/978-94-6239-628-9_18How to use a DOI?

Keywords

Machine Learning; Additive Manufacturing; Prediction modelling; Supervised Learning; Unsupervised learning

Abstract

Additive Manufacturing (AM) has proven to be a revolutionary technique of fabrication in numerous fields because of its salient features of complexity in geometries and customization of components. However, there are many challenges to printing the part with good quality and reliability, which are linked to the phenomenon of component warping and poor surface finish. The growth of digital manufacturing technologies has led to pathways to the utilization of data-driven machine learning (ML) in AM to get the benefits of quality assurance, process enhancement, and intricate system modelling techniques. The present paper discusses about the employment of ML to support AM operations. In this paper, the predictive modelling for AM via ML has been examined in terms of its potential to enhance product quality. The present work has successively underlined the manners of the ML integration as data-driven ML approach for product enhancements. A comprehensive review on predictive modelling techniques has revealed the critical role of ML in highlighting the importance of understanding material characteristics, working conditions and process parameters. Various ML algorithms, including regression models and deep learning frameworks, are evaluated for their efficacy in predicting geometric deviations, mechanical properties like fatigue life, temperature profile, melt pool characteristics, optimization of process parameters, and real-time monitoring to minimize defects. It has been concluded that ML algorithm can be effective for the prediction of AM processes in the metal part fabrication. It has also been summated that modern machine learning algorithms can optimize process parameters and facilitate the analysis of in-process defect detection. In AM processes, machine learning supports professionals in process planning, production planning, assessment, and product quality management.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Jasvinder Singh
AU  - Sameer Sharma
AU  - Rahul Dev Gupta
PY  - 2026
DA  - 2026/03/31
TI  - Machine Learning Perspective of Predictive Modelling for Additive Manufacturing
BT  - Proceedings of the International Conference on Recent Trends in Intelligent Computing, Manufacturing, and Electronics (rTIME 2025)
PB  - Atlantis Press
SP  - 193
EP  - 209
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-628-9_18
DO  - 10.2991/978-94-6239-628-9_18
ID  - Singh2026
ER  -