AccScience Publishing / MSAM / Volume 4 / Issue 3 / DOI: 10.36922/MSAM025200033
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ORIGINAL RESEARCH ARTICLE

Sustainable manufacturing of FDM-manufactured composite impellers using hybrid machine learning and simulation-based optimization

Subramani Raja1 ,  Ahamed Jalaludeen Mohammad Iliyas1 ,  Paneer Selvam Vishnu1 ,  Amaladas John Rajan2 ,  Maher Ali Rusho3 ,  Mohamad Reda Reda Refaai4* ,  Oluseye Adewale Adebimpe5
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1 Center for Advanced Multidisciplinary Research and Innovation, Chennai Institute of Technology, Chennai, Tamil Nadu, India
2 Department of Mechanical Engineering, School of Mechanical Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India
3 Research and Development Unit, Mr.R BUSINESS CORPORATION, Karur, Tamil Nadu, India
4 Department of Mechanical Engineering, College of Engineering, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
5 Department of Industrial and Production Engineering, Faculty of Technology, University of Ibadan, Ibadan, Oyo, Nigeria
MSAM 2025, 4(3), 025200033 https://doi.org/10.36922/MSAM025200033
Received: 14 May 2025 | Accepted: 4 July 2025 | Published online: 28 July 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Conventional optimization of fused deposition modeling (FDM) often relies on trial-and-error or heuristic approaches, which lack scalability and precision, especially for complex geometries such as impellers. While prior studies have integrated artificial intelligence (AI) or multi-criteria decision-making (MCDM) techniques for process optimization, their combined application remains limited, particularly in scenarios that prioritize energy-efficient and sustainable manufacturing. This study introduces a novel hybrid AI-MCDM framework for the multi-objective optimization of FDM-printed composite impellers, integrating mechanical performance, energy consumption, and material utilization within a unified decision-making model. A key feature of the approach is the real-time tracking of energy usage, enabling dynamic evaluation of process efficiency. Experimental validation demonstrates a 7% enhancement in tensile strength, a 25% reduction in energy consumption, and a 30% decrease in material wastage compared to baseline configurations. These results underscore the potential of AI-driven simulation and optimization frameworks to support sustainable additive manufacturing, with significant implications for aerospace, biomedical, and energy sector applications.

Graphical abstract
Keywords
Fused deposition modeling
Rapid prototyping
Machine learning
Multi-criteria decision-making
Sustainable manufacturing
Optimization algorithms
Mechanical characterization
SDG Goals
Funding
Not applicable
Conflict of interest
The authors declare no conflicts of interest.
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Materials Science in Additive Manufacturing, Electronic ISSN: 2810-9635 Published by AccScience Publishing
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