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

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.

Keywords

Fused deposition modeling / Rapid prototyping / Machine learning / Multi-criteria decision-making / Sustainable manufacturing / Optimization algorithms / Mechanical characterization / SDG Goals

Funding

This project is sponsored by Prince Sattam Bin Abdulaziz University (PSAU) as part of funding for its SDG Roadmap Research Funding Programme project number PSAU2023-SDG-2023/SDG/74.

Conflict of Interest

The authors declare no conflicts of interest.

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