IEOM Index

5th Indian International Conference on Industrial Engineering and Operations Management

AI BASED AHP AND COCOSO MODEL FOR AUTOMOTIVE PISTON MATERIAL SELECTION IN ADDITIVE MANUFACTURING

Karolina Mohanty , Yashmeet Malviya , Ramanujam Radhakrishnan & Grace Sharon Joy

Publisher: IEOM Society International

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The automotive industry continually strives to develop components that are lighter, stronger, and more sustainable. In this context, the need for systematic and intelligent material selection methods has become increasingly significant. This study investigates MCDM based material selection model for piston production within the framework of emerging additive manufacturing technologies. The research is conducted in two stages. In the first stage, various potential metal composites for additive manufacturing of automotive pistons are evaluated and ranked using an Artificial Intelligence (AI)-assisted Analytic Hierarchy Process (AHP). AI techniques are employed to enhance the precision and objectivity of the pairwise comparison process and to ensure consistency in the evaluation of key criteria such as sustainability, cost-effectiveness, and performance. In the second stage, the highest-ranked composite material identified through the AI–AHP framework is further validated using the Combined Compromise Solution (CoCoSo) method and compared with the conventionally used M174 aluminum alloy. The results reveal that the selected composite exhibits superior mechanical, thermal, and environmental performance compared to M174 aluminum. This study underscores the importance of intelligent, data-driven decision-making framework such as AI-integrated AHP and CoCoSo in achieving optimized and sustainable material selection for automotive composites in additive manufacturing.

Published in: 5th Indian International Conference on Industrial Engineering and Operations Management, Vellore, India

Publisher: IEOM Society International
Date of Conference: November 6 -8 , 2025

ISBN: 979-8-3507-4451-4
ISSN/E-ISSN: 2169-8767

https://doi.org/10.46254/IN05.20250348