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About this Abstract
Meeting TMS Specialty Congress 2026
Symposium 4th World Congress on Artificial Intelligence in Materials & Manufacturing (AIM 2026)
Presentation Title ICME-Driven, Uncertainty-Aware Surrogate Modeling of Composite EV Battery Enclosures Using High-Throughput Simulations and Machine Learning
Author(s) Shadab Anwar Shaikh, Harish Cherukuri, Kranthi Balusu, Ram Devanathan, Ayoub Soulami
On-Site Speaker (Planned) Shadab Anwar Shaikh
Abstract Scope This work presents an ICME-driven, uncertainty-aware surrogate modeling framework for accelerating the design of composite EV battery enclosures with a focus on crash performance. The approach integrates high-throughput finite element simulations of thermoforming and crash with Gaussian Process Regression (GPR) to develop a probabilistic surrogate model that accurately predicts critical crash metrics while quantifying prediction uncertainty. Training data spans a broad range of material and process parameters relevant to composite manufacturing, enabling the surrogate to capture complex nonlinear relationships. Validation against independent simulation datasets demonstrates predictive accuracy with mean absolute percentage errors below 8.1% across all output variables. The results underscore the effectiveness of combining ICME methodologies with machine learning to create computationally efficient, reliable tools for the design optimization and virtual testing of composite battery enclosures in advanced mobility applications.
Proceedings Inclusion? Undecided

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