Developing a Multi-Modal Machine Learning Model For Predicting Performance of Automotive Hood Frames

TL;DR

This paper introduces a multimodal machine learning model that predicts automotive hood frame performance, reducing reliance on simulations and enabling rapid, accurate design evaluations especially in early development stages.

Contribution

The study develops a novel MMML architecture that integrates multiple data modalities to improve performance prediction and generalizes to unseen frame geometries, enhancing engineering design efficiency.

Findings

MMML outperforms single-modality models in accuracy.

The model successfully predicts unseen frame geometries.

Results demonstrate potential to accelerate automotive design processes.

Abstract

Is there a way for a designer to evaluate the performance of a given hood frame geometry without spending significant time on simulation setup? This paper seeks to address this challenge by developing a multimodal machine-learning (MMML) architecture that learns from different modalities of the same data to predict performance metrics. It also aims to use the MMML architecture to enhance the efficiency of engineering design processes by reducing reliance on computationally expensive simulations. The proposed architecture accelerates design exploration, enabling rapid iteration while maintaining high-performance standards, especially in the concept design phase. The study also presents results that show that by combining multiple data modalities, MMML outperforms traditional single-modality approaches. Two new frame geometries, not part of the training dataset, are also used for prediction using the trained MMML model to showcase the ability to generalize to unseen frame models. The findings underscore MMML's potential in supplementing traditional simulation-based workflows, particularly in the conceptual design phase, and highlight its role in bridging the gap between machine learning and real-world engineering applications. This research paves the way for the broader adoption of machine learning techniques in engineering design, with a focus on refining multimodal approaches to optimize structural development and accelerate the design cycle.