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

• URL: http://arxiv.org/abs/2508.20358v2
• Date: Sat, 13 Sep 2025 17:24:01 GMT
• Title: Developing a Multi-Modal Machine Learning Model For Predicting Performance of Automotive Hood Frames
• Authors: Abhishek Indupally, Satchit Ramnath,
• Abstract summary: This paper develops a multimodal machine-learning architecture that learns from different modalities of the same data to predict performance metrics.<n>It also aims to use the MMML architecture to enhance the efficiency of engineering design processes by reducing reliance on computationally expensive simulations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Speed Always Wins: A Survey on Efficient Architectures for Large Language Models [51.817121227562964]
  Large Language Models (LLMs) have delivered impressive results in language understanding, generation, reasoning, and pushes the ability boundary of multimodal models.<n> Transformer models, as the foundation of modern LLMs, offer a strong baseline with excellent scaling properties.<n>The traditional transformer architecture requires substantial computations and poses significant obstacles for large-scale training and practical deployment.
  arXiv  Detail & Related papers  (2025-08-13T14:13:46Z)
• FUDOKI: Discrete Flow-based Unified Understanding and Generation via Kinetic-Optimal Velocities [76.46448367752944]
  multimodal large language models (MLLMs) unify visual understanding and image generation within a single framework.<n>Most existing MLLMs rely on autore (AR) architectures, which impose inherent limitations on future development.<n>We introduce FUDOKI, a unified multimodal model purely based on discrete flow matching.
  arXiv  Detail & Related papers  (2025-05-26T15:46:53Z)
• MoLAE: Mixture of Latent Experts for Parameter-Efficient Language Models [10.623996218106564]
  Mixture of Experts (MoE) has become a key architectural paradigm for efficiently scaling Large Language Models (LLMs)<n>We introduce MoLAE, a novel parameterization that reformulating expert operations through a shared projection into a lower-dimensional latent space, followed by expert-specific transformations.<n>We show that MoLAE significantly improves efficiency across multiple dimensions while preserving model capabilities.
  arXiv  Detail & Related papers  (2025-03-29T14:35:34Z)
• Will Pre-Training Ever End? A First Step Toward Next-Generation Foundation MLLMs via Self-Improving Systematic Cognition [89.50068130832635]
  Self-Improving cognition (SIcog) is a self-learning framework for constructing next-generation foundation MLLMs by multimodal knowledge.<n>We propose Chain-of-Description for step-by-step visual understanding and integrate structured Chain-of-Thought (CoT) reasoning to support in-depth multimodal reasoning.<n>Experiments demonstrate SIcog's effectiveness in developing MLLMs with enhanced multimodal cognition.
  arXiv  Detail & Related papers  (2025-03-16T00:25:13Z)
• Read-ME: Refactorizing LLMs as Router-Decoupled Mixture of Experts with System Co-Design [59.00758127310582]
  We propose a novel framework Read-ME that transforms pre-trained dense LLMs into smaller MoE models. Our approach employs activation sparsity to extract experts. Read-ME outperforms other popular open-source dense models of similar scales.
  arXiv  Detail & Related papers  (2024-10-24T19:48:51Z)
• Cliqueformer: Model-Based Optimization with Structured Transformers [102.55764949282906]
  Large neural networks excel at prediction tasks, but their application to design problems, such as protein engineering or materials discovery, requires solving offline model-based optimization (MBO) problems.<n>We present Cliqueformer, a transformer-based architecture that learns the black-box function's structure through functional graphical models (FGM)<n>Across various domains, including chemical and genetic design tasks, Cliqueformer demonstrates superior performance compared to existing methods.
  arXiv  Detail & Related papers  (2024-10-17T00:35:47Z)
• Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts [54.529880848937104]
  We develop a unified MLLM with the MoE architecture, named Uni-MoE, that can handle a wide array of modalities. Specifically, it features modality-specific encoders with connectors for a unified multimodal representation. We evaluate the instruction-tuned Uni-MoE on a comprehensive set of multimodal datasets.
  arXiv  Detail & Related papers  (2024-05-18T12:16:01Z)
• Large Language Model Agent as a Mechanical Designer [7.136205674624813]
  We propose a framework that leverages a pretrained Large Language Model (LLM) in conjunction with an FEM module to autonomously generate, evaluate, and refine structural designs.<n>LLM operates without domain-specific fine-tuning, using general reasoning to propose design candidates, interpret FEM-derived performance metrics, and apply structurally sound modifications.<n>Compared to Non- Sorting Genetic Algorithm II (NSGA-II), our method achieves faster convergence and fewer FEM evaluations.
  arXiv  Detail & Related papers  (2024-04-26T16:41:24Z)
• Mechanistic Design and Scaling of Hybrid Architectures [114.3129802943915]
  We identify and test new hybrid architectures constructed from a variety of computational primitives. We experimentally validate the resulting architectures via an extensive compute-optimal and a new state-optimal scaling law analysis. We find MAD synthetics to correlate with compute-optimal perplexity, enabling accurate evaluation of new architectures.
  arXiv  Detail & Related papers  (2024-03-26T16:33:12Z)
• An Augmented Surprise-guided Sequential Learning Framework for Predicting the Melt Pool Geometry [4.021352247826289]
  Metal Additive Manufacturing (MAM) has reshaped the manufacturing industry, offering benefits like intricate design, minimal waste, rapid prototyping, material versatility, and customized solutions. A crucial aspect for MAM's success is understanding the relationship between process parameters and melt pool characteristics. Traditional machine learning (ML) methods, while effective, depend on large datasets to capture complex relationships. Our study introduces a novel surprise-guided sequential learning framework, SurpriseAF-BO, signaling a significant shift in MAM.
  arXiv  Detail & Related papers  (2024-01-10T23:05:23Z)
• Multi-modal Machine Learning in Engineering Design: A Review and Future Directions [9.213020570527451]
  This paper presents a comprehensive overview of the current state, advancements, and challenges of multi-modal machine learning (MMML) We highlight the inherent challenges in adopting MMML in engineering design, and proffer potential directions for future research. MMML models, as the next generation of intelligent design tools, hold a promising future to impact how products are designed.
  arXiv  Detail & Related papers  (2023-02-14T01:40:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.