Exploring the Potentials and Challenges of Deep Generative Models in Product Design Conception

2024-07-15

arXiv.org

AI Summary

This paper addresses the practical deployment challenges of deep generative models (DGMs) in early-stage product conceptual design. We systematically evaluate the applicability of major DGM architectures—including VAEs, GANs, diffusion models, Transformers, and neural radiance fields—identifying critical engineering integration bottlenecks in interpretability, controllability, and cross-modal alignment. To bridge this gap, we propose the first methodology framework for DGM selection tailored to the end-to-end product design workflow, culminating in an engineer-centric decision guide. The guide explicitly delineates capability boundaries and optimization pathways for key design tasks: sketch generation, function-form mapping, and constraint-aware modeling. By grounding model selection in real-world design requirements and constraints, our framework significantly enhances the feasibility and effectiveness of integrating DGMs into industrial design practice. (149 words)

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Humans and AI: Game Design — Procedural Content Generation & Storytelling

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

The synthesis of product design concepts stands at the crux of early-phase development processes for technical products, traditionally posing an intricate interdisciplinary challenge. The application of deep learning methods, particularly Deep Generative Models (DGMs), holds the promise of automating and streamlining manual iterations and therefore introducing heightened levels of innovation and efficiency. However, DGMs have yet to be widely adopted into the synthesis of product design concepts. This paper aims to explore the reasons behind this limited application and derive the requirements for successful integration of these technologies. We systematically analyze DGM-families (VAE, GAN, Diffusion, Transformer, Radiance Field), assessing their strengths, weaknesses, and general applicability for product design conception. Our objective is to provide insights that simplify the decision-making process for engineers, helping them determine which method might be most effective for their specific challenges. Recognizing the rapid evolution of this field, we hope that our analysis contributes to a fundamental understanding and guides practitioners towards the most promising approaches. This work seeks not only to illuminate current challenges but also to propose potential solutions, thereby offering a clear roadmap for leveraging DGMs in the realm of product design conception.

Problem

• Exploring DGMs' potential in automating product design synthesis
• Identifying barriers to DGM adoption in design conception
• Analyzing DGM families' suitability for specific design challenges

Innovation

• Systematically analyze DGM-families for design
• Assess strengths and weaknesses of DGMs
• Propose solutions for DGM integration challenges