Process-aware and high-fidelity microstructure generation using stable diffusion

2025-07-01

AI Summary

Modeling the mapping between material processing parameters and microstructures remains challenging due to sparse experimental data and the continuous nature of process variables. To address this, we propose a high-fidelity microstructure generation method based on diffusion models. We pioneer the adaptation of Stable Diffusion 3.5 Large to materials science, introducing a numerically aware embedding mechanism that enables precise, controllable synthesis conditioned on continuous parameters—such as annealing temperature, duration, and magnification. Efficient fine-tuning is achieved via DreamBooth and LoRA, while a U-Net–VGG16 hybrid semantic segmentation model is developed to rigorously assess generation quality. Quantitative evaluation shows the synthesized microstructures closely match real ones: two-point radius correlation error <2.1%, line-path statistics error <0.6%, and phase segmentation accuracy of 97.1%. This framework significantly enhances the reliability and interpretability of data-driven materials design.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Generation
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Synthesizing realistic microstructure images conditioned on processing parameters is crucial for understanding process-structure relationships in materials design. However, this task remains challenging due to limited training micrographs and the continuous nature of processing variables. To overcome these challenges, we present a novel process-aware generative modeling approach based on Stable Diffusion 3.5 Large (SD3.5-Large), a state-of-the-art text-to-image diffusion model adapted for microstructure generation. Our method introduces numeric-aware embeddings that encode continuous variables (annealing temperature, time, and magnification) directly into the model's conditioning, enabling controlled image generation under specified process conditions and capturing process-driven microstructural variations. To address data scarcity and computational constraints, we fine-tune only a small fraction of the model's weights via DreamBooth and Low-Rank Adaptation (LoRA), efficiently transferring the pre-trained model to the materials domain. We validate realism using a semantic segmentation model based on a fine-tuned U-Net with a VGG16 encoder on 24 labeled micrographs. It achieves 97.1% accuracy and 85.7% mean IoU, outperforming previous methods. Quantitative analyses using physical descriptors and spatial statistics show strong agreement between synthetic and real microstructures. Specifically, two-point correlation and lineal-path errors remain below 2.1% and 0.6%, respectively. Our method represents the first adaptation of SD3.5-Large for process-aware microstructure generation, offering a scalable approach for data-driven materials design.

Problem

• Generating realistic microstructure images from processing parameters
• Overcoming limited training data for microstructure synthesis
• Adapting diffusion models for process-aware material design

Innovation

• Stable Diffusion 3.5 adapted for microstructure generation
• Numeric-aware embeddings encode continuous processing variables
• DreamBooth and LoRA for efficient domain transfer