The rapid progress in artificial intelligence (AI) based image generation led to groundbreaking achievements, like OpenAI's DALL-E 2, showcasing state-of-the-art generative models in deep learning and computer vision. Recently, the Denoising Diffusion Probabilistic Model (DDPM) has emerged as a strong contender, excelling in generating high-resolution images with complex features similar to those found in real-world images. In this study, we investigate DDPM's potential as both generator and predictor of scanning electron microscope (SEM) images, encompassing both known and unseen microstructural conditions. To rigorously evaluate DDPM, we curated a comprehensive dataset comprising 27 distinct cast-forged AZ80 magnesium alloy components with varied process parameters and microstructure features. Some conditions were held back during training to test DDPM's predictive abilities for unseen scenarios. Our study demonstrates the model's remarkable capacity to capture the inherent physical relationships between process parameters and microstructure features. We scrutinize the synthesized images alongside real-world SEM counterparts, undertaking a comprehensive analysis of various morphological properties. Remarkably, the results show the model's performance, with an average error of 6.36 % +/- 0.42 for measured microstructural properties in seen conditions and an equally impressive 6.67 % +/- 0.85 for unseen conditions. This study envisions a transformative shift in materials science, as advanced AI predictive models offer new potential to streamline the laborious process of microstructure image generation.
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