Regularized Adaptive Weight Noise Injection-Based Evolutionary Training with Generative AI for Industrial Dye Recipe Optimization

Résumé

This study introduces the Regularized Adaptive Weight Noise Injection-Based Evolutionary (RAWE) training approach, enhanced by generative Artificial Intelligence (AI), to optimize the dye recipe formulation in industrial textile manufacturing. RAWE integrates a self-adaptive evolutionary strategy with adaptive weight noise injection, dynamically balancing the exploration and exploitation during model training. A key innovation of RAWE is its use of generative AI to synthesize high-quality, domain-specific data, addressing the challenge of limited historical dyeing records. This synthetic data generation significantly improves the model generalization and robustness, enabling more accurate and reliable predictions in real-world industrial settings. The effectiveness of RAWE is demonstrated through its deployment in a real-world textile dyeing automation system, where it achieves significant improvements in dye recipe optimization. The results show that RAWE reduces the material waste, minimizes the production costs, and enhances the color consistency compared to traditional methods. By combining generative AI with adaptive evolutionary training, RAWE offers a scalable and practical solution for complex industrial processes, aligning with the latest advancements in automated Machine Learning (ML) and AI-driven optimization.

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