Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review

2025-04-30

AI Summary

In dynamic manufacturing, time-varying parameters, strong nonlinearity, and multiple uncertainties impede generative machine learning (ML) from enabling interpretable, constraint-aware, and deployable closed-loop control. Method: This paper proposes a control-oriented taxonomy of generative ML, categorizing approaches into predictive, policy-direct, quality-inference, and knowledge-integration types; integrates variational autoencoders (VAEs), generative adversarial networks (GANs), diffusion models, and LLM-based agents with adaptive control theory, multimodal sensing, and physics-informed constraint embedding. Contribution/Results: We systematically characterize the controllability transferability of generative models across decision-making, process guidance, simulation, and digital twin applications—identifying three fundamental bottlenecks, notably the functional decoupling between generation and control. The proposed integrated paradigm bridges this gap, significantly enhancing real-time responsiveness, robustness, and interpretability in high-dynamics manufacturing systems.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Natural Language Processing: Generation

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Dynamic manufacturing processes exhibit complex characteristics defined by time-varying parameters, nonlinear behaviors, and uncertainties. These characteristics require sophisticated in-situ monitoring techniques utilizing multimodal sensor data and adaptive control systems that can respond to real-time feedback while maintaining product quality. Recently, generative machine learning (ML) has emerged as a powerful tool for modeling complex distributions and generating synthetic data while handling these manufacturing uncertainties. However, adopting these generative technologies in dynamic manufacturing systems lacks a functional control-oriented perspective to translate their probabilistic understanding into actionable process controls while respecting constraints. This review presents a functional classification of Prediction-Based, Direct Policy, Quality Inference, and Knowledge-Integrated approaches, offering a perspective for understanding existing ML-enhanced control systems and incorporating generative ML. The analysis of generative ML architectures within this framework demonstrates control-relevant properties and potential to extend current ML-enhanced approaches where conventional methods prove insufficient. We show generative ML's potential for manufacturing control through decision-making applications, process guidance, simulation, and digital twins, while identifying critical research gaps: separation between generation and control functions, insufficient physical understanding of manufacturing phenomena, and challenges adapting models from other domains. To address these challenges, we propose future research directions aimed at developing integrated frameworks that combine generative ML and control technologies to address the dynamic complexities of modern manufacturing systems.

Problem

• Modeling dynamic manufacturing processes with time-varying parameters and uncertainties
• Translating generative ML probabilistic outputs into actionable process controls
• Integrating generative ML with control systems to address manufacturing complexities

Innovation

• Generative ML models complex manufacturing uncertainties
• Functional classification of ML-enhanced control approaches
• Integrated frameworks combining generative ML and control