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

arXiv: https://arxiv.org/abs/2505.00210 · HTML: https://mlsanity.com/paper/2505.00210/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 85 references that are in the map.
- First posted on arXiv 30 Apr 2025.

## Builds on (most cited first, 30 of 33)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [RT-1: Robotics Transformer for Real-World Control at Scale](https://mlsanity.com/paper/2212.06817/) (2022)
- [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://mlsanity.com/paper/2106.01345/) (2021)
- [Planning with Diffusion for Flexible Behavior Synthesis](https://mlsanity.com/paper/2205.09991/) (2022)
- [Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation](https://mlsanity.com/paper/2209.05451/) (2022)
- [MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation](https://mlsanity.com/paper/2302.08113/) (2023)
- [Implicit Behavioral Cloning](https://mlsanity.com/paper/2109.00137/) (2021)
- [Tutorial on Variational Autoencoders](https://mlsanity.com/paper/1606.05908/) (2016)
- [Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images](https://mlsanity.com/paper/1506.07365/) (2015)
- [DALL-E-Bot: Introducing Web-Scale Diffusion Models to Robotics](https://mlsanity.com/paper/2210.02438/) (2022)
- [Cautious Model Predictive Control using Gaussian Process Regression](https://mlsanity.com/paper/1705.10702/) (2017)
- [Aligning Optimization Trajectories with Diffusion Models for Constrained Design Generation](https://mlsanity.com/paper/2305.18470/) (2023)
- [Adaptive Policy Learning for Offline-to-Online Reinforcement Learning](https://mlsanity.com/paper/2303.07693/) (2023)
- [Towards autonomous system: flexible modular production system enhanced with large language model agents](https://mlsanity.com/paper/2304.14721/) (2023)
- [Multimodal VAE Active Inference Controller](https://mlsanity.com/paper/2103.04412/) (2021)
- [Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review](https://mlsanity.com/paper/2110.12076/) (2021)
- [Hybrid thermal modeling of additive manufacturing processes using physics-informed neural networks for temperature prediction and parameter identification](https://mlsanity.com/paper/2206.07756/) (2022)
- [AdaDiff: Accelerating Diffusion Models through Step-Wise Adaptive Computation](https://mlsanity.com/paper/2309.17074/) (2023)
- [Deep Neural Operator Enabled Digital Twin Modeling for Additive Manufacturing](https://mlsanity.com/paper/2405.09572/) (2024)
- [Real-Time Decision-Making for Digital Twin in Additive Manufacturing with Model Predictive Control using Time-Series Deep Neural Networks](https://mlsanity.com/paper/2501.07601/) (2025)
- [Cooperative Aerial-Ground Multi-Robot System for Automated Construction Tasks](https://mlsanity.com/paper/2204.13926/) (2022)
- [DeepInspect: An AI-Powered Defect Detection for Manufacturing Industries](https://mlsanity.com/paper/2311.03725/) (2023)
- [On The Reliability Of Machine Learning Applications In Manufacturing Environments](https://mlsanity.com/paper/2112.06986/) (2021)
- [Self-Supervised Meta-Learning for All-Layer DNN-Based Adaptive Control with Stability Guarantees](https://mlsanity.com/paper/2410.07575/) (2024)
- [Unsupervised Multimodal Fusion of In-process Sensor Data for Advanced Manufacturing Process Monitoring](https://mlsanity.com/paper/2410.22558/) (2024)

## Built on by (most cited first, 2 of 2)

- [ARISE: Agentic Rubric-Guided Iterative Survey Engine for Automated Scholarly Paper Generation](https://mlsanity.com/paper/2511.17689/) (2025)
- [Generative Multi-Robot Motion Planning via Diffusion Modeling with Multi-Agent Reinforcement Learning Guidance](https://mlsanity.com/paper/2606.00933/) (2026)

## Abstract (arXiv)

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.