# From Automation to Autonomy in Smart Manufacturing: A Bayesian Optimization Framework for Modeling Multi-Objective Experimentation and Sequential Decision Making

arXiv: https://arxiv.org/abs/2504.04244 · HTML: https://mlsanity.com/paper/2504.04244/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 114 references that are in the map.
- First posted on arXiv 5 Apr 2025.

## Builds on (most cited first, 16 of 16)

- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [pymoo: Multi-objective Optimization in Python](https://mlsanity.com/paper/2002.04504/) (2020)
- [Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2006.05078/) (2020)
- [Stochastic Optimization with Importance Sampling](https://mlsanity.com/paper/1401.2753/) (2014)
- [Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces](https://mlsanity.com/paper/2109.10964/) (2021)
- [Input Warping for Bayesian Optimization of Non-stationary Functions](https://mlsanity.com/paper/1402.0929/) (2014)
- [Multi-Objective Bayesian Optimization with Active Preference Learning](https://mlsanity.com/paper/2311.13460/) (2023)
- [AI-based Modeling and Data-driven Evaluation for Smart Manufacturing Processes](https://mlsanity.com/paper/2008.12987/) (2020)
- [A Unified Industrial Large Knowledge Model Framework in Industry 4.0 and Smart Manufacturing](https://mlsanity.com/paper/2312.14428/) (2023)
- [Ordinal Bayesian Optimisation](https://mlsanity.com/paper/1912.02493/) (2019)
- [An Augmented Surprise-guided Sequential Learning Framework for Predicting the Melt Pool Geometry](https://mlsanity.com/paper/2401.05579/) (2024)
- [Towards Futuristic Autonomous Experimentation--A Surprise-Reacting Sequential Experiment Policy](https://mlsanity.com/paper/2112.00600/) (2021)
- [Practical Batch Bayesian Optimization for Less Expensive Functions](https://mlsanity.com/paper/1811.01466/) (2018)
- [A Data Driven Sequential Learning Framework to Accelerate and Optimize Multi-Objective Manufacturing Decisions](https://mlsanity.com/paper/2304.09278/) (2023)
- [Learning non-stationary and discontinuous functions using clustering, classification and Gaussian process modelling](https://mlsanity.com/paper/2211.16909/) (2022)
- [Large-Batch, Iteration-Efficient Neural Bayesian Design Optimization](https://mlsanity.com/paper/2306.01095/) (2023)

## Built on by (most cited first, 1 of 1)

- [Boosting Predictive Performance on Tabular Data through Data Augmentation with Latent-Space Flow-Based Diffusion](https://mlsanity.com/paper/2511.16571/) (2025)

## Abstract (arXiv)

Discovering novel materials with desired properties is essential for driving innovation. Industry 4.0 and smart manufacturing have promised transformative advances in this area through real-time data integration and automated production planning and control. However, the reliance on automation alone has often fallen short, lacking the flexibility needed for complex processes. To fully unlock the potential of smart manufacturing, we must evolve from automation to autonomous systems that go beyond rigid programming and can dynamically optimize the search for solutions. Current discovery approaches are often slow, requiring numerous trials to find optimal combinations, and costly, particularly when optimizing multiple properties simultaneously. This paper proposes a Bayesian multi-objective sequential decision-making (BMSDM) framework that can intelligently select experiments as manufacturing progresses, guiding us toward the discovery of optimal design faster and more efficiently. The framework leverages sequential learning through Bayesian Optimization, which iteratively refines a statistical model representing the underlying manufacturing process. This statistical model acts as a surrogate, allowing for efficient exploration and optimization without requiring numerous real-world experiments. This approach can significantly reduce the time and cost of data collection required by traditional experimental designs. The proposed framework is compared with traditional DoE methods and two other multi-objective optimization methods. Using a manufacturing dataset, we evaluate and compare the performance of these approaches across five evaluation metrics. BMSDM comprehensively outperforms the competing methods in multi-objective decision-making scenarios. Our proposed approach represents a significant leap forward in creating an intelligent autonomous platform capable of novel material discovery.