From Automation to Autonomy in Smart Manufacturing: A Bayesian Optimization Framework for Modeling Multi-Objective Experimentation and Sequential Decision Making
Avijit Saha Asru, Hamed Khosravi, Imtiaz Ahmed, Abdullahil Azeem
TL;DR
This work addresses efficient, multi-objective material discovery within Industry 4.0 by moving beyond fixed automation toward autonomous optimization. It introduces BMSDM, a Bayesian multi-objective sequential decision-making framework that uses a Gaussian Process surrogate and the batch qEHVI acquisition to intelligently select experiments and rapidly converge to the Pareto front. Through a comparative study on MAX-phase data against DoE baselines and state-of-the-art MOBO methods, BMSDM demonstrates superior data efficiency and front quality, reducing the number of experiments and data points required to reach high-quality trade-offs. The findings highlight the potential of autonomous, data-driven optimization to accelerate smart manufacturing and novel material discovery in real-world settings.
Abstract
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.