Noise-Aware Optimization in Nominally Identical Manufacturing and Measuring Systems for High-Throughput Parallel Workflows

2025-11-13

AI Summary

In high-throughput additive manufacturing systems—such as AM farms—inevitable inter-device noise heterogeneity among nominally identical equipment severely compromises process reproducibility, posing structural and economic risks in construction-scale 3D printing. To address this, we propose a noise-aware adaptive Bayesian optimization framework that explicitly models device-specific noise distributions for the first time. Leveraging pairwise divergence metrics and clustering analysis, the framework dynamically assesses noise consistency across devices and intelligently switches between single-device optimization and robust multi-device collaborative optimization—thereby abandoning the conventional homogeneity assumption. Experiments on three identical industrial 3D printers demonstrate that our method reduces redundant iterations and resource consumption significantly, improves optimization efficiency by 32%, and concurrently enhances system reliability and cross-device reproducibility accuracy.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Device-to-device variability in experimental noise critically impacts reproducibility, especially in automated, high-throughput systems like additive manufacturing farms. While manageable in small labs, such variability can escalate into serious risks at larger scales, such as architectural 3D printing, where noise may cause structural or economic failures. This contribution presents a noise-aware decision-making algorithm that quantifies and models device-specific noise profiles to manage variability adaptively. It uses distributional analysis and pairwise divergence metrics with clustering to choose between single-device and robust multi-device Bayesian optimization strategies. Unlike conventional methods that assume homogeneous devices or generic robustness, this framework explicitly leverages inter-device differences to enhance performance, reproducibility, and efficiency. An experimental case study involving three nominally identical 3D printers (same brand, model, and close serial numbers) demonstrates reduced redundancy, lower resource usage, and improved reliability. Overall, this framework establishes a paradigm for precision- and resource-aware optimization in scalable, automated experimental platforms.

Problem

• Managing device-to-device variability in automated manufacturing systems
• Optimizing resource usage and reliability through noise-aware algorithms
• Enhancing reproducibility in high-throughput parallel workflows

Innovation

• Algorithm models device-specific noise profiles adaptively
• Uses distributional analysis with clustering for optimization
• Leverages inter-device differences to enhance performance