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

TL;DR

This paper introduces a noise-aware optimization framework that models device-specific noise profiles to improve reproducibility and efficiency in high-throughput manufacturing systems, especially when device variability is significant.

Contribution

It presents a novel decision-making algorithm that explicitly leverages inter-device differences for adaptive optimization, surpassing traditional homogeneous assumptions.

Findings

Reduced resource usage in 3D printing case study

Improved reliability and reproducibility

Enhanced performance through device-specific noise modeling

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.