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

Christina Schenk, Miguel Hernández-del-Valle, Luis Calero-Lumbreras, Marcus Noack, Maciej Haranczyk

TL;DR

This work tackles how device-to-device noise variability harms reproducibility in automated, high-throughput experimentation. It introduces a noise-aware decision-making framework that uses distributional analysis, clustering, and pairwise divergence metrics to choose between single-device and multi-device Bayesian optimization (BO). Through a case study with three nominally identical 3D printers, the approach demonstrates that device-specific optimization improves convergence, reliability, and resource efficiency, while pooling data across heterogeneous devices can hinder performance. The framework offers a principled pathway to balance precision and scalability in scalable automated platforms, with potential extensions to real-time noise analytics and predictive maintenance.

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.

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

TL;DR

This work tackles how device-to-device noise variability harms reproducibility in automated, high-throughput experimentation. It introduces a noise-aware decision-making framework that uses distributional analysis, clustering, and pairwise divergence metrics to choose between single-device and multi-device Bayesian optimization (BO). Through a case study with three nominally identical 3D printers, the approach demonstrates that device-specific optimization improves convergence, reliability, and resource efficiency, while pooling data across heterogeneous devices can hinder performance. The framework offers a principled pathway to balance precision and scalability in scalable automated platforms, with potential extensions to real-time noise analytics and predictive maintenance.

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.

Paper Structure

This paper contains 16 sections, 12 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methods
  3. Initial Experimental Data Collection
  4. Noise Analysis: Distribution Analysis, Clustering, and Pairwise Divergence Metrics
  5. Kolmogorov–Smirnov (KS) Statistic
  6. Kullback–Leibler (KL) Divergence
  7. Wasserstein (Earth Mover’s) Distance
  8. Bhattacharyya Distance
  9. Optimization Decision Criterion
  10. Single-Device Optimization and Multi-Device Optimization
  11. Results
  12. Initial Experimental Data Collection
  13. Noise Analysis Results: Distribution Analysis, Clustering and Pairwise Divergence Metrics
  14. Single-Device Optimization vs. Multi-Device Optimization
  15. Discussion
  16. ...and 1 more sections

Figures (4)

  • Figure 1: Workflow.
  • Figure 2: Noise and clustering analysis across printers: (a) Standard deviation of print weights per repetition mode and printer, (b) Kernel density estimates of weight distributions, (c) Cluster membership distribution by printer, (d) Noise value (standard deviation) versus mean weight for each measurement, with points colored by printer and marker shape indicating cluster assignment.
  • Figure 3: Convergence and performance analysis of multi-device optimization illustrating device-to-device variability. Robust mean performance (black) and individual performances (Printer 1: red, Printer 2: blue, Printer 3: green).
  • Figure 4: Convergence and performance analysis of single-device optimization illustrating device-to-device variability.