通过建模设备噪声差异，提升3D打印等高通量系统稳定性与效率
Noise-Aware Optimization in Nominally Identical Manufacturing and Measuring Systems for High-Throughput Parallel Workflows
- 基于设备特异性噪声建模，动态选择单机或多机优化策略
- 实验显示冗余减少、资源消耗降低且可靠性提升
- 适合大规模自动化制造系统中追求高精度与资源节约的场景
实验噪声的器件间变异对可重复性有重大影响，尤其在自动化高通量系统如增材制造集群中。尽管小规模实验室中可控，但在大规模场景（如建筑级3D打印）下，噪声可能引发结构或经济失败。本文提出一种噪声感知决策算法，通过分布分析与成对差异度量结合聚类，量化并建模各设备特有的噪声特征，以自适应管理变异性。相比传统假设设备同质或采用通用鲁棒性的方法，该框架主动利用器件间差异，提升性能、可重复性与效率。一项涉及三台名义上相同的3D打印机（同品牌、同型号、相近序列号）的实验表明，系统冗余减少、资源使用降低且可靠性提高。整体上，该框架为可扩展自动化实验平台提供了面向精度与资源的优化新范式。
原文摘要 · Abstract (English)
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.
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