Redundancy Analysis and Mitigation for Machine Learning-Based Process Monitoring of Additive Manufacturing

• URL: http://arxiv.org/abs/2504.21317v1
• Date: Wed, 30 Apr 2025 05:04:53 GMT
• Title: Redundancy Analysis and Mitigation for Machine Learning-Based Process Monitoring of Additive Manufacturing
• Authors: Jiarui Xie, Yaoyao Fiona Zhao,
• Abstract summary: Excessive redundancy leads to increased equipment costs, compromised model performance, and high computational requirements.<n>This paper defines redundancy in ML-based AM process monitoring and categorizes it into sample-level, feature-level, and model-level redundancy.<n>A comprehensive multi-level redundancy mitigation (MLRM) framework is proposed, incorporating advanced methods such as data registration, downscaling, cross-modality knowledge transfer, and model pruning.
• Score: 3.414636048610798
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The deployment of machine learning (ML)-based process monitoring systems has significantly advanced additive manufacturing (AM) by enabling real-time defect detection, quality assessment, and process optimization. However, redundancy is a critical yet often overlooked challenge in the deployment and operation of ML-based AM process monitoring systems. Excessive redundancy leads to increased equipment costs, compromised model performance, and high computational requirements, posing barriers to industrial adoption. However, existing research lacks a unified definition of redundancy and a systematic framework for its evaluation and mitigation. This paper defines redundancy in ML-based AM process monitoring and categorizes it into sample-level, feature-level, and model-level redundancy. A comprehensive multi-level redundancy mitigation (MLRM) framework is proposed, incorporating advanced methods such as data registration, downscaling, cross-modality knowledge transfer, and model pruning to systematically reduce redundancy while improving model performance. The framework is validated through an ML-based in-situ defect detection case study for directed energy deposition (DED), demonstrating a 91% reduction in latency, a 47% decrease in error rate, and a 99.4% reduction in storage requirements. Additionally, the proposed approach lowers sensor costs and energy consumption, enabling a lightweight, cost-effective, and scalable monitoring system. By defining redundancy and introducing a structured mitigation framework, this study establishes redundancy analysis and mitigation as a key enabler of efficient ML-based process monitoring in production environments.

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