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

2025-04-30

AI Summary

Machine learning–based monitoring systems for additive manufacturing suffer from redundancy—causing excessive computational overhead, degraded model performance, and elevated hardware costs—yet lack a unified definition or systematic mitigation framework. This work formally defines redundancy at three granularities: sample-level, feature-level, and model-level. We propose the Multi-Level Redundancy Mitigation (MLRM) framework, which integrates end-to-end collaborative compression techniques, including data registration, temporal downsampling, cross-modal knowledge transfer, and model pruning. Evaluated on defect detection in directed energy deposition, MLRM reduces inference latency by 91%, classification error rate by 47%, and storage footprint by 99.4%, substantially improving real-time responsiveness, energy efficiency, and industrial deployability. Our core contribution is the first integrated framework for redundancy analysis and mitigation tailored specifically to ML-based monitoring in additive manufacturing.

Technology Category

• Machine Learning: Hardware-aware ML
• Computer Vision: Multi-modal Vision
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• Defines and categorizes redundancy in ML-based AM monitoring
• Proposes a multi-level framework to reduce redundancy effectively
• Validates framework with improved latency, error rate, and storage

Innovation

• Defines and categorizes redundancy in ML-based AM monitoring
• Proposes multi-level redundancy mitigation (MLRM) framework
• Reduces latency, error rate, and storage requirements significantly