Audio-visual cross-modality knowledge transfer for machine learning-based in-situ monitoring in laser additive manufacturing

TL;DR

This paper introduces cross-modality knowledge transfer methods for laser additive manufacturing monitoring, enabling high-accuracy defect detection with reduced sensor requirements by transferring knowledge between audio and visual data modalities.

Contribution

It proposes three novel CMKT methods, including semantic alignment, fully supervised, and semi-supervised mapping, to improve feature representation and reduce hardware costs in LAM monitoring.

Findings

Semantic alignment achieves 98.6% accuracy without audio sensors.

CMKT methods outperform traditional multimodal fusion in accuracy.

Explainable AI reveals improved feature relevance and noise reduction.

Abstract

Various machine learning (ML)-based in-situ monitoring systems have been developed to detect anomalies and defects in laser additive manufacturing (LAM) processes. While multimodal fusion, which integrates data from visual, audio, and other modalities, can improve monitoring performance, it also increases hardware, computational, and operational costs. This paper introduces a cross-modality knowledge transfer (CMKT) methodology for LAM in-situ monitoring, which transfers knowledge from a source modality to a target modality. CMKT enhances the representativeness of the features extracted from the target modality, allowing the removal of source modality sensors during prediction. This paper proposes three CMKT methods: semantic alignment, fully supervised mapping, and semi-supervised mapping. The semantic alignment method establishes a shared encoded space between modalities to facilitate knowledge transfer. It employs a semantic alignment loss to align the distributions of identical groups (e.g., visual and audio defective groups) and a separation loss to distinguish different groups (e.g., visual defective and audio defect-free groups). The two mapping methods transfer knowledge by deriving features from one modality to another using fully supervised and semi-supervised learning approaches. In a case study for LAM in-situ defect detection, the proposed CMKT methods were compared with multimodal audio-visual fusion. The semantic alignment method achieved an accuracy of 98.6% while removing the audio modality during the prediction phase, which is comparable to the 98.2% accuracy obtained through multimodal fusion. Using explainable artificial intelligence, we discovered that semantic alignment CMKT can extract more representative features while reducing noise by leveraging the inherent correlations between modalities.