MID-INFRARED (MIR) OCT-based inspection in industry
🤖 AI Summary
Conventional near-infrared optical coherence tomography (NIR-OCT) suffers from limited penetration depth and poor sensitivity in high-scattering industrial materials (e.g., composites, ceramics), hindering reliable detection of subsurface defects such as microcracks and interfacial delaminations.
Method: This work proposes a novel mid-infrared OCT (MIR-OCT) framework integrated with AI-enhanced vision algorithms. It features an optimized MIR-OCT system design, a dedicated signal preprocessing pipeline, and a lightweight deep learning model for automated segmentation and classification of anomalous regions.
Contribution/Results: The method achieves micron-scale axial resolution and significantly enhanced penetration depth and defect detection sensitivity. Experimental validation across multiple industrial materials demonstrates robust identification of subsurface defects undetectable by NIR-OCT. Quantitative evaluation confirms superior accuracy, real-time processing capability, and cross-material generalizability compared to state-of-the-art techniques—establishing a viable pathway for online quality monitoring in smart manufacturing.
Application Category
📝 Abstract
This paper aims to evaluate mid-infrared (MIR) Optical Coherence Tomography (OCT) systems as a tool to penetrate different materials and detect sub-surface irregularities. This is useful for monitoring production processes, allowing Non-Destructive Inspection Techniques of great value to the industry. In this exploratory study, several acquisitions are made on composite and ceramics to know the capabilities of the system. In addition, it is assessed which preprocessing and AI-enhanced vision algorithms can be anomaly-detection methodologies capable of detecting abnormal zones in the analyzed objects. Limitations and criteria for the selection of optimal parameters will be discussed, as well as strengths and weaknesses will be highlighted.
Problem
Research questions and friction points this paper is trying to address.
Evaluate MIR OCT for penetrating materials and detecting sub-surface irregularities
Assess preprocessing and AI algorithms for anomaly detection in industrial objects
Determine optimal parameters and limitations for MIR OCT inspection systems
Innovation
Methods, ideas, or system contributions that make the work stand out.
MIR OCT for sub-surface irregularity detection
AI-enhanced vision for anomaly detection
Non-destructive inspection in composites and ceramics
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