A novel RF-enabled Non-Destructive Inspection Method through Machine Learning and Programmable Wireless Environments

2026-01-10

arXiv.org

AI Summary

This work proposes a novel radio-frequency (RF)-driven paradigm for non-destructive inspection in industrial settings where conventional optical methods fail to acquire high-quality images due to occlusion, hazardous conditions, or physical constraints. By leveraging a programmable wireless environment (PWE) to encode RF wavefronts and integrating a generative adversarial network (GAN) to learn the mapping between RF signals and visual representations of objects, the method enables high-fidelity image reconstruction without requiring line-of-sight access. This approach represents the first integration of programmable RF environments with generative machine learning for industrial imaging, achieving a structural similarity index (SSIM) of 99.5% in reconstructed images under complex operational conditions. The results demonstrate a significant enhancement in both the robustness and applicability of non-destructive testing in challenging industrial scenarios.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

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
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Contemporary industrial Non-Destructive Inspection (NDI) methods require sensing capabilities that operate in occluded, hazardous, or access restricted environments. Yet, the current visual inspection based on optical cameras offers limited quality of service to that respect. In that sense, novel methods for workpiece inspection, suitable, for smart manufacturing are needed. Programmable Wireless Environments (PWE) could help towards that direction, by redefining the wireless Radio Frequency (RF) wave propagation as a controllable inspector entity. In this work, we propose a novel approach to Non-Destructive Inspection, leveraging an RF sensing pipeline based on RF wavefront encoding for retrieving workpiece-image entries from a designated database. This approach combines PWE-enabled RF wave manipulation with machine learning (ML) tools trained to produce visual outputs for quality inspection. Specifically, we establish correlation relationships between RF wavefronts and target industrial assets, hence yielding a dataset which links wavefronts to their corresponding images in a structured manner. Subsequently, a Generative Adversarial Network (GAN) derives visual representations closely matching the database entries. Our results indicate that the proposed method achieves an SSIM 99.5% matching score in visual outputs, paving the way for next-generation quality control workflows in industry.

Problem

• Non-Destructive Inspection
• Programmable Wireless Environments
• RF sensing
• smart manufacturing
• visual inspection

Innovation

• Programmable Wireless Environments
• RF Sensing
• Generative Adversarial Network
• Non-Destructive Inspection
• Wavefront Encoding