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

TL;DR

This paper introduces a new RF-enabled non-destructive inspection method using programmable wireless environments and machine learning, enabling high-quality visual inspection in challenging industrial settings.

Contribution

It presents a novel RF sensing pipeline that encodes wavefronts and employs GANs to generate visual inspection outputs, advancing non-destructive testing technology.

Findings

Achieves 99.5% SSIM in visual output quality

Effectively correlates RF wavefronts with industrial asset images

Demonstrates potential for next-generation industrial quality control

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.