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

Stavros Tsimpoukis, Dimitrios Tyrovolas, Sotiris Ioannidis, Maria Kafesaki, Ian F. Akyildiz, George K. Karagiannidis, Christos K. Liaskos

TL;DR

This work tackles the limitations of optical NDI in occluded and privacy-sensitive industrial settings by introducing a RF-enabled framework that leverages Programmable Wireless Environments (PWE) to encode scene geometry into RF wavefronts. A graph-based PWE routing mechanism, together with software-defined metasurfaces and a pix2pix GAN, maps structured RF Readings to grayscale visual representations (Digital Twins), guided by a Pearson-correlation based similarity matrix. The approach demonstrates high visual fidelity, achieving an SSIM of $99.5\%$ on simulated datasets, and enables privacy-preserving, illumination-robust inspection suitable for Industry 4.0 integrations. The proposed pipeline potentially enables real-time, non-invasive quality control and XR-enabled remote inspection, with future extensions toward material-aware volumetric monitoring.

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.

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

TL;DR

This work tackles the limitations of optical NDI in occluded and privacy-sensitive industrial settings by introducing a RF-enabled framework that leverages Programmable Wireless Environments (PWE) to encode scene geometry into RF wavefronts. A graph-based PWE routing mechanism, together with software-defined metasurfaces and a pix2pix GAN, maps structured RF Readings to grayscale visual representations (Digital Twins), guided by a Pearson-correlation based similarity matrix. The approach demonstrates high visual fidelity, achieving an SSIM of on simulated datasets, and enables privacy-preserving, illumination-robust inspection suitable for Industry 4.0 integrations. The proposed pipeline potentially enables real-time, non-invasive quality control and XR-enabled remote inspection, with future extensions toward material-aware volumetric monitoring.

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.
Paper Structure (13 sections, 13 equations, 5 figures, 2 tables, 1 algorithm)

This paper contains 13 sections, 13 equations, 5 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. System Model & Methodology
  4. System Model
  5. Methodology
  6. RF-Encoding Process
  7. Similarity Metric
  8. RF-Encoding
  9. PWE Routing and Visual Reconstruction
  10. PWE-Routing
  11. Visual Inspection Reconstruction
  12. Simulation Results
  13. Conclusion

Figures (5)

  • Figure 1: Overview of the NDI process via RF-Encoding manipulations, within an indoor PWE. While the right visualization illustrates an abstract representation of the overall RF-Wavefronts to Digital Twin visuals workflow.
  • Figure 2: Examples of Ray-Tracing and Ray-Routing in the PWE environment.
  • Figure 3: Indicative results of Digital Twin visuals generated by the pix2pix model.
  • Figure 4: Examples of GAN's results, where Leftmost: $\mathcal{I}(\hat{\boldsymbol{w}})$, Middle: GAN output, Rightmost: Ground truth.
  • Figure 5: Examples of GAN-metric mismatches, where Leftmost: GAN output image, Middel: Ground truth database image, Rightmost: Matched database image.