Anomaly Detection for Antenna-Based Defect Detection in Additive Manufacturing: An Explainable, Instructive Learning Framework

Wenkai Tan, Houbing Herbert Song

Track: Regular Track (Page limit: 6-8 pages)

Keywords: Latent space explanation, Anomaly detection, Signal processing, Additive manufacturing

Abstract: Additive manufacturing enables the layer-by-layer fabrication of complex and customizable structures but remains prone to internal defects that are difficult to detect during fabrication. This work presents an anomaly detection framework for real-time defect monitoring using a custom-designed wireless antenna sensor. The sensor consists of an aluminum split-ring resonator patterned with gold conductive ink and mounted on a ceramic substrate. It passively responds to structural and thermal changes during the manufacturing process by producing frequency-domain scattering parameter signals. To process these high-dimensional signals without requiring defect-labeled data, we develop a zero-bias deep neural network trained exclusively on normal-class samples. The model constructs a class-structured latent space using cosine similarity to learned templates and applies Mahalanobis distance to define statistical decision boundaries. Anomalies are identified when inputs deviate significantly from the expected distribution of normal data. The framework is designed around two deployment-oriented principles. Explainability enables users and domain experts to understand detection decisions through latent space visualization, feature attribution, and statistical reasoning that connects model outputs to interpretable signal characteristics. Instructibility allows users to tune detection sensitivity through threshold control without retraining, adapting the system to different quality requirements and process tolerances. The system is evaluated using experimental data collected from the antenna sensor under controlled thermal variation. Results demonstrate that the proposed method accurately detects anomalous signal patterns without using defect supervision, while maintaining interpretability and user control. This approach offers a transparent, adaptive, and data-efficient solution for in situ defect monitoring in additive manufacturing environments.

Submission Number: 24