Hybrid AI–Taguchi–ANOVA Approach for Thermographic Monitoring of Electronic Devices
Filippo Laganà; Danilo Pratticò; Marco F. Quattrone; Salvatore A. Pullano; Salvatore Calcagno · 2026 · Eng—Advances in Engineering
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Defects in printed circuit boards (PCBs), if not detected promptly, may persist over time until they cause the failure of critical components. Traditional monitoring methods, which are limited to simulations or superficial measurements, obstruct predictive maintenance and real-time fault detection. To address these issues and enhance real-time diagnostics of thermal anomalies in PCBs, this work proposes an integrated system that combines infrared thermography (IRT), artificial intelligence (AI) algorithms, and Taguchi–ANOVA statistical techniques. IR thermography was employed to identify therm
Abstract by Filippo Laganà; Danilo Pratticò; Marco F. Quattrone; Salvatore A. Pullano; Salvatore Calcagno, Eng—Advances in Engineering (2026) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.3390/eng7010028