AS7341 Spectral Sensor with Machine Learning for Non-Contact Temperature Monitoring in Electrolytic-Plasma Hardening

Electrolytic-plasma hardening of steel components requires reliable non-contact temperature monitoring, but traditional pyrometry is complicated by the variable emissivity of steel and the intense radiation of the plasma envelope. This work presents an approach that repurposes a compact multispectral AS7341 sensor into a virtual temperature sensor based on physically grounded spectral feature engineering and regularized machine learning. The use of logarithmic ratios of the near-infrared channel (940 nm) to the visible channels suppresses the plasma contribution and linearizes Wien’s radiation law. On a controlled dataset of 20 cycles, this increases the Pearson correlation with the peak temperature from r = 0.498 (raw NIR channel) to r = 0.781 for the log(NIR/Clear) feature. Current is identified as a confounding variable; normalizing the NIR/Clear ratio by the cycle-averaged current (r = 0.761) ensures correct signal interpretation under varying process conditions. Two narrow channels–NIR (940 nm) and F8 (680 nm)–provide accuracy equivalent to the broadband Clear channel (r = 0.778 vs. 0.781), thus simplifying hardware implementation. Ridge regression using three weakly correlated features (log(NIR/Clear), cycle duration, and initial temperature) achieves a mean absolute error of 91.4 °C under leave-one-out cross-validation (LOOCV) and 85.5 °C on an independent current-group test (R² = 0.536). Independent verification by scanning electron microscopy and Vickers microhardness on 30KhGSA steel confirms reliable separation of the three thermal regimes: underheating (<800 °C, 280–320 HV), optimal quenching (800–900 °C, 620–680 HV, fine-needle martensite), and overheating (>900 °C, 540–590 HV). The proposed set of spectral features provides a physically justified basis for a low-cost industrial temperature sensor for electrolytic-plasma processing.