Antony Morales-Cervantes

High-temperature oxidation affects the surface condition and quality of steel products, yet automated identification of the thermal stages governing oxide-scale formation remains limited. This study evaluated whether infrared thermography time series combined with dimensionality reduction and supervised learning could classify the heating, isothermal, and cooling phases of AISI 1045 steel oxidation. Five controlled Joule-heating experiments generated 3356 thermographic records. Each thermogram was flattened, and principal component analysis was applied independently to each experiment; the first two principal components were then used to train Random Forest, Multi-Layer Perceptron, Support Vector Machine, k-Nearest Neighbors, and XGBoost classifiers. Performance was assessed using leave-one-group-out cross-validation to preserve specimen-level independence. The Multi-Layer Perceptron achieved the best performance, with an accuracy of 0.8934, Macro-precision of 0.8740, Macro-recall of 0.8938, and Macro-F1-score of 0.8562. The highest class-specific area under the receiver operating characteristic curve was 0.95 for cooling. Most errors occurred between temporally adjacent phases, whereas confusion between heating and cooling was negligible, indicating physically consistent classification. These findings establish a proof of concept for automated thermal-phase recognition from raw thermographic sequences and support further validation under broader materials, processing conditions, and industrial environments.

Periodontal diseases such as gingivitis and periodontitis are common oral health conditions that require timely and accurate detection. This study presents a non-invasive diagnostic support system that integrates infrared thermography with clinical data to classify periodontal health status. A cross-sectional study was conducted on 91 individuals, categorized as healthy, with gingivitis, or with periodontitis. Thermographic images from three facial perspectives were analyzed to extract gingival temperature features, which were combined with clinical parameters including plaque index, age, sex, smoking status, and the presence of systemic conditions. Several machine learning models were evaluated using ten-fold cross-validation, both with and without dimensionality reduction. A two-step classification approach yielded the best results: logistic regression was used to identify periodontitis, followed by XGBoost to differentiate between healthy and gingivitis cases. The combined model achieved an accuracy of 94.51% and an F1-score of 94.49%, while models based solely on thermographic data reached an accuracy of 75.82%. These findings support the feasibility of using localized infrared thermographic measurements and artificial intelligence to improve the classification of periodontal disease. The proposed method offers a promising non-invasive solution to enhance diagnostic accuracy and inform personalized dental care.

This study presents a pioneering approach, integrating infrared thermography and deep learning to analyse surface oxide layers on AISI 1045 steel, addressing the critical need for advanced monitoring in steelmaking processes. Using thermography for observation and semantic segmentation for accurate identification, 50 tests between 200 and 700 °C were analysed in a Joule-controlled heating system to study the formation and thickening of oxide layers on steel surfaces. A convolutional neural network (CNN), specifically SegNet, was trained for semantic segmentation, facilitating detailed analysis. The model achieved an overall accuracy of 96.40% in identifying the presence of oxide. By quantifying pixelation changes, relationships in oxide evolution kinetics were obtained, and by quantifying the activation energy in isothermal cases, the magnitude is in the range reported by other works. The approach also highlighted the potential for non-destructive monitoring and control on a large scale without compromising personnel safety. This potential could improve industrial process control, predict surface quality or provide data relevant to sub-processes.