This article reviews and analyzes the approaches utilized for monitoring cutting tool conditions. The Research focuses on publications from 2012 to 2022 (10 years), in which Machine Learning and other statistical processes are used to determine the quality, condition, wear, and remaining useful life (RUL) of shearing tools. The paper quantifies the typical signals utilized by researchers and scientists (vibration of the cutting tool and workpiece, the tool cutting force, and the tool’s temperature, for example). These signals are sensitive to changes in the workpiece quality condition; therefore, they are used as a proxy of the tool degradation and the quality of the product. The selection of signals to analyze the workpiece quality and the tool wear level is still in development; however, the article shows the main signals used over the years and their correlation with the cutting tool condition. These signals can be taken directly from the cutting tool or the workpiece, the choice varies, and both have shown promising results. In parallel, the Research presents, analyzes, and quantifies some of the most utilized statistical techniques that serve as filters to cleanse the collected data before the prediction and classification phase. These methods and techniques also extract relevant and wear-sensitive information from the collected signals, easing the classifiers’ work by numerically changing alongside the tool wear and the product quality.

Perfectly coated surfaces are an essential quality feature in the automotive and consumer goods industries. They are the result of an optimized, controlled coating process. Because entire assemblies could be rejected if Out-of-Specification (OOS) parts are installed, this has a severe economic impact. This paper presents a novel, line-integrated multi-camera system with intelligent algorithms for anomaly detection on small KTL-coated aluminum parts. The system also aims to automatize the previously used human inspection to a sophisticated and automated vision system that efficiently detects defects and anomalies on coated parts.

In industrial inspection, the detection of surface defects—such as scratches, dents, or other defects—is crucial for ensuring product quality. However, the limited availability of annotated images of such defects poses challenges for developing reliable detection models. This paper proposes a novel image augmentation algorithm to address this limitation by generating synthetic imperfections from a small sample set. Unlike traditional approaches that augment entire images, the proposed approach isolates defects and applies spatial transformations (e.g., rotation, shear) before blending them into background images. This allows for the creation of diverse training datasets. The algorithm employs a 2D binary map to encode the spatial relationship between defect and background regions of images and then applies an AI-driven model to generate new realistic blended images. By leveraging a generator-discriminator framework, the augmentation process is iteratively refined during training to maintain the style and texture of the original imperfections. Experimental results show that the proposed method significantly enhances the performance of defect detection models compared to existing augmentation techniques while preserving the realism of the generated images.

This study develops a classification model for Electric Vehicles (EVs) using data from EV charging stations. Utilizing a dataset of six charging sessions, a deep learning algorithms, Auto-Encoders, was implemented to classify EVs based on charging time, energy consumption, and charging patterns. The model's effectiveness is assessed through accuracy and precision, offering insights into optimizing EV charging infrastructure and services.

Accurate forecasting of daily solar radiation is essential for renewable energy management, optimization of photovoltaic systems, and power-grid stability. This study presents an evaluation of six forecasting models: SARIMA, LSTM, NARX, Random Forest, XGBoost, and a stacking ensemble. Daily solar radiation and meteorological data were retrieved from the NASA Prediction of Worldwide Energy Resources (NASA POWER) platform for Poruba, Ostrava, Czech Republic. A chronological data-partitioning strategy was adopted, with data from 2001–2020 used for training, 2023 for validation, and 2024–2025 reserved for prospective testing. The modeling framework incorporated meteorological variables, cyclic calendar encodings, lagged radiation observations, and rolling statistical features to capture short-term fluctuations and long-term seasonal dynamics. Among the individual models, XGBoost achieved the strongest test performance (R² = 0.7704), followed by Random Forest (R² = 0.7565), NARX (R² = 0.7524), and LSTM. SARIMA exhibited the weakest predictive accuracy (R² = 0.7265), reflecting its limitations in modeling nonlinear temporal dependencies. The stacking ensemble, combining Linear Regression, Random Forest, and K-Nearest Neighbors as base learners and Ridge regression as the meta-learner, achieved the highest performance (R² = 0.9997), outperforming all individual models on the prospective test set. Statistical significance testing using the Friedman omnibus test, Wilcoxon signed-rank test, and Diebold–Mariano test with Holm–Bonferroni correction confirmed that the ensemble’s superiority was statistically robust across all pairwise comparisons (p < 0.001). These findings demonstrate that ensemble learning, supported by structured feature engineering and temporally consistent model evaluation, provides an effective and generalizable framework for long-term solar radiation forecasting.