In the era of Industry 4.0 and the transition toward Industry 5.0, advanced manufacturing is increasingly driven by data analytics, artificial intelligence, and cyber-physical systems. The integration of intelligent monitoring systems and self-learning algorithms is reshaping machining processes, enabling higher efficiency, precision, and sustainability. Recent advancements in smart factories emphasize the use of AI-powered process control, enabling machines to self-optimize, self-correct, and even self-retrain to maintain optimal performance. This paper proposes a hybrid control and monitoring framework designed to enhance turning operations by integrating artificial neural networks (ANNs) for predictive modeling and adaptive recalibration. The system leverages machine learning (ML) to improve machining efficiency, tool longevity, and energy consumption optimization. By implementing forward and inverse ANN models, the framework enables real-time estimation of cutting forces and energy consumption, facilitating data-driven decision-making in machining processes. Furthermore, an adaptive recalibration mechanism ensures continuous model updates, allowing the system to dynamically adjust based on evolving machining conditions such as tool wear, material properties, and environmental variations. This research contributes to these advancements by proposing an ANN-based hybrid approach, predictive modeling, and adaptive recalibration. The proposed framework ensures continuous monitoring, automated adjustments, and intelligent decision-making, making it a scalable and adaptable solution for modern machining operations.

The increasing demand for energy efficiency in manufacturing has driven the need for advanced modeling techniques to optimize the machining processes. The honing process, critical for achieving high-precision surface finishes in manufacturing, faces challenges in optimizing tool wear and material removal for enhanced sustainability and efficiency. This study develops a predictive modeling framework using machine learning techniques, including support vector regression (SVR), random forest (RF), and XGBoost, to forecast tool wear (h₁–h₈) and mass loss in honing processes. Experimental tests were conducted on EN-GJL-300 gray cast-iron workpieces using diamond abrasive blades (FEPA F120 and F240) under varied conditions (rotation speed, translation speed, and pressure). The models, trained with 5-fold cross-validation and hyperparameter tuning via GridSearchCV, achieved high accuracy, with SVR yielding R² values of 0.9609–0.9782 for wear predictions and XGBoost achieving R² of 0.9005 for mass predictions. Incorporating grain size as a predictor showed that finer grains (54 µm vs. 120 µm) reduced wear, thereby improving prediction reliability. The proposed models enable precise control of honing parameters, enhancing tool life and process efficiency, with implications for sustainable manufacturing in automotive and precision engineering applications.

The increasing demand for energy efficiency in manufacturing has driven the need for advanced modeling techniques to optimize power consumption in machining processes. This study presents a novel approach to modeling power consumption in milling processes using machine learning, leveraging a custom-designed braking device integrated into the milling machine’s main spindle to measure friction forces with high precision. A comprehensive dataset of observations, including parameters such as speed, force, intensity, apparent power, active power, and power factor, was collected under loaded conditions. Nine machine learning models—Linear Regression, Random Forest, Support Vector Regression, Polynomial Regression, Multi-Layer Perceptron with 2 and 3 layers, K-Nearest Neighbors, Bagging, and a hybrid Probabilistic Random Forest—Multi-Layer Perceptron (PIRF–MLP)—were evaluated using 5-fold cross-validation to ensure robust performance assessment. The PIRF–MLP model achieved the highest performance, demonstrating superior accuracy in predicting utile power. The feature importance analysis revealed that force and speed significantly influence power consumption. The proposed methodology, validated on a milling machine, offers a scalable solution for real-time energy monitoring and optimization in machining, contributing to sustainable manufacturing practices. Future work will focus on expanding the dataset and testing the models across diverse machining conditions to enhance generalizability.

Product innovation involves an understanding of technology through the introduction of a product that is new or has significantly improved characteristics and intended uses with the purpose of upgrading or even changing, in this case, the mobility landscape. Although product development is creative in upgrading components, the implementation requires a systematic approach to guide the processes that must be taken into account and applied to achieve the implementation of a new product to market. To be successful it is important that the needs, preferences and limitations, be taken into consideration. In this paper, an optimized methodology for developing a new product is proposed starting from the existing ones. By investigating the current concepts of solutions to improve the ability to move physically, it is attempted to propose a typology to address the mobility problem. On a basic manual wheelchair, a system will be attached to ensure overcoming the obstacles and to guarantee the ability to negociate effectively.

A mechanism can be defined as several rigid bodies connected to produce constrained relative motion between them and transmit forces and couples from the source of input power to result in motion. The movement of a body is studied using geometry so the linksare considered to be rigid. The connections between links are modeled being ideal movement, pure rotation or sliding. The design of mechanism presupposes knowledge of the direction of motion, velocity, accelerations and the forces of each component at various positions during the operating cycle. The necessary informations can be obtained from the kinematic analysis of the motion of the mechanism. Because the purpose of a planar mechanism is to move one of the links or a point of an element through a specific motion, it is important to verify that the mechanism performs the established function. This paper presents the problem of modelling and analyzing the six bar linkages mechanism using specialized software. Regression analysis will be performed to find the optimum domain lenghts of the parameters bars taking into account the angles of the mechanism to ensure the stability of the entire equipment. The results of the model were used to correct and optimize the design of the product. Effectiveness of regression models has been determined by analyzing correlation coefficient and by comparing with the experimental results. It has been concluded that regression modelling can be used to predict the datas accurately.

The paper deals with comparative analysis of machined surfaces by classic electrical discharge machining (EDM) and hybrid ultrasonic EDM of CoCr alloys, using computer vision aimed at emphasizing the advantages of this hybrid technology. The analysis revealed generally the superior stability of EDM+US process against classic EDM explained by better evacuation of debris from the working gap due to ultrasonically induced cavitation. This key phenomenon also contributed to the enhancement of machining rate by removing the material in liquid state and also the in solid state from the microgeometry peaks but also reducing the surface roughness if the power on the ultrasonic chain is optimzed.