This paper investigates the rheological, mechanical and electrical properties of a Ethylene-Vinyl Acetate (EVA) polymer filled with 1, 3 and 5 wt.% multi-walled carbon nanotubes (MWCNTs). The melt flow and pressure-volume-Temperature (pvT) behaviors of the EVA/MWCNT composites were investigated using a high-pressure capillary rheometer, while the electro-mechanical response was investigated on injection-molded samples. Rheological experiments showed that the melt shear viscosity of the EVA/MWCNT composite is dependent on nanotube loading and, at high shear rates, the viscosity showed temperature-dependent shear thinning behavior with a flow index n < 0.35. The specific volume of the EVA/MWCNT composite decreased with increasing pressure and MWCNT wt.%. The transition temperature, corresponding to the pvT crystallization, increased linearly with increasing pressure, i.e., about 20 to 30 °C when cooling under pressure. The elastic modulus, tensile strength and stress at break increased with increasing MWCNT wt.%, whereas the strain at break decreased, suggesting the formation of MWCNT secondary agglomerates. The electrical conductivity of the EVA/MWCNT composite increased with increasing MWCNT wt.% and melt temperature, reaching ~10⁻² S/m for the composite containing 5 wt.% MWCNTs. Using the statistical percolation theory, the percolation threshold was estimated at 0.9 wt.% and the critical exponent at 4.95.

The quality of the refill friction stir spot welding (RFSSW) process is heavily dependent on the selected welding parameters that influence the resultant joint characteristics. Thermomechanical phenomena integral to the process were investigated using finite element (FE) analysis on two dissimilar materials. This FE analysis was subsequently validated through controlled experiments to ensure reliability. An artificial neural network (ANN) was employed to create a neural model based on an experimental setup involving 120 different sets of welding parameters. The parameters adjusted in the experimental plan included pin penetration depth, rotational speed, retention time, and positioning relative to material hardness. To assess the neural model’s accuracy, outputs such as maximum temperature and normal stress at the end of the welding process were analyzed and validated by six data sets selected for their uniform distribution across the training domain.

Friction stir welding (FSW) began as a fairly specialized joining method, but over the past three decades it has evolved into something considerably more versatile, a manufacturing platform that now handles complex multi-material assemblies and solid-state additive processes with reasonable reliability. This review follows this evolution, paying particular attention to friction stir additive manufacturing (FSAM) and the persistent difficulties that arise when joining dissimilar systems, such as aluminum to steel or metals to polymers, where the fate of the joint is largely decided by how well the intermetallic compounds are kept under control. Machine learning, artificial intelligence, and high-fidelity numerical models are reducing the reliance on trial-and-error that once dominated parameter selection and defect prediction, bringing FSW closer to the operating principles of Industry 4.0. Hybrid variants, including ultrasonically assisted and underwater FSW, also receive attention here, as they offer researchers finer control over heat generation and plastic flow than the standard process allows. Throughout the study, microstructural observations are directly connected to mechanical results, with the aim of analyzing the current state of solid-state manufacturing and identifying the questions that most urgently need answering.

The energy efficiency in metal cutting is, sometimes, very low. Recent studies show that the energy consumed for detaching chips represents only about 15% of the total energy involved in material machining. The available solutions for energy optimization in cutting processes mentioned are the improvement of manufacturing equipment, the optimization of processes, and appropriate production scheduling. Already-performed research shows that to increase energy efficiency, the cutting force must be reduced, for example, by reducing the depth of the cut and by increasing the feed rate. However, this is applicable when the cutting force is quasi-constant during the process, which is not the case for gear teeth cutting when the cutting force significantly varies. The present paper proposes a new solution for energy efficiency increase in gear teeth cutting, namely, the smoothing of the cutting force variation during the machining process, by keeping the area of the detached chip section quasi-constant during the cutting process. This can be reached by replacing the constant rolling feed, currently used in gear teeth cutting, with a rolling feed that varies after an appropriate law. An algorithm dedicated to implementing this solution has been developed. It uses graphical modeling in CATIA to find the variation in the detached chip area. Original MatLab R2018a applications were developed (i) to identify the analytical form of the law that approximates this variation and (ii) to find the variation law of the feed during the rolling motion, if imposing a constant area of the detached chip. The algorithm was successfully applied in the cases of toothing with a rack tool (by slotting) and with a pinion cutter (by slotting and turning). A technical solution for method implementation in practice is also presented.

The optimal management of manufacturing processes can be achieved through a set of optimal decisions, which must be made to choose the best method to follow every time the process planner reaches a point when several potential manufacturing paths branch off. A dedicated method, namely the Holistic Optimization Method (HOM), has already been developed for this purpose and has been validated in several studies based on artificial- and real-instance databases. The HOM consists of two algorithms: (i) the causal identification of a manufacturing process and (ii) a comparative assessment with similar already-assessed manufacturing cases recorded in an instance database. The two algorithms can be used to estimate the values of the different performance indicators of manufacturing processes. Their application for processing cost estimation in the case of the manufacturing processes of bearing components has already shown good results. In this paper, the HOM is presented as a solution for predicting the timespan needed for grinding roller bearing rings. The specific algorithms of the HOM were applied, grounded in the use of a database with data collected from the industrial environment. The cause variables selected to describe the grinding process of the roller bearing rings were the inner and outer diameter of the ring, its width and weight, the machined surface roughness, the grinding stone rotation speed, the cutting speed, the feed rate, and the cutting depth, while the effect variable to be used by the process planner as the decision criterion was the timespan.

Additive manufacturing has evolved beyond monomaterial fabrication, enabling the integration of dissimilar polymers within a single structure to achieve spatially tailored properties. In Fused Filament Fabrication (FFF), however, the discrete, layer-wise deposition and inherent material incompatibilities make the interfacial region a critical determinant of structural integrity. Rather than acting as a simple boundary, the interface governs stress transfer, damage initiation, and failure propagation, especially in biodegradable polymer systems where thermal and rheological mismatches are pronounced. This study investigates bi-component FFF structures manufactured from PLA and PLA/PHA using mechanically interlocked interface geometries (T-type and dovetail configurations). Mechanical performance was assessed through tensile, flexural, and Charpy impact testing, complemented by fracture analysis, surface topography evaluation, and X-ray Computed Tomography (XCT) for internal defect characterization. The results establish correlations between interface design, defect distribution, and overall structural response.