This paper investigates the extrusion process to manufacture bimetallic cylinders combining a magnesium alloy core (AZ31B) and a titanium alloy sleeve (Ti6Al4V) of interest in aeronautical applications. A robust finite element model has been developed to determine the most influential parameters and to study the effect of them on the extrusion force and damage induced by means of Design of Experiments (DOE) and Taguchi method. The results show that the most influential parameters in the extrusion forces are the friction between sleeve and container/die and the height of the cylinder; and the less influential ones are the process temperature and ram speed. Moreover, minimum values of forces along with low damage can be reached by favorable interface contact conditions, minimizing the friction at the core-container/die interface, as the main influencing factor; followed by the geometrical dimensions of the billet, being the billet height more important when paying attention to the minimum forces, and being the core diameter when considering the minimum damage as the most important criterion. The results can potentially be used to improve the efficiency of this kind of extrusion process and the quality of the extruded part that, along with the use of lightweight materials, can contribute to sustainable production approaches.

Multi-material co-extrusion is a complex thermo-mechanical forming process used to obtain bimetallic billets. Its complexity is due to the combination of diffusion phenomena in the interface of both materials together with the high temperature and pressure generated and the different flow stress characteristics created by the joining of dissimilar materials. Accordingly, the selection of optimal process parameters becomes key to ensure process feasibility. In this work, a comparison among different multi-criteria decision making (MCDM) methodologies, together with different weighting methods, were applied to the simulation results by using DEFORM3D© software to select the optimal combination of process parameters to fulfil the criteria of minimum damage, extrusion force, and tool wear, together with the maximum reduction in the average grain size.

This paper investigates the effect that the selection of the die material generates on the extrusion process of bimetallic cylindrical billets combining a magnesium alloy core (AZ31B) and a titanium alloy sleeve (Ti6Al4V) of interest in aeronautical applications. A robust finite element model is developed to analyze the variation in the extrusion force, damage distribution, and wear using different die materials. The results show that die material is a key factor to be taken into account in multi-material extrusion processes. The die material selection can cause variations in the extrusion force from 8% up to 15%, changing the effect of the extrusion parameters, for example, optimum die semi-angle. Damage distribution in the extrudate is also affected by die material, mainly in the core. Lastly, die wear is the most affected parameter due to the different hardness of the materials, as well as due to the variations in the normal pressure and sliding velocity, finding critical values in the friction coefficient for which the die cannot be used for more than one forming stage because of the heavy wear suffered. These results can potentially be used to improve the efficiency of this kind of extrusion process and the quality of the extruded part that, along with the use of lightweight materials, can contribute to sustainable production approaches.

Predictive maintenance based on vibration monitoring can significantly improve gearbox reliability in heavy-industry environments. Although it is well established in vibration engineering that operating regimes influence vibration levels, the contribution of this work lies in providing an integrated, data-driven diagnostic linkage between continuously acquired IoT vibration indicators and key process/operational variables to identify and quantify the dominant drivers of vibration escalation. This study deployed wireless IoT sensors for continuous acquisition of RMS vibration and lubrication temperature in gearboxes operating in cement and mining plants and applied multivariate machine learning models to detect anomalies and identify root causes. We compared boosted multilayer feedforward neural networks, boosted trees, and k-nearest neighbors using RMS vibration and process variables including mill feed, lubrication pressures, and temperature. The boosted neural network delivered superior validation performance and isolated low or near-zero mill feed during operation as the primary driver of elevated RMS vibration, with lubrication instability acting as a secondary interacting factor. This shifts the diagnosis from a generic “high vibration during transients” statement to actionable operational mitigations—minimum feed set-points, controlled ramping logic, and lubrication pressure governance—supported by multivariate evidence. Our results motivate further validation with k-fold and out-of-time tests.

The selection of the most suitable material is one of the key decisions to be made during the design stage of a manufacturing process. Traditional approaches, such as Ashby maps based on material properties, are widely used in industry. However, in the production of multi-material components, the criteria for the selection can include antagonistic approaches. The aim of this work is to implement a methodology based on the results of process simulations for several materials and to classify them by applying an advanced data analytics method based on machine learning (ML)—in this case, the support vector regression (SVR) or multi-criteria decision-making (MCDM) methodology. Specifically, the multi-criteria optimization and compromise solution (VIKOR) was combined with entropy weighting methods. To achieve this, a finite element model (FEM) was built to evaluate the extrusion force and the die wear during the multi-material co-extrusion process of bimetallic Ti6Al4V-AZ31B billets. After applying SVR and VIKOR in combination with the entropy weighting methodology, a comparison was established based on material selection and the complexity of the methodology used. The results show that the material chosen in both methodologies is very similar, but the MCDM method is easier to implement because there is no need for evaluating the error of the prediction model, and the time required for data preprocessing is less than the time needed when applying SVR. This new methodology is proven to be effective as an alternative to traditional approaches and is aligned with the new trends in industry based on simulation and data analytics.