Andrea Corrado

Electric discharge machining processes are prominent in the fastest-growing industries because of their accuracy, achievable complex workpiece shapes, and cost-effectiveness. Furthermore, the machining of high-quality difficult-to-machine alloys is becoming critical in the aerospace, manufacturing, and defence industries. While the optimisation of EDM parameters is essential for improving machining outcomes, it is also important to consider the trade-offs between different performances metrics, such as material removal rate and part accuracy. Part accuracy in terms of dimensional and geometric deviations from nominal values was rarely considered in the literature, if not by the authors. Balancing these factors remains a challenge in the field of EDM. Therefore, this work aims to carry out a multi-objective optimisation of both MRR and part accuracy. A Ni-based alloy (Inconel-625) was used that is widely used in creep-resistant turbine blades and vanes and turbine disks in gas turbine engines for aerospace and defence industries. Four performance indices were optimised simultaneously: two related to the performance of the EDM process and two connected with the form deviations of the manufactured surfaces. Multi-hole copper electrodes having different diameters and three process parameters were varied during the experimental tests. Grey relational analysis and the Adaptive Neuro-Fuzzy Inference System method were used for optimisation. Grey relational analysis found that the following values of the process parameter—0.16 mm of multi-hole electrode diameter, 12 Amperes of Peak current, 200 µs of pulse on time and 0.2 kg/m² as dielectric pressure—produce the optimal performance, i.e., a material removal rate of 0.099 mm³/min, an electrode wear rate of 0.0002 g/min, a circularity deviation of 0.0043 mm and a cylindricity deviation of 0.027 mm. From the experimental examination using multi-hole electrodes, it is concluded that the material removal rate increases and the electrode wear rate decreases because of the availability of higher spark discharge areas between the electrode and work material interface. The Adaptive Neuro-Fuzzy Inference System models showed minimum mean percentage error and, therefore, better performance in comparison with regression models.

Industry 4.0 needs to have a digital representation of the real manufacturing and assembly processes to foresee the effects of modifications on equipment, tools and processes. Assembly processes often use adhesive to keep together the components because it has many advantages. The simplest example of adhesive assembly is a single lap joint. In the literature, the attention is focused on nominal adhesive assemblies, that do not represent the real products and that are tested to evaluate the product's strength. Therefore, the obtained mechanical performances are far from those connected with the real products. The present work takes into account the geometric deviations of a single lap joint, as the adherends' misalignment, due to the manufacturing process and used equipment on its strength. A numerical tool of the literature was modified to deal with adherends' misalignment to estimate both the tensile and the bending strength. The numerical results were validated through experimental tests. The developed numerical model shows a very low deviation from experimental results. The original contribution of this work is that the developed numerical model simulates the adhesive process of a real joint with adherends' misalignment and not of its nominal geometry; thus, providing a tool more useful in optics Industry 4.0 to represent a process closer to the real.

In this work, the influence of hydrothermal ageing on performances of CFRP single lap bonded joints subjected to two types of pre-treatments was investigated. The first is a degreasing pre-treatment, while the second if a laser texturing pre-treatment through a CO₂ laser system. The study considered the influence of an aggressive environment on geometrical deviations and mechanical performances of the bonded joints. Measurements showed that adherends were more susceptible to water uptake after ageing, allowing an increase in laminate thickness of about 2.5%. Lap shear tests were carried out and a decrease in obtained mechanical performances was observed only for the degreased specimens, confirming the effectiveness of the adopted laser texturing to reduce ageing effects. Moreover, the proposed laser texturing pre-treatment for bonding is a sustainable process too because reduces the volume of the removed material efficiently with reduced costs.

Variation management aims to reduce the effects of geometric variations of parts to assemble. Industry is facing a digital revolution in being able to manage geometrical deviation data along the entire lifecycle of a product. This requires the development of new assembly simulation models and tools. New design trends are orienting towards more and more lighter products. They are constituted by thin metal sheets or in composite material. Lighter products in composite materials are always joined through structural adhesive. This work presents different ways to model the adhesive inside a simulation that allows to predict the geometrical variations of an assembly constituted by compliant parts. Three different ways to model adhesive, i.e. glue contact, beam and shell elements, were taken into account. Different thickness values, different component constraints and application points of part variations were also considered. Two lighter assemblies were considered: one constituted by two metal plane sheets that are partially overlapped; the second is formed by three thin sheets in composite material, one plane and two L-shaped. The critical issues connected with the three different solutions to model adhesives in a simulation to manage geometric variations from component to assembly were underlined to guide a possible choice.