In the current scenario of manufacturing competitiveness, it is a requirement that new technologies are implemented in order to overcome the challenges of achieving component accuracy, high quality, acceptable surface finish, an increase in the production rate, and enhanced product life with a reduced environmental impact. Along with these conventional challenges, the machining of newly developed smart materials, such as shape memory alloys, also require inputs of intelligent machining strategies. Wire electrical discharge machining (WEDM) is one of the non-traditional machining methods which is independent of the mechanical properties of the work sample and is best suited for machining nitinol shape memory alloys. Nano powder-mixed dielectric fluid for the WEDM process is one of the ways of improving the process capabilities. In the current study, Taguchi’s L16 orthogonal array was implemented to perform the experiments. Current, pulse-on time, pulse-off time, and nano-graphene powder concentration were selected as input process parameters, with material removal rate (MRR) and surface roughness (SR) as output machining characteristics for investigations. The heat transfer search (HTS) algorithm was implemented for obtaining optimal combinations of input parameters for MRR and SR. Single objective optimization showed a maximum MRR of 1.55 mm³/s, and minimum SR of 2.68 µm. The Pareto curve was generated which gives the optimal non-dominant solutions.

This paper presents the machining of Ti6Al4V Integral Blade Rotors (IBR) using CO₂ as cryogenic cooling. This kind of component is typical in gas turbines, pumps and other rotary machines. Flank milling technique using diamond polycrystalline (PCD) tools using CO₂ and Minimum Quantity Lubrication (MQL), denominated CryoMQL, is an alternative to conventional oil emulsions. The proposed approach implies a balance between technical and environmental issues, and it makes feasible the use PCD tools avoiding the high temperature reactivity of Ti6Al4V alloy with this type of cutting tools. CO₂ must be supplied and injected onto the cutting zone, avoiding the risks of dry ice formation and clogging of both pipes and nozzles. For this purpose, a new device for CO2 delivery was developed, using gas and liquid CO₂.

The diverse capabilities of nickel-based super-alloy (Udimet 720), like robust mechanical strength, ductility, resistance to excessive temperature deformation, and advanced corrosion and oxidation resistance, make it suitable for use in multiple applications. These super-alloys are identified as extremely difficult materials for machining to meet feature and manufacturing requirements. In the present work, we demonstrated the machining of Udimet 720 by employing the wire-electrical discharge machining (WEDM) technique. Pulse-on-time (T_on), pulse-off-time (T_off), current, and MWCNT amount were preferred as input variables. The effect of selected design variables was studied on material removal rate (MRR), surface roughness (SR), and recast layer thickness (RLT). Box-Behnken design was utilized to design an experimental matrix. For statistical analysis, analysis of variance (ANOVA) was employed. From ANOVA, the current had the highest contributor with 35.85% to affect MRR, while MWCNT amount was found to be the highest contributor for deciding the values of both SR and RLT with contributions of 42.66% and 40.07%, respectively. The addition of MWCNT at 1 g/L has substantially improved MRR from 0.8546 to 1.2199 g/min, SR reduced from 5.88 µm to 2.98 µm, and reduction in RLT from 17.8 to 11.61 µm. The passing vehicle search (PVS) algorithm was implemented, and the results of single-objective optimization presented the largest MRR of 1.8883 g/min, least SR of 1.89 µm, and least RLT of 9.70 µm. Additionally, a set of non-dominated solutions was obtained through Pareto optimal fronts. A small acceptable deviation was detected among the actual and forecasted results from PVS algorithm. It clearly reveals the acceptance of the PVS technique in the present study for Udimet 720. Lastly, the significance of MWCNT amount on surface textures was revealed by employing scanning electron microscopy (SEM).

Wire Electrical Discharge Machining (WEDM) is a popular non-conventional machining technology widely used in high-added value sectors such as aerospace, biomedicine, and the automotive industry. Even though the technology is now ready to meet the requirements of the most complex components, certain fundamental aspects related to the discharge process and gap conditions are not yet fully explained and understood. Combining single discharge experiments with numerical simulation represents a good approach for obtaining a deeper insight into the fundamentals of the process. In this paper, a fundamental study of the WEDM through single discharge experiments and numerical simulation is presented. WEDM single discharge experiments are described with the aim of identifying the relation between crater dimensions, discharge gap, and part surface roughness. A thermal transient numerical model of the WEDM process is presented, and correlation with actual industrial material removal rates (MRR) is analyzed. Results from single discharge WEDM experiments show that crater volume is as much as 40% lower when discharging on a rough surface than when the discharge occurs on a flat surface. The proposed thermal numerical model can predict actual removal rates of industrial machines with great accuracy for roughing cuts, deviations with experimental values being below 10%. However, lager deviations have been observed for other WEDM conditions, namely trim cuts, thus confirming the need for future research in this direction.

Traceability is a critical issue in the manufacturing of aerospace components. However, extracting understandable information from huge amounts of data from manufacturing processes may become a very difficult task. In this paper, a novel proposal for geometrical defect detection in the manufacturing of fir-tree slots for disk turbines using wire electrical discharge machining is presented. Useful data about the wire Electrical Discharge Machining (WEDM) process are collected every 5 ms and each single discharge is classified as a function of ignition delay time. Information from this large amount of data is extracted by using a deep neural network, which includes two hidden dense layers, each with 64 units and Relu activation, and it ends with a single unit with no activation. The average of the per-epoch absolute error (MAE) scores has been used to decide the optimum training situation for the deep learning network. Validation of the method has been carried out by machining a high-precision fir-tree slot for a disk turbine under industrial conditions. Results show that even though a strict tolerance band of ±5 µm has been applied, as many as 80% of the predictions from the network match the results of the conventional measuring method (coordinate measuring machine).

Manufacturing more efficient low pressure turbines has become a topic of primary importance for aerospace companies. Specifically, wire electrical discharge machining of disc turbine fir-tree slots has attracted increasing interest in recent years. However, important issues must be still addressed for optimum application of the WEDM process for fir-tree slot production. The current work presents a novel approach for tolerance monitoring based on unsupervised machine learning methods using distribution of ionization time as a variable. The need for time-consuming experiments to set-up threshold values of the monitoring signal is avoided by using K-means and hierarchical clustering. The developments have been tested in the WEDM of a generic fir-tree slot under industrial conditions. Results show that 100% of the zones classified into Clusters 1 and 2 are related to short-circuit situations. Further, 100% of the zones classified in Clusters 3 and 5 lie within the tolerance band of ±15 μm. Finally, the 9 regions classified in Cluster 4 correspond to situations in which the wire is moving too far away from the part surface. These results are strongly in accord with tolerance distribution as measured by a coordinate measuring machine.