Theoretical models of manufacturing processes provide a valuable insight into physical phenomena but their application to practical industrial situations is sometimes difficult. In the context of Industry 4.0, artificial intelligence techniques can provide efficient solutions to actual manufacturing problems when big data are available. Within the field of artificial intelligence, the use of deep learning is growing exponentially in solving many problems related to information and communication technologies (ICTs) but it still remains scarce or even rare in the field of manufacturing. In this work, deep learning is used to efficiently predict unexpected events in wire electrical discharge machining (WEDM), an advanced machining process largely used for aerospace components. The occurrence of an unexpected event, namely the change of thickness of the machined part, can be effectively predicted by recognizing hidden patterns from process signals. Based on WEDM experiments, different deep learning architectures were tested. By using a combination of a convolutional layer with gated recurrent units, thickness variation in the machined component could be predicted in 97.4% of cases, at least 2 mm in advance, which is extremely fast, acting before the process has degraded. New possibilities of deep learning for high-performance machine tools must be examined in the near future.

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.

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.

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).

In recent years, WEDM has been adopted in precision machining as an important alternative machining method because of its greater flexibility and lower wear cost compared with traditional machining, whereas wire breakage during WEDM is an unignorable problem that influences the machining quality and efficiency. Therefore, it has been a research topic of considerable interest. This work focuses on exploring the reason for the wire breakage. Firstly, by observing the wire after discharges and using the finite element method, a complete thermal model considering both latent heat and flushing efficiency was built. With this model, the simulation of the crater on the wire was finished. Then based on the result gotten from the simulation and experiment, the heat partition ratio to the wire was found as 46.74% by inverse fitting. After that, a wire breakage experiment was done and through locating the position of 50 discharges before wire breakage it is found that 34% of discharges were located within a range of 2 mm, and the minimum area in the wire cross-section only remained 41.88% compared with the original wire. This kind of decrease in the cross-section area made the stress higher than the UTS of the wire and caused the wire breakage. The findings of this work allow for a more in-depth understanding of the effect of discharge accumulation on wire breakage.