## 2 Materials and Methods  
The hypothesis supporting the research is that, from a sufficiently large number of experiments, data about tolerance defect can be related to machining process data directly obtained from the WEDM machine. Therefore, the first step is to carry out machining experiments and obtain large sets of data relating part tolerance and condition of ignition delay time (\(t_{d}\)), as already suggested in [10].  
For the experiments part material used in industry for disk turbine manufacturing has been selected, namely Inconel 718. This is a Nickel-based alloy with extremely good properties for the requirements of disk turbines. The thickness of the component is 20 mm. Machining is realized under industrial conditions on the WEDM machine ONA AV-35 using brass wire (CuZn37, diameter 0.25 mm), which is the industrial equipment for disc turbine production. Machine parameters for roughing and first trim cut are collected in Table 1.  
An on-line recording application has been implemented on the machine CNC in order to get as many data from the actual process as possible. The application allows collecting data every 5 ms. From the complete set of data, each single discharge has been classified as a function of ignition delay time, as shown in Table 2. The number of different discharge type will be recorded as one set of data.  
Experiments involve varying the actual distance between wire and workpiece, which can be used to define the final tolerance of the profile. After the roughing cut, in the process of 1 st trim cut, the workpiece is divided into 5 regions to be cut with different wire infeed values, as gathered in Table 3. The different aspect of the 5 regions can be seen in Figure 2. For every region, as many as 20,000 sets of data have been acquired with a sample ratio of 5 ms.  
\begin{table}
\begin{tabular}{c c c} \hline
**EDM Parameters** & **Roughing Cut** & **1 st Trim Cut** \\ \hline Current (A) & 5 & 5 \\ Off-time (\(\upmu\)s) & 7 & 6 \\ Wire tension (N) & 26 & 30 \\ Voltage (V) & 60 & 100 \\ Servo (V) & 30 & 81 \\ Offset (mm) & 0.185 & 0.170 \\ Wire speed (mm/min) & 10 & 8 \\ Wire tension (N) & 26 & 30 \\ Dielectric & Deionised water \\ \hline \end{tabular}
\end{table}
Table 1: Industrial settings for the roughing and first trim cut as taken from the ONA AV35 WEDM machine tables.  
\begin{table}
\begin{tabular}{c c} \hline
**Discharge Type** & \(t_{d}\)**Value (\(\upmu\)s)** \\ \hline Type 1 & \textless{}1 \\ Type 2 & 1–3 \\ Type 3 & 3–10 \\ Type 4 & \textgreater{}10 \\ \hline \end{tabular}
\end{table}
Table 2: Discharge discrimination as a function of ignition delay time \(t_{d}\).  
\begin{table}
\begin{tabular}{c c} \hline
**Region** & **Wire Infeed (\(\upmu\)m)** \\ \hline Region 1 & \(-16\) \\ Region 2 & \(-8\) \\ Region 3 & 0 \\ Region 4 & 8 \\ Region 5 & 16 \\ \hline \end{tabular}
\end{table}
Table 3: Wire infeed values during the experiment in the different regions of the workpiece.  
Once the data have been collected from the experiments, the deep learning neural network can be developed and trained. Python environment using Keras [11] and Tensorflow [12; 13] backend have been selected for developing the structure of the network. As many as 15000 sets of data are used as training data and another 5000 sets as testing data. The network includes two hidden dense layers, each with 64 units and Relu activation, and it ends with a single unit with no activation (a linear layer). This kind of linear layer is used for predicting a single continuous value [11].