# Intelligent Framework Design for Quality Control in Industry 4.0
## 3 Materials and Methods
Each of these conditions is recorded for two loads, 0 Nm and 80 Nm, resulting in a total of 12 classes. There are 75,000 data points for each class, which adds up to a total of 900,000 data points. Table 1 presents the data point count and statistical parameters (e.g., standard deviation, maximum, and minimum) for sensor 1 and sensor 2 for all 12 classes [33]. Figure 4: Methodology: The ANN model is pre-production quality control and the CNN is used for the post-production quality control system. The sensor data undergo data normalization, with missing values substituted with average values, and fault conditions are encoded using label encoding, which converts categorical labels into numerical format. Label encoding is performed in such a way that each of the 12 classes of the gear fault gets encoded with numeric values from 0 to 11. The sensor data are subjected to min-max normalization. Normalization scales data to a specific range; this is crucial for ensuring all the features in the data contribute equally to the model training process [34]. Normalization can reduce bias in the results as data with large-scale data samples can dominate the model, leading to biased results. Stability issues in a model can be reduced with normalization. An unstable model often leads to the problem of an exploding gradient [35]. Table 2 displays the final data frame following data cleaning and preprocessing. Figure 5 displays the data distributions for all 12 classes from sensor 1, with individual plots for each class labeled from a to l. The x-axis represents the number of recorded samples, while the y-axis represents the displacement of vibration in millimeters. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multirow{2}{*}{**Fault Type**} & \multirow{2}{*}{**Data Points**} & \multicolumn{3}{c}{**Sensor 1 Data**} & \multicolumn{3}{c}{**Sensor 2 Data**} \\ \cline{3-8}  & & & **Standard** & **Maximum** & **Minimum** & **Standard** & **Maximum** & **Minimum** \\  & & & **Deviation** & **Value** & **Value** (mm)** & **Deviation** & **Value (mm)** & **Value (mm)** \\ \hline
1 & Eccentricity @ 0 Nm & 75,000 & 0.004067 & 2.551224 & 2.490613 & 0.004356 & 2.456119 & 2.404050 \\ \hline
2 & Eccentricity @ 80 Nm & 75,000 & 0.007847 & 2.564200 & 2.467453 & 0.008053 & 2.486835 & 2.380726 \\ \hline
3 & Missing tooth @ 0 Nm & 75,000 & 0.004643 & 2.573398 & 2.471067 & 0.005034 & 2.496691 & 2.381219 \\ \hline
4 & Missing tooth @ 80 Nm & 75,000 & 0.010712 & 2.769193 & 2.250308 & 0.011348 & 2.674252 & 2.240452 \\ \hline
5 & No fault at @ 0 Nm & 75,000 & 0.004700 & 2.550567 & 2.488971 & 0.004902 & 2.462854 & 2.404050 \\ \hline
6 & No fault at @ 80 Nm & 75,000 & 0.010239 & 2.592781 & 2.451356 & 0.008373 & 2.493241 & 2.378755 \\ \hline
7 & Root crack @ 0 Nm & 75,000 & 0.004435 & 2.550402 & 2.487164 & 0.004382 & 2.458255 & 2.402408 \\ \hline
8 & Root crack @ 80 Nm & 74,999 & 0.009132 & 2.574055 & 2.465482 & 0.008644 & 2.485028 & 2.370542 \\ \hline
9 & Surface faults @ 0 Nm & 74,999 & 0.015707 & 2.820934 & 2.219428 & 0.015188 & 2.666696 & 2.205302 \\ \hline
10 & Surface faults @ 80 Nm & 75,000 & 0.025973 & 2.812228 & 2.243573 & 0.030520 & 2.709896 & 2.161939 \\ \hline
11 & Chipped tooth @ 0 Nm & 75,000 & 0.004680 & 2.551224 & 2.489299 & 0.004830 & 2.460554 & 2.391074 \\ \hline
12 & Chipped tooth @ 80 Nm & 75,000 & 0.010501 & 2.593273 & 2.459405 & 0.009774 & 2.517223 & 2.340319 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data statistics. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Index**} & \multicolumn{1}{c}{**Sensor 1**} & \multicolumn{1}{c}{**Sensor 2**} & \multirow{2}{*}{**Time (s)**} & \multirow{2}{*}{**Load Value**} & \multirow{2}{*}{**Gear Fault**} \\  & **(mm)** & & **(mm)** & & **(Nm)** & **(Label Encoded)** \\ \hline
0 & 0.4932 & 0.4922 & 0.9089 & 0 & 4.0 \\
1 & 0.4932 & 0.4919 & 0.1239 & 80 & 9.0 \\
2 & 0.5000 & 0.4916 & 0.7922 & 0 & 0.0 \\
3 & 0.5027 & 0.4982 & 0.9311 & 80 & 3.0 \\
4 & 0.5025 & 0.4907 & 0.8197 & 80 & 1.0 \\ \hline \hline \end{tabular}
\end{table}