## 4 Experimental Setup  
The images of the CNMG 120408/12, TNMG 160408/12, and VNMG 160408 turning inserts used in the turning application are captured using the DFK 33 GP006 GigE color camera with TCL 3520 5 MP lens. Initially, the top, side, and front views are considered for the classification. A processor with Intel i7 and 16 GB RAM was used to develop the classification model, and the models were implemented using R computer language with the Keras package using a TensorFlow backend. Examples of these pictures can be seen in Table 1. Table 1 shows that the top and side views do not clearly show the type of wear, but the wear is easily distinguishable in the front view images; therefore, only front view images were considered for the classification model. The setup for capturing the images of different views can be seen in Figure 4. The images are captured in standard room lighting without any dedicated light source. As we can see from Figure 2, the background of the insert has no impact on the feature extraction process. The process started with collecting the front-view images of the inserts for three different categories: 79 images of damaged inserts, 121 images of deformed inserts, and 128 images of abrasive wear inserts were captured. All the pictures were then resized to 200 (width) \(\times\) 200 (height). The model must be made robust against variation and transformation. One of the ways to do this is to use data augmentation, where the data are subjected to various transformations such as rotation, flipping, and shearing the images; this helps in improving the generalization error as the model is trained to be invariant to these transformations [40]. The training dataset was subjected to data augmentation, with an allowed rotation range of 10 degrees, width shift range of 20 percent, height shift range of 10 percent, and zooming range of 20 percent. The parameters for augmentation were chosen carefully so as not to alter the wear description of the images, but to accommodate for poor quality images that can be seen when the system is deployed in machine shops. An example of this data augmentation can be seen in Figure 5. It should be noted that the validation images were not subjected to data augmentation. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Wear Pattern** & **Top View** & **Side View** & **Front View** \\ \hline Damaged & & & \\ Deformation & & & \\ Abrasive & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Wear patterns and different views. Figure 4: Image capturing station. The augmented data were then fed to the neural networks. The images were first subjected to the image transformations of the convolution and max pooling layers, where the ROI was identified, and useful features were extracted from the images autonomously. The data in the final layers formed the last max pooling layer and formed the input to the densely connected layers. The images were converted to pixel data, and each pixel formed the input to the first FCL; the data were mapped to the labels of the pictures, and the output of the FCL was the prediction of wear type. The base model can now identify the nuanced differences in the cutting insert by identifying if the insert has deformation, normal wear, or damage. The knowledge developed in the base model can now be used to identify the change in quality by retaining the model. For the target model, the images of inserts are classified into GO/NO GO categories; for this part of the study, only CNMG 120408 turning inserts are used; one of the examples for GO/NO GO can be seen in Table 2. Table 2 also gives an example for different TCPs accessible in machine shops. The objective of the case study with respect to the target model is to prove that the model identifies the nuanced differences in changes in wear morphology and predicts the consequence of using a tool. That is, if the tool produces a GO quality part or NO GO quality part while using lesser images and training time and iterations so that the TCP deployment is fast-tracked.