## 2 Literature Review  
Tool condition monitoring methods are classified into direct and indirect methods [18]; direct methods mostly involve the use of computer vision [14, 15], radiation [19], and electrical resistance [20], whereas indirect methods involve online monitoring methods that use vibration [21, 22, 23], force [22], and temperature and sound [24] signals. Indirect methods are less complicated and can be implemented straightforwardly and monitored in real-time [10], but they are prone to making noise and are less accurate than the direct methods [25]. Real-time monitoring is also not a crippling disadvantage for direct systems as there is enough time in between machining operation and cycles [26] to get the required data without disturbing the sequence of operations of a machine shop. In addition, the unidirectional execution of existing G-code-based systems does not allow for real-time changes in the machining parameters [27, 28, 29]; therefore, there is no way to integrate the response generated by indirect systems in real-time. Considering direct methods are more accurate systems, the study adopts the direct monitoring methodology. Vision-based systems are the most popular systems when it comes to direct tool condition monitoring. Vision systems have also improved in recent years and are being used in different facets of machining like collision avoidance [2, 30], which also demonstrates the ability of vision systems to detect changes while maintaining distance from the cutting process. Computer vision systems are used to monitor changes in the wear morphologies of an insert. Wear morphology classification has been the subject of many studies in past years; [PERSON] [31] employed vision systems for wear morphology classification using quantitative definitions of different wear patterns. The conventional tool condition monitoring used in machine shops involves the quantitative approach. For autonomous systems, the quantitative approach proves difficult for implementation considering the variety of qualitative parameters that need to be hardcoded into the system to identify a variety of wear morphologies. The hard coding of parameters is also computationally expensive; thus, there is a need for a system that identifies the features of different types of wear. The need for identification of different morphologies is satisfied to an extent using CNN by [PERSON] et al. [15]; this study has inspired the base wear classification model discussed in Section 3.1. Autonomously detecting damage to the inserts before they are used in machining is one of the essential requirements for making tool monitoring autonomous. [PERSON] et al. [32] developed a vision-based system to detect broken inserts in milling cutters automatically. [PERSON] et al. [14] used image processing and image segmentation techniques to develop a system that could identify built-up edges, fractures, and other insert deformations. These studies used image processing techniques, which require human intervention to develop feature descriptions; this limits the independent implementation of these systems for a variety of wear morphologies. As opposed to image processing techniques, the CNN approach learns to identify the region of interest (ROI) and features descriptions to identify different wear morphologies, and this eliminates the need for the human feature descriptions step needed in other techniques [33]. Considering the utility of autonomous feature extraction, the proposed study uses the CNN approach for tool condition monitoring. Even though tool condition monitoring is one of the classical problems, there are fewer publications in the context of the correlation of tool condition with the quality of the component. [PERSON] and [PERSON] [34] developed a system that correlates tool condition and production quality. The study also developed a multi-level categorization of the wear using a support vector machine methodology.