## 1 Introduction  
The last decade has been the decade of the fourth industrial revolution (Industry 4.0) for manufacturing around the world using smart systems and technology. Industry 4.0 uses the concepts of machine learning and big data to reduce wastage of time and resources and make the production process more efficient [1, 2]. The concepts of Industry 4.0 require machines that are smart and autonomous [3], and this presents an excellent opportunity for the development of machine learning algorithms that improve operations and help in the reduction of waste. Smart systems are essential for the implementation of Industry 4.0 in machine shops, but what is the definition of a smart machine in the context of machining quality control? There is no clear definition proposed in past studies. A smarter machine in the context of machining quality control can be envisioned as a machine with the intelligence to understand and implement the quality requirements. This machine only produces the parts that meet the design requirements(i.e., conforming parts) and detects the changes in machining parameters or environmental factors. These changes in environmental factors might result in the manufacturing of parts that do not meet the design requirements (i.e., non-conforming parts). In other words, the machine has the intelligence to predict whether the machine will produce a conforming or a non-conforming part based on the environmental inputs. In a mass manufacturing facility, the environmental factors like type of machine used, jigs, and fixtures in which the machining is taking place are reasonably stable, and these are changed only when the production lines are repurposed to produce different components. Given the stable environmental factors, the quality of the machining largely depends on the consumables like cutting tools, coolant oils, and others used by these machining processes [4; 5]. If the consumables are used for too long, they contribute to the production of non-conforming parts, and if the consumables are underutilized, they add to the overheads and wastage [6]. The process of defining the limits for an overused and an underused tool can be termed a tool change policy (TCP) and is illustrated in Section 4. In an intelligent tool condition monitoring (TCM) system, the definition and implementation of a TCP should be carried out autonomously and independently. TCM is one of the classical problems of manufacturing, and it has been extensively studied in the last four decades [7; 8]. However, three barriers have been identified by the presented study that challenge the deployment of existing solutions in machine shops around the world. First, the inflexibility of the systems to accommodate different TCPs: tool condition affects different aspects of machining like surface finish [9] and dimensional accuracy [10; 11]. For example, a TCP for one tool is when the chatter marks start to appear, while for another tool TCP it is related to a burr on the edge. The existing studies fail to provide flexibility to accommodate different TCPs. Quantification of tool wear ignores the concept of a TCP and diverts the attention to the quantification of wear on inserts, and this is the second identified challenge for deployment of current TCM systems. The studies try to quantify the wear on inserts in terms of millimeters of flank wear [12; 13; 14; 15]. This quantification provides no information about the usability of the tool. In machine shops around the world, quality management is not seen as a process that directly adds value to the component, and from an economic point this process must be limited to what is absolutely necessary [16]; that is why the manufacturers are interested to know whether components meet the design requirements (a conforming part) or do not meet design requirements (a non-conforming part). One of the examples for this qualitative approach is GO (conforming part)/NO GO (non-conforming part) gauges [17], which are discussed in Section 3.