## 1 Introduction
Many machining stations have resulted in a just-in-time maintenance policy that replaces the cutting tool at a specified time before the end of its useful life [11]. The shortfall of this approach is that the cutting tool is under-utilized, which also increases the running cost during machining. This has encouraged many research approaches to adopt smart manufacturing, because of the possibility of incorporating product quality requirements and manufacturing conditions into the manufacturing system, using fourth industrial revolution technologies such as smart sensors, smart IoT devices, cloud computing, and machine learning (ML) techniques [12]. Data processing and analysis for decision makingare essential for smart machining. Most approaches have adopted parameters from the tool, workpiece, and machine tool for decision making at the machining station. Several studies have shown that acoustic and vibration signals can be used for intelligent decision making using machine learning techniques. Although a vibration signal may accurately reflect the condition of the tool during operation, much effort is required to extract the significant component of the signal relating to the tool and workpiece condition during operation. Cutting tool failure is a complex phenomenon that requires painstaking study, as the effect impacts the productivity, in diverse ways, of a machining station. Tool cracking/fracture (chipping) happens when a small fragment breaks from the cutting [13]. Although this condition may occur during the cutting operation, it may also happen during any phase of the tool's useful life. Tool wear, on the other hand, is the gradual change in the geometry of the cutting tool due to the progressive removal of materials [14]. Even though this condition happens gradually over a period during the useful life of the tool, it affects and determines the quality of the product/workpiece at a machining station. Another tool condition that affects the workpiece/product quality during the machining process is tool breakage. This happens when the cutting edge of the cutting tool breaks off [15]. Gradual or sudden deterioration of the cutting tool condition often results in the highlighted tool failures or conditions depending on the operating conditions. These failures are mainly due to two (2) mechanisms, which are the abrasion/friction between the interface of the tool and workpiece, and the adhesion due to the plastic deformation of the workpiece material [16]. This makes the unified study of both the workpiece and tool condition compelling. [PERSON] and [PERSON] [17] carried out a study on the effect of the operating parameters on the cutting tool wear, considering cutting velocity, feed rate, and depth of cut, and found that tool wear was found to increase with increased feed rate and depth of cut. A similar result was shown by [PERSON] [18], i.e., that cutting speed and depth of cut have significant impacts on principal and flank wear, with the latter lower than the standard limit of 0.2 mm with the maximum surface roughness of 0.99 \(\upmu\)m. These studies showed that the operating conditions of the machine tool, such as the depth of cut, feed rate, and cutting speed, have a significant effect on the tool wear condition during machining. A comparative study was also carried out on tool faults using vibration and cutting force signals to classify the conditions of the tool as healthy, worn flank, broken insert, and extended tool [19]. This study indicated that the conditions of the machine tool, such as vibration and acoustics, could influence or indicate the tool condition and be used as a factor for evaluating the tool condition. A similar study was carried out on the effect of vibration and cutting zone temperature on the surface roughness of workpiece and tool wear in an eco-friendly minimum quantity lubrication (MQL) at a constant cutting depth and feed rate. The results showed that, at a reduced vibration signal and temperature, there was also reduced tool wear and surface roughness [20].