## 1 Introduction  
Smart manufacturing has become the focus of most manufacturing companies due to product requirements, materials, technology, and data processing techniques [1]. Achieving the required product quality at a machining station is a daunting task due to the operating condition of the machine tool and the cutting tool condition. Tool wear/chipping directly impact the quality of the workpiece/product output from the machining station and the production cost. [PERSON], [PERSON] [2] researched the effect of the complexity of product output on the cutting tool and the cost of machining and, hence, optimized some turning parameters concerning the tool conditions and the machining cost. Besides optimizing the turning parameters and machining cost, estimating the tool condition before and during the machining operation can also provide a knowledge base for determining the workpiece/product quality output. Tool wear/chip measurements have been evaluated using different direct and indirect techniques. Whereas the former method tries to measure the threshold of the wear/chip from the cutting tool, the latter estimates the wear/chip threshold from the effect caused by it [3; 4; 5]. The direct measurement approach evaluates the chip size of the tool using different techniques and tools that can measure the chip size. The shortfall of this approach is that it requires intermittent stopping of the machining operation for the measurement to be taken. However, this limitation was avoided in the study by [PERSON], [PERSON] [6] using a computer vision-based system for measuring the dimension of an object in real-time during the machining process. On the other hand, the indirect method estimates the tool wear/chip condition by measuring the corresponding effect caused by the condition during machining [7]. Machine tool conditions during operation, such as the vibration of the tail-stock, the tool post, and the acoustics from the operation, may be evaluated to diagnose the condition of the tool and workpiece during operation. Furthermore, due to the obstructive nature of the machining operation, this method has been used by several research. In the current study, an integrated direct and indirect approach for tool condition monitoring was developed that is capable of non-obstructively evaluating the tool condition during machining. Smart machining manufacturing is superseding conventional methods in precision manufacturing and optimization of facility outlay, resulting in improved productivity. The concern in machining operation is the tool condition and the quality of the product output. While the former affects the latter, the specifications of the product quality finish determine whether or not the product will be accepted, re-worked, or scrapped. Damage to the cutting tool during the machining process may cause either an upper-bound or lower-bound approach. The former affects only the cutting tool whereas, in the latter, damage to the cutting tool directly affects the workpiece, causing it to be scrapped. This directly increases the cost of production as both the tool and workpiece are replaced. [PERSON], [PERSON] [8] investigated the effects of different machining parameters on surface roughness, tool wear, cutting temperature, and chip formation while turning Cu/Mo-SiCp composites. The result indicates that the most important parameter affecting surface roughness, tool wear, and the cutting temperature was the particle additive ratio. [PERSON], [PERSON] [9] also evaluated the tool wear, surface roughness, cutting temperature, and chip morphology using an Al/TiN-coated carbide cutting tool and found that optimization methods during the machining of particles play a significant role in extending the tool life considering the nature of the machining environment. Machining conditions and the machining environment significantly affect both the quality of the product output and the cutting tool wear at a machining station [10].