## 2 Motivation for Proposed Study and Related Works  
Precision drilling, which requires an accurate drilling process, ensures a high-quality product; however, it is essential to understand the condition and dynamic performance of a drill bit in a drilling process, especially for high-speed micro drilling, to reduce the risk of hole-location errors, reaming fractures, and drill fractures [6]. Although only a few studies have examined the dynamics of drilling processes that result in undesirable effects, such as chatter and drill breaks, most studies have shown that breakage is the most common cause of drill failure, and this is most often traced to excessive drilling forces in the drilling process, as well as drill bit bluntness [17; 18]. Cutting chip geometry and symmetry can significantly affect the cutting and dynamic properties of a drill bit. Even the tiniest variation in the complex geometry or symmetry of a cutting chip can significantly affect the cutting and dynamic properties of a drill bit. As the recent industry 4.0 revolution becomes more apparent, which interestingly features micro (nano) technological advancement (amongst many other technologies), PCB production requirements are prioritizing higher cost efficiency, which invariably implies that PCBs are becoming smaller in size. This also implies a reduction in hole sizes on these PCBs to accommodate for the numerous micro chips and electronic units [19]. Unlike the past PCB holes that featured a minimum diameter of \(\phi\)0.2 mm, recent PCBs feature more miniature holes that are (\(\phi\)0.15-\(\phi\)0.075 mm) in diameter and are in higher demand. Unfortunately, these micro drill bits are difficult to regind and are often scrapped after use. Therefore, it becomes imperative to devise reliable regrinding solutions that would minimize drill breakage, hole-location errors, reaming fractures, and drill fractures. Interestingly, the micro drill bit ARIS developed by Instron Co. Ltd., Seoul, Korea, offers a highly reliable (intelligent) solution for regrinding micro drill bits with high efficiency and durability [19]; however, amidst its efficiencies, due to the micro scale of these drill bits, the regrinding wheel surface of these high-precision regrinding systems should be monitored for wear. This is because there is a high positive correlation between the grinding wheel surface wear and poor cutting chip geometry and symmetry. On the one hand, vibration monitoring has, by far, been proven across diverse applications to be effective for condition monitoring applications [5; 12]. This is because most industrial equipment produces different ranges of mechanical vibrations during operation, which change as the equipment operating/health condition changes. On the other hand, the abundance of signal processing techniques provide conventional paradigms for understanding the spectral changes in the vibration signals using advanced computational methods, such as FFT, mel frequency cepstral coefficients, and wavelet transform [20]. Interestingly, the regrinding system's vulnerabilities to mechanical vibration offer an avenue for critically isolating unique (discriminative) frequency bands in the signals for accurate condition monitoring to ensure drill bit safety and retain acceptable drill bit efficiencieswhile achieving desired cost benefits. For instance, [PERSON] et al. [7] explored the efficiencies of the FFT algorithm for isolating (denoising) auditory signals from the grinding wheel of a G50150 Automatic Surface Grinder machine to enhance the accuracies of a deep learning-based diagnostic model for wear monitoring. By isolating the discriminative frequency bands between 300 and 500 Hz, they were able to provide intelligent diagnostic support to assist operators in determining whether the grinding wheel was worn or not.