Abstract
Unexpected drill breakage can be foreseen and prevented. We observed a factory and identified the warning signs of tool breakage for micro gun drills, as well as a laboratory experiment for micro drills. The vibrations of stable drilling and the vibrations that warn of tool breakage were analyzed based on the time and frequency domain features. We developed a prognostic model. We conducted physical drilling experiments on a Swiss turning machine and a laboratory research platform. Stainless steel was drilled with two types of 0.9-mm-diameter tools: 125-mm-long micro gun drills on Swiss turning machine and 25-mm-long micro drills. In both types of testing, two accelerometers were installed on the tool holder to collect two-directional vibration signals; a linear discriminant function processed the Z-axis and Y-axis signals for the telltale warning signs of impending tool breakage, and obtained a 100% classification rate. To confirm the effect of drilling disturbances on the prognostic system, the entries and exits of tools to and from workpieces were studied. The results demonstrate that both types of signal features can be used without causing any misclassification.
Highlights
The demand for microfeature fabrication on products has increased drastically, and various techniques have been proposed to achieve feature sizes between 1 μm to 1 mm [1,2,3]
The features of the signals corresponding to the conditions that lead to tool breakage are crucial for the development of the tool breakage prognostic system
A micro gun drilling experiment was conducted on a Swiss turning machine, as well as an evaluation experiment setup in the laboratory through simulated micro drilling
Summary
The demand for microfeature fabrication on products has increased drastically, and various techniques have been proposed to achieve feature sizes between 1 μm to 1 mm [1,2,3]. A number of studies related to condition monitoring of drilling tool have been reported lately with various sensors including drilling torque, cutting force, vibration, current /power, and acoustic emission signal [10,11,12,13,14]. Most of the studies reported the results for the tool wear life estimation in milling process and the cutting force collected from the dynamometer is well investigated due to its high sensitivity to tool wear conditions. Instead of adopting cutting fore for RUL estimation of tool, Kondo et al [28] investigated effectiveness of the thrust force, the motor current of spindle, and the AE signals from workpiece for the monitoring of the pre-failure phase and the detection of the tool breakage in drilling holes through a thin stainless-steel plate. The feature selection criterion, a cost function, is defined as follows:
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: The International Journal of Advanced Manufacturing Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
