Experimental Study of Vibration Signal for a Prognostic System to Prevent Tool Breakage in Micro Gun Drilling

Unexpected drill breakage can be foreseen and prevented. We observed a factory and identified the warning signs of tool breakage for micro gundrills, 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 gundrills 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.

Prediction of Drill Bit Breakage Using an Infrared Sensor

In this paper, a novel drill bit breakage prediction method featuring a low-cost commercial infrared sensor to monitor drill bit corner wear is proposed. In the proposed method, the drill bit outer corner wear state can be monitored by measuring reflected infrared light because the reflection phenomenon is influenced by wear, edge shape, and surface roughness of the drill bit. In the experiments, a titanium workpiece was drilled without using cutting fluid to accelerate drill bit fracture. After drilling a hole in the workpiece, reflected infrared light was measured for the drill bit rotating at 100 rpm. Collected data on intensity of infrared light reflected from the circumferential surface of the drill bit versus the rotation angle of the drill bit were considered to predict tool breakage; two significant positions to predict tool breakage were found from the reflected infrared light graphs. By defining gradient vectors from the slopes of the reflected infrared light curves, a reliable criterion for determining drill bit breakage could be established. The proposed method offers possibilities for new measurement and analysis methods that have not been used in conventional tool wear and damage studies. The advantage of the proposed method is that the measurement device is easy to install and the measured signal is resistant to electromagnetic noise and ambient temperature because optical fiber is used as the signal transmission medium. It also eliminates the need for complex analysis of the measured signal, eliminating the need for a high-performance analyzer and reducing analysis time.

Digitized Stress Function-Based Feed Rate Scheduling for Prevention of Mesoscale Tool Breakage during Milling Hardened Steel

In this article, a digitized stress function-based feed rate scheduling algorithm is formulated for the prevention of tool breakage while having an optimum material removal rate in mesoscale rough milling of hardened steel. Instead of setting limits to the cutting forces and material removal rates, the presented method regulates the tool’s stresses. A 3D coupled Eulerian-Lagrangian finite element method (FEM) model is used to simulate a 3D chip flow-based stress according to the mesoscale tool’s rotation during cutting of hardened steel. Maximum uncut chip thickness and tool engaging angle of the uncut chip is identified as the fundamental driving factors of tool breakage in down milling configuration. Furthermore, a multiple linear regression model is formed to digitize the stress with two major factors for digitized feed scheduling. The optimum feed rates for each segment along the tool path can be obtained through finite element models and a multiple linear regression model. The feed rate scheduling method is validated through cutting experiments with tool paths of linear and arc segments. In a series of experimental validations, the algorithm demonstrated the capability of reducing the machining time while eliminating cutting tool breakages.

Tool Life Stage Prediction in Micro-Milling From Force Signal Analysis Using Machine Learning Methods

Tool condition monitoring is difficult in micro-milling due to irregular wear and chipping of the cutting edges, which lead to unexpected tool breakage. This study demonstrates the use of force data to reliably predict different tool life stages until tool breakage, while micro-milling hard materials like stainless steel (SS304) using tungsten carbide tools of 500 μm diameter. Extensive experiments involving machining of 465 slots over 62 min of machining time were performed in this study. The resulting voluminous force data were analyzed to divide the tool life into three stages based on the variation in the forces and other related features. The first stage is the initial 12.5% of the tool life, second stage consists of 12.5–70% of tool life, and the third stage is from 70% to 100% tool life. The analysis of the tool wear and cutting forces shows that the average tool diameter reduces by 32 μm, 67 μm and 108 μm, and the average resultant cutting force were 2.45 N, 4.17 N, and 4.93 N in stage 1, 2, and 3, respectively. To avoid catastrophic breakage of the tool, the tool life stages are predicted from the force data using machine learning models. Among the machine learning models, random forest method gave a better prediction accuracy of 88.5%. The model was further improved by incorporating the initial cutting edge radius as an additional feature, and the variance in the prediction was seen to drop by 48.76%.