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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.
Li-Yu Hsu; Ming-Chyuan Lu. Experimental Study of Vibration Signal for a Prognostic System to Prevent Tool Breakage in Micro Gun Drilling. 2021, 1 .
AMA StyleLi-Yu Hsu, Ming-Chyuan Lu. Experimental Study of Vibration Signal for a Prognostic System to Prevent Tool Breakage in Micro Gun Drilling. . 2021; ():1.
Chicago/Turabian StyleLi-Yu Hsu; Ming-Chyuan Lu. 2021. "Experimental Study of Vibration Signal for a Prognostic System to Prevent Tool Breakage in Micro Gun Drilling." , no. : 1.
In this study, the correlation between welding quality and features of acoustic emission (AE) signals collected during laser microwelding of stainless-steel sheets was analyzed. The performance of selected AE features for detecting low joint bonding strength was tested using a developed monitoring system. To obtain the AE signal for analysis and develop the monitoring system, lap welding experiments were conducted on a laser microwelding platform with an attached AE sensor. A gap between the two layers of stainless-steel sheets was simulated using clamp force, a pressing bar, and a thin piece of paper. After the collection of raw signals from the AE sensor, the correlations of welding quality with the time and frequency domain features of the AE signals were analyzed by segmenting the signals into ten 1 ms intervals. After selection of appropriate AE signal features based on a scatter index, a hidden Markov model (HMM) classifier was employed to evaluate the performance of the selected features. Three AE signal features, namely the root mean square (RMS) of the AE signal, gradient of the first 1 ms of AE signals, and 300 kHz frequency feature, were closely related to the quality variation caused by the gap between the two layers of stainless-steel sheets. Classification accuracy of 100% was obtained using the HMM classifier with the gradient of the signal from the first 1 ms interval and with the combination of the 300 kHz frequency domain signal and the RMS of the signal from the first 1 ms interval.
Ming-Chyuan Lu; Shean-Juinn Chiou; Bo-Si Kuo; Ming-Zong Chen. Analysis of Acoustic Emission (AE) Signals for Quality Monitoring of Laser Lap Microwelding. Applied Sciences 2021, 11, 7045 .
AMA StyleMing-Chyuan Lu, Shean-Juinn Chiou, Bo-Si Kuo, Ming-Zong Chen. Analysis of Acoustic Emission (AE) Signals for Quality Monitoring of Laser Lap Microwelding. Applied Sciences. 2021; 11 (15):7045.
Chicago/Turabian StyleMing-Chyuan Lu; Shean-Juinn Chiou; Bo-Si Kuo; Ming-Zong Chen. 2021. "Analysis of Acoustic Emission (AE) Signals for Quality Monitoring of Laser Lap Microwelding." Applied Sciences 11, no. 15: 7045.
This study focused on correlation analysis between welding quality and sound-signal features collected during microlaser welding. The study provides promising features for developing a monitoring system that detects low joint strength caused by a gap between metal sheets after welding. To obtain sound signals for signal analysis and develop the monitoring system, experiments for laser microlap welding were conducted on a laser microwelding platform by installing a microelectromechanical system (MEMS) microphone away from the welding point, and an acoustic emission (AE) sensor on the fixture. The gap between two metal sheet layers was controlled using clamp force, a pressing bar, and the appropriate installation of a thin piece of paper between the metal sheets. After sound signals from the microphone were collected, the correlation between features of time-domain sound signals and of welding quality was analyzed by categorizing the referred signals into eight sections during welding. After appropriately generating the features after signal analysis and selecting the most promising features for low-joint-strength monitoring on the basis of scatter index J, a hidden Markov model (HMM)-based classifier was applied to evaluate the performance of the selected sound-signal features. Results revealed that three sound-signal features were closely related to joint-strength variation caused by the gap between two metal-sheet layers: (1) the root-mean-square (RMS) value of the first section of sound signals, (2) the standard deviation of the first section of sound signals, and (3) the standard deviation to the RMS ratio of the second section of sound signals. In system evaluation, a 100% classification rate was obtained for normal and low-bonding-strength monitoring when the HMM-based classifier was developed on the basis of the three selected features.
Bo-Si Kuo; Ming-Chyuan Lu. Analysis of a Sound Signal for Quality Monitoring in Laser Microlap Welding. Applied Sciences 2020, 10, 1934 .
AMA StyleBo-Si Kuo, Ming-Chyuan Lu. Analysis of a Sound Signal for Quality Monitoring in Laser Microlap Welding. Applied Sciences. 2020; 10 (6):1934.
Chicago/Turabian StyleBo-Si Kuo; Ming-Chyuan Lu. 2020. "Analysis of a Sound Signal for Quality Monitoring in Laser Microlap Welding." Applied Sciences 10, no. 6: 1934.
A model for the relation between the acoustic emission signal generation and tool wear was established for cutting processes in micromilling by considering the acoustic emission (AE) generation and propagation mechanisms. In addition, the effect of tool wear on the AE signal generation in frequency and amplitude was studied. In the model development, the finite element analysis was first used to calculate the shear strain rate distribution on the shear plane based on the orthogonal cutting assumption. Conversely, the contact stress distribution of workpiece on the flank wear face was established based on the Waldorf model. Following the finite element method, the dislocation density in materials was calculated based on Orowan’s law with the calculated stress rate. Finally, the AE signal detected by the sensor was calculated by considering the Gaussian probability density function for the distribution of AE source on the shear plane and the one-dimension wave equation for AE signal propagation. Based on the developed model, the effect of tool wear on the AE signal generation was investigated and compared to the experimental results. The results obtained from these investigations indicate that the proposed model can be used to predict the effect of tool wear on the AE signal generation.
Chien-Wei Hung; Ming-Chyuan Lu. Model development for tool wear effect on AE signal generation in micromilling. The International Journal of Advanced Manufacturing Technology 2012, 66, 1845 -1858.
AMA StyleChien-Wei Hung, Ming-Chyuan Lu. Model development for tool wear effect on AE signal generation in micromilling. The International Journal of Advanced Manufacturing Technology. 2012; 66 (9):1845-1858.
Chicago/Turabian StyleChien-Wei Hung; Ming-Chyuan Lu. 2012. "Model development for tool wear effect on AE signal generation in micromilling." The International Journal of Advanced Manufacturing Technology 66, no. 9: 1845-1858.
This study analyzed the sound signals obtained from the micromilling process for microtool wear monitoring. Various spans of spectral features were created by analyzing sound signals on tool wear monitoring in microcutting. The selection algorithm based on class mean scattering criteria and the hidden Markov model (HMM) model was developed to verify the effect of various feature selection algorithms on the system performance. The effect of the feature bandwidth size, the size of observation sequence, and choice of the hidden states for HMM parameters were also studied. The results indicate that the normalized sound signals obtained from the single microphone with a frequency range between 20 and 80 kHz demonstrated the potential to provide a solution to monitor micromills with the proper selection of feature bandwidth and other parameters.
Ming-Chyuan Lu; Bing-Syun Wan. Study of high-frequency sound signals for tool wear monitoring in micromilling. The International Journal of Advanced Manufacturing Technology 2012, 66, 1785 -1792.
AMA StyleMing-Chyuan Lu, Bing-Syun Wan. Study of high-frequency sound signals for tool wear monitoring in micromilling. The International Journal of Advanced Manufacturing Technology. 2012; 66 ():1785-1792.
Chicago/Turabian StyleMing-Chyuan Lu; Bing-Syun Wan. 2012. "Study of high-frequency sound signals for tool wear monitoring in micromilling." The International Journal of Advanced Manufacturing Technology 66, no. : 1785-1792.
This study develops a micro-tool condition monitoring system consisting of accelerometers on the spindle, a data acquisition and signal transformation module, and a backpropagation neural network. This study also discusses the effect of the sensor installations, selected features, and the bandwidth size of the features on the classification rate. To collect the vibration signals necessary for training the system model and verifying the system, an experiment was implemented on a micro-milling research platform along with a 700 μm diameter micro-end mill and a SK2 workpiece. A three-axis accelerometer was installed on a sensor plate attached to the spindle housing to collect vibration signals in three directions during cutting. The frequency domain features representing changes in tool wear were selected based on the class mean scatter criteria after transforming signals from the time domain to the frequency domain by fast Fourier transform. Using the appropriate vibration features, this study develops and tests a backpropagation neural network classifier. Results show that proper feature extraction for classification provides a better solution than applying all spectral features into the classifier. Selecting five features for classification provides a better classification rate than the case with four and three features along with the 30 Hz bandwidth size of the spectral feature. Moreover, combining the signals for tool condition from both direction signals provides a better classification rate than determining the tool condition using a one-direction single sensor.
Wan-Hao Hsieh; Ming-Chyuan Lu; Shean-Juinn Chiou. Application of backpropagation neural network for spindle vibration-based tool wear monitoring in micro-milling. The International Journal of Advanced Manufacturing Technology 2011, 61, 53 -61.
AMA StyleWan-Hao Hsieh, Ming-Chyuan Lu, Shean-Juinn Chiou. Application of backpropagation neural network for spindle vibration-based tool wear monitoring in micro-milling. The International Journal of Advanced Manufacturing Technology. 2011; 61 (1-4):53-61.
Chicago/Turabian StyleWan-Hao Hsieh; Ming-Chyuan Lu; Shean-Juinn Chiou. 2011. "Application of backpropagation neural network for spindle vibration-based tool wear monitoring in micro-milling." The International Journal of Advanced Manufacturing Technology 61, no. 1-4: 53-61.