Exploring the Efficiencies of Spectral Isolation for Intelligent Wear Monitoring of Micro Drill Bit Automatic Regrinding In-Line Systems
Abstract
:1. Introduction
- A multi-sensor vibration monitoring system is proposed for the automatic micro drill bit regrinding of in-line equipment. The proposed framework adopts spectral isolation by integrating the low-frequency vibration responses from the regrinding frame and the high-frequency vibration responses from the gas bearing-powered regrinding spindle in a comprehensive manner. These provide highly discriminate information from the regrinding frame and the regrinding spindle, respectively, for improved condition monitoring.
- A multi-option diagnostic framework that exploits the multiple sensor data’s vulnerabilities is proposed. The framework offers the options of choosing different data sources—stand-alone and/or integrated sensor data and exploits different 1D-CNN and MLP model architectures. This presents an avenue for evaluating the efficiencies of spectral isolation for improved tool wear monitoring and for assessing the computational cost implications of employing the proposed intelligent monitoring technology.
- For the proposed study, we leverage experimental data from an ultra-precision micro drill bit automatic regrinding in-line system (ARIS), which regrinds micro drill bits (– mm) used in the PCB manufacturing process. Empirical and descriptive conclusions are drawn following extensive investigations and evaluations. Our research offers a reliable framework for future research and practice in real-time industrial monitoring/diagnostic applications.
2. Motivation for Proposed Study and Related Works
3. Background of Study
3.1. Working Principle of the G50150 Micro Drill Bit ARIS
3.2. Proposed Intelligent Monitoring Framework
3.2.1. FFT-Based Spectral Isolation
3.2.2. Standard DL-Based Diagnostic/Classification Models
4. Experimental Assessment
4.1. Data Acquisition and Spectral Isolation
4.2. DL-Based Diagnostic Assessments
5. Conclusions and Future Works
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Model | Architecture | Hyperparameters/Description |
---|---|---|
CNN64 | Conv1D, GlobalAveragePooling1D, Dense_output | Filter1 = 64, kernel1_size = 3, activation_Conv1D = ReLU, activation_Dense_output = Softmax, optimizer = adam, Loss = categorical_crossentropy |
CNN64_64 | Conv1D—Conv1D, GlobalAveragePooling1D, Dense_output | Filter1 = Filter2 = 64, kernel1_size = 8, kernel2_size = 5, activation_Conv1D = ReLU, activation_Dense_output = Softmax, optimizer = adam, Loss = categorical_crossentropy |
CNN64_Dense100 | Conv1D, GlobalAveragePooling1D, Dense_100, Dense_output | Filter1 = 64, activation_Conv1D = ReLU, kernel1_size = 3, activation_Dense_100 = ReLU, activation_Dense_output = Softmax, optimizer = adam, Loss = categorical_crossentropy |
FCN [33] | Conv1D+Batch_Norm, Conv1D+Batch_Norm, Conv1D+Batch_Norm, GlobalAveragePooling1D, Dense_output | Filter1 = 128, Filter2 = 256, Filter3 = 128, activation_Conv1D = ReLU, kernel1_size = 8, kernel2_size = 5, kernel3_size = 3, activation_Dense_output = Softmax, optimizer = adam, Loss = categorical_crossentropy |
DNN64 | Dense_64—Dense_output | MLP: nodes in Dense_64 = 64, activation_Dense_64 = ReLU, activation_Dense_output = Softmax, optimizer = adam, Loss = categorical_crossentropy |
DNN128_64 | Dense_128—Dense_64—Dense_output | MLP: nodes in Dense_128 = 128, nodes in Dense_64 = 64, activation_Dense_128 = activation_Dense_64 = ReLU, activation_Dense_output = Softmax, optimizer = adam, Loss = categorical_crossentropy |
DNN100_150_50 | Dense_100—Dense_150—Dense_50—Dense_output | MLP: nodes in Dense_100 = 100, nodes in Dense_150 = 150, nodes in Dense_50 = 50, activation_Dense_100 = activation_Dense_150 = activation_Dense_50 = ReLU, activation_Dense_output = Softmax, optimizer = adam, Loss = categorical_crossentropy |
Spindle Only | Frame Only | Spindle + Frame | ||||||
---|---|---|---|---|---|---|---|---|
Parameter | ||||||||
Dimension | () × 4 | () × 4 | () × 3 | () × 3 | () × 7 | () × 7 | () × 7 | () × 7 |
CNN64 | 90.2% | 97.8% | 85.4% | 88.9% | 92.1% | 98.0% | 84.2% | 88.3% |
CNN64_64 | 92.1% | 97.6% | 88.1% | 90.0% | 92.5% | 98.7% | 88.9% | 94.7% |
CNN64_Dense100 | 93.6% | 98.2% | 88.6% | 91.2% | 92.2% | 97.8% | 90.1% | 95.1% |
FCN | 96.0% | 99.3% | 91.1% | 95.5% | 95.7% | 98.8% | 92.3% | 97.6% |
DNN64 | 73.8% | 85.6% | 56.2% | 75.8% | 65.8% | 87.2% | 74.7% | 83.7% |
DNN128_64 | 88.7% | 91.6% | 82.6% | 88.7% | 90.3% | 93.1% | 90.6% | 94.2% |
DNN100_150_50 | 89.1% | 92.1% | 84.0% | 90.1% | 91.2% | 94.0% | 92.4% | 93.0% |
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Akpudo, U.E.; Hur, J.-W. Exploring the Efficiencies of Spectral Isolation for Intelligent Wear Monitoring of Micro Drill Bit Automatic Regrinding In-Line Systems. Algorithms 2022, 15, 194. https://doi.org/10.3390/a15060194
Akpudo UE, Hur J-W. Exploring the Efficiencies of Spectral Isolation for Intelligent Wear Monitoring of Micro Drill Bit Automatic Regrinding In-Line Systems. Algorithms. 2022; 15(6):194. https://doi.org/10.3390/a15060194
Chicago/Turabian StyleAkpudo, Ugochukwu Ejike, and Jang-Wook Hur. 2022. "Exploring the Efficiencies of Spectral Isolation for Intelligent Wear Monitoring of Micro Drill Bit Automatic Regrinding In-Line Systems" Algorithms 15, no. 6: 194. https://doi.org/10.3390/a15060194
APA StyleAkpudo, U. E., & Hur, J. -W. (2022). Exploring the Efficiencies of Spectral Isolation for Intelligent Wear Monitoring of Micro Drill Bit Automatic Regrinding In-Line Systems. Algorithms, 15(6), 194. https://doi.org/10.3390/a15060194