Prediction of Tool Forces in Manual Grinding Using Consumer-Grade Sensors and Machine Learning
Abstract
:1. Introduction
2. Materials and Methods
2.1. Experimental Study
2.2. Test Setup for Force Measurement and Data Loggers
2.3. Data Processing
- (a)
- Minimum, maximum, sum, abs sum, mean, median,
- (b)
- standard deviation, root mean square, interquartile range, skewness, several percentiles (5%, 20%, 80%, 95%),
- (c)
- peak2peak, peak2rms, margin factor, root sum of squares, absolute root mean,
- (d)
- zero-crossing rate, mean crossing rate,
- (e)
- mean frequency, median frequency,
- (f)
- value and frequency of the three highest peaks in amplitude spectrum,
- (g)
- spectral energy in 20 defined sections of the amplitude spectrum (e.g., 80–150 Hz, 350–500 Hz) and
- (h)
- several Daubechies wavelets (e.g., db2_Wavelets_approx, db2_Wavelets_shannon_Entropy3).
2.4. Regression
2.5. Gaussian Process Regression (GPR)
2.6. Machine Learning Framework
3. Results
4. Discussion
4.1. Discussion of the Results and Comparison of the Applications and Hand-Held Grinding Machines
4.2. Comparison of the Result with the State of the Research
4.3. Limitations and Further Research Directions
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
CV | cross validation |
GP | Gaussian process |
GPR | Gaussian process regression |
KJF | knee joint forces |
IMU | inertial measurement unit |
LOTO | leave-one-trial-out |
MAE | mean absolute error |
r | Pearson correlation coefficient |
R2 | coefficient of determination |
rMAE | relative mean absolute error |
SVM | support vector machine |
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Application | MAE [N] | rMAE [%] | r | MAE [N] | rMAE [%] | r | MAE [N] | rMAE [%] | r |
---|---|---|---|---|---|---|---|---|---|
Roughing with a roughing disc | |||||||||
2.83 (0.64) | 137.25 (67.77) | 0.47 (0.12) | 1.66 (0.43) | 20.91 (4.09) | 0.85 (0.04) | 2.15 (0.56) | 9.42 (2.31) | 0.91 (0.04) | |
Roughing with a fiber disc | |||||||||
2.64 (0.52) | 64.38 (42.52) | 0.48 (0.11) | 1.03 (0.21) | 11.11 (2.43) | 0.89 (0.05) | 1.80 (0.36) | 10.06 (1.57) | 0.80 (0.19) | |
Grinding with a flap disc | |||||||||
6.84 (1.77) | 108.93 (67.21) | 0.10 (0.19) | 2.28 (0.73) | 27.71 (16.37) | 0.59 (0.14) | 3.79 (1.67) | 25.34 (16.83) | 0.58 (0.16) | |
Cutting with a cutoff-wheel | |||||||||
3.14 (1.08) | 630.23 (888.75) | 0.09 (0.14) | 4.66 (2.99) | 24.03 (9.88) | 0.70 (0.05) |
Application | MAE [N] | rMAE [%] | r | MAE [N] | rMAE [%] | r | MAE [N] | rMAE [%] | r |
---|---|---|---|---|---|---|---|---|---|
Roughing with a roughing disc | |||||||||
2.63 (0.78) | 84.47 (16.62) | 0.17 (0.17) | 1.69 (0.40) | 23.27 (5.47) | 0.81 (0.06) | 2.01 (0.38) | 8.68 (1.67) | 0.85 (0.21) | |
Roughing with a fiber disc | |||||||||
2.94 (0.90) | 39.04 (7.08) | 0.58 (0.07) | 1.14 (0.25) | 12.32 (4.67) | 0.91 (0.07) | 1.66 (0.27) | 7.62 (1.01) | 0.94 (0.02) | |
Grinding with a flap disc | |||||||||
7.87 (0.99) | 88.06 (54.40) | 0.41 (0.11) | 1.67 (0.37) | 13.97 (4.40) | 0.83 (0.08) | 1.81 (0.40) | 7.52 (1.71) | 0.94 (0.02) | |
Cutting with a cutoff-wheel | |||||||||
4.62 (2.04) | 503.67 (532.58) | 0.16 (0.16) | 2.61 (0.42) | 15.31 (3.63) | 0.90 (0.04) |
Application | rMAE [%] | R2 | rMAE [%] | R2 | rMAE [%] | R2 | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 Hz | 10 Hz | 20 Hz | CV | CV | 1 Hz | 10 Hz | 20 Hz | CV | CV | 1 Hz | 10 Hz | 20 Hz | CV | CV | |
Roughing with a roughing disc | |||||||||||||||
131.60 (75.57) | 137.25 (67.77) | 136.87 (69.77) | 62.90 | 0.58 | 23.73 (3.10) | 20.91 (4.09) | 20.82 (3.58) | 15.38 | 0.81 | 12.79 (4.95) | 9.42 (2.31) | 9.41 (2.73) | 7.63 | 0.87 | |
Roughing with a fiber disc | |||||||||||||||
71.57 (51.55) | 64.38 (42.52) | 62.99 (40.15) | 50.66 | 0.26 | 14.27 (3.54) | 11.11 (2.43) | 12.62 (2.88) | 9.14 | 0.86 | 11.42 (1.03) | 10.06 (1.57) | 10.90 (2.41) | 11.85 | 0.69 | |
Grinding with a flap disc | |||||||||||||||
100.82 (69.40) | 108.93 (67.21) | 102.40 (64.12) | 75.31 | 0.24 | 80.61 (109.90) | 27.71 (16.37) | 71.59 (106.04) | 13.92 | 0.68 | 30.49 (24.28) | 25.34 (16.83) | 31.96 (12.95) | 10.91 | 0.71 | |
Cutting with a cutoff-wheel | |||||||||||||||
490.22 (517.88) | 630.23 (888.75) | 644.98 (886.18) | 624.30 | 0.24 | 19.94 (11.69) | 24.03 (9.88) | 23.34 (9.00) | 14.87 | 0.76 |
Application | rMAE [%] | R2 | rMAE [%] | R2 | rMAE [%] | R2 | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 Hz | 10 Hz | 20 Hz | CV | CV | 1 Hz | 10 Hz | 20 Hz | CV | CV | 1 Hz | 10 Hz | 20 Hz | CV | CV | |
Roughing with a roughing disc | |||||||||||||||
78.12 (32.19) | 84.47 (16.62) | 82.18 (20.77) | 52.03 | 0.71 | 24.73 (2.95) | 23.27 (5.47) | 23.09 (6.48) | 15.33 | 0.81 | 12.02 (1.49) | 8.68 (1.67) | 8.59 (1.11) | 7.67 | 0.87 | |
Roughing with a fiber disc | |||||||||||||||
38.99 (6.89) | 39.04 (7.08) | 40.05 (11.08) | 37.53 | 0.58 | 23.92 (11.36) | 12.32 (4.67) | 14.30 (5.94) | 7.97 | 0.89 | 12.03 (3.28) | 7.62 (1.01) | 8.37 (1.14) | 7.07 | 0.87 | |
Grinding with a flap disc | |||||||||||||||
83.18 (52.71) | 88.06 (54.40) | 95.59 (58.97) | 65.92 | 0.41 | 16.65 (4.80) | 13.97 (4.40) | 13.95 (4.12) | 13.49 | 0.71 | 9.23 (2.32) | 7.52 (1.71) | 8.10 (1.62) | 10.83 | 0.71 | |
Cutting with a cutoff-wheel | |||||||||||||||
715.05 (1161.6) | 503.67 (532.58) | 529.96 (557.62) | 553.79 | 0.36 | 19.83 (5.49) | 15.31 (3.63) | 19.26 (5.64) | 14.86 | 0.76 |
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Dörr, M.; Ott, L.; Matthiesen, S.; Gwosch, T. Prediction of Tool Forces in Manual Grinding Using Consumer-Grade Sensors and Machine Learning. Sensors 2021, 21, 7147. https://doi.org/10.3390/s21217147
Dörr M, Ott L, Matthiesen S, Gwosch T. Prediction of Tool Forces in Manual Grinding Using Consumer-Grade Sensors and Machine Learning. Sensors. 2021; 21(21):7147. https://doi.org/10.3390/s21217147
Chicago/Turabian StyleDörr, Matthias, Lorenz Ott, Sven Matthiesen, and Thomas Gwosch. 2021. "Prediction of Tool Forces in Manual Grinding Using Consumer-Grade Sensors and Machine Learning" Sensors 21, no. 21: 7147. https://doi.org/10.3390/s21217147
APA StyleDörr, M., Ott, L., Matthiesen, S., & Gwosch, T. (2021). Prediction of Tool Forces in Manual Grinding Using Consumer-Grade Sensors and Machine Learning. Sensors, 21(21), 7147. https://doi.org/10.3390/s21217147