New Input Factors for Machine Learning Approaches to Predict the Weld Quality of Ultrasonically Welded Thermoplastic Composite Materials
Abstract
:1. Introduction
1.1. Quality Assurance in Ultrasonic Welding
1.2. Parameter Research
1.2.1. Thermography
1.2.2. Acoustic Signals
2. Materials and Methods
2.1. Experimental Setup
2.2. Data Processing and Analysis Steps
2.2.1. Acoustic Signals
2.2.2. Thermography
2.3. Machine Learning
- Duration of welding (number of measured data points of one parameter);
- Mean temperature of a thermal image;
- Peak amplitude of the FFT of the 2 kHz bin of data;
- Peak amplitude of the FFT of the 20 kHz bin of data.
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
TCs | thermoplastic composites |
UW | ultrasonic welding |
CUW | continuous ultrasonic welding |
SPW | spot welding |
ML | machine learning |
AI | artificial intelligence |
FEM | finite element model |
LSTM | long short-term memory |
RF | random forest |
ED | energy director |
NDT | non-destructive testing |
GFRP | glass-fiber-reinforced polymers |
CFRP | carbon-fiber-reinforced polymers |
AE | acoustic emission |
SE | sound emission |
DOE | design of experiment |
LSS | lab-shear-strength |
FFT | fast Fourier transformation |
ROI | region of interest |
KNN | k-nearest-neighbor |
RF | random forest |
SVM | support vector machine |
Appendix A. Spearman Correlation Coefficients of the Sound Data
Frequency Bin (kHz) | Correlation Coefficient | p-Value | Number of Samples |
---|---|---|---|
0.698330539 | 87 | ||
0.259039878 | 0.015404615 | 87 | |
−0.332253046 | 0.011567735 | 57 | |
0.4 | 0.22286835 | 11 | |
−0.067653277 | 0.666429017 | 43 | |
−0.047619048 | 0.910849169 | 8 | |
−0.225225225 | 0.180159238 | 37 | |
0.182237469 | 0.187200228 | 54 | |
0.238961039 | 0.296849183 | 21 | |
0.601170081 | 87 | ||
- | - | 0 | |
- | - | 0 |
Frequency Bin (kHz) | Correlation Coefficient | p-Value | Number of Samples |
---|---|---|---|
0.225452716 | 0.058705078 | 71 | |
0.376102418 | 0.000882821 | 75 | |
0.288141026 | 0.020949089 | 64 | |
−0.03 | 0.886801818 | 25 | |
−0.165722344 | 0.198001899 | 62 | |
−0.018181818 | 0.957685241 | 11 | |
0.197192513 | 0.271353653 | 33 | |
0.252613936 | 0.039169609 | 67 | |
−0.145894428 | 0.425599249 | 32 | |
0.699861486 | 87 | ||
- | - | 0 | |
−0.028571429 | 0.957154519 | 6 |
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Channel | Frequ. Bin (kHz) | Corr Coef | p-Value | Num. of Samples |
---|---|---|---|---|
0.698 | 87 | |||
0.259 | 0.015 | 87 | ||
−0.332 | 0.012 | 57 | ||
0.182 | 0.187 | 54 | ||
0.601 | 87 | |||
0.225 | 0.059 | 71 | ||
0.376 | 0.001 | 75 | ||
0.288 | 0.021 | 64 | ||
−0.166 | 0.198 | 62 | ||
0.253 | 0.039 | 67 | ||
0.7 | 87 |
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Görick, D.; Schuster, A.; Larsen, L.; Welsch, J.; Karrasch, T.; Kupke, M. New Input Factors for Machine Learning Approaches to Predict the Weld Quality of Ultrasonically Welded Thermoplastic Composite Materials. J. Manuf. Mater. Process. 2023, 7, 154. https://doi.org/10.3390/jmmp7050154
Görick D, Schuster A, Larsen L, Welsch J, Karrasch T, Kupke M. New Input Factors for Machine Learning Approaches to Predict the Weld Quality of Ultrasonically Welded Thermoplastic Composite Materials. Journal of Manufacturing and Materials Processing. 2023; 7(5):154. https://doi.org/10.3390/jmmp7050154
Chicago/Turabian StyleGörick, Dominik, Alfons Schuster, Lars Larsen, Jonas Welsch, Tobias Karrasch, and Michael Kupke. 2023. "New Input Factors for Machine Learning Approaches to Predict the Weld Quality of Ultrasonically Welded Thermoplastic Composite Materials" Journal of Manufacturing and Materials Processing 7, no. 5: 154. https://doi.org/10.3390/jmmp7050154