Application of an Artificial Neural Network in the Modelling of Heat Curing Effects on the Strength of Adhesive Joints at Elevated Temperature with Imprecise Adhesive Mix Ratios
Abstract
:1. Introduction
2. Materials and Methods
2.1. Materials and Sample Preparation
2.2. Mechanical Testing
2.3. Statistical Analysis
- The Shapiro–Wilk W-test (for the normality of distribution in the produced series of resultant joint strength test values);
- The tests by Fisher, Levene, and Brown–Forsythe (for the equality of group variances); and
- Student’s t-test (for the results characterised by normality of distribution and equality of variances to analyse the equality of the average resultant joint strength values at the adopted significance level).
2.4. Long Short-Term Memory Artificial Recurrent Neural Network
3. Results and Discussion
3.1. Mechanical Testing
3.2. LSTM Forecast Adhesive Joint Strength Values at Elevated Temperature
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Adhesive Component A/B Mix Ratio (Resin/Hardener) | Curing Conditions | Test Temperature | ||
---|---|---|---|---|
1.4:1/1:0.71 | x | 3 days/25 °C | x | 25 °C |
1.8:1/1:0.55 | ||||
2.0:1/1:0.50(manufacturer-specified “m”) | ||||
2.2:1/1:0.45 | 1 h/100 °C | 70 °C | ||
2.6:1/1:0.40 | ||||
3.0:1/1:0.33 |
CF | AF + CF | ACFP | AF |
---|---|---|---|
| | | |
A/B Mix Ratio (Resin/Hardener) | Cured 3 Days at 25 °C | Cured 1 h at 100 °C | ||
---|---|---|---|---|
Mean Tensile Strength (MPa) | Failure Mode | Mean Tensile Strength (MPa) | Failure Mode | |
−50% 1:0.3 | 35.80 | CF | 40.56 | CF |
−30% 1:0.4 | 34.48 | CF | 41.12 | CF |
−10% 1:0.45 | 35.68 | CF | 41.15 | CF |
m: 1:0.5 | 35.51 | CF | 39.90 | CF |
+10% 1:0.55 | 34.70 | CF | 37.62 | 78% AF + CF, 22% CF |
+30% 1:0.7 | 25.26 | 89% AF + CF, 11% CF | 27.40 | AF + CF |
A/B Mix Ratio (Resin/Hardener) | Cured 3 Days at 25 °C | Cured 1 h at 100 °C | ||
---|---|---|---|---|
Mean Tensile Strength (MPa) | Failure Mode | Mean Tensile Strength (MPa) | Failure Mode | |
−50% 1:0.3 | 23.19 | CF | 25.67 | CF |
−30% 1:0.4 | 22.72 | AF + CF | 24.44 | AF + CF |
−10% 1:0.45 | 22.67 | 78% CF, 22% AF + CF | 20.71 | AF + CF |
m: 1:0.5 | 20.57 | 56% AF + CF, 44% CF | 16.73 | 78% AF + CF, 22% AF |
+10% 1:0.55 | 13.88 | AF + CF | 10.58 | 56% AF + CF, 44% AF |
+30% 1:0.7 | 6.38 | ACFP | 5.23 | AF |
# | Test | Cubic Spline Forecast | Error (%) | Least-Squares Forecast | Error (%) |
---|---|---|---|---|---|
1 | 0.424 | 0.352 | 16.98 | 0.353 | 16.75 |
2 | 0.392 | 0.400 | 2.04 | 0.398 | 1.53 |
3 | 0.465 | 0.465 | 0.00 | 0.465 | 0.00 |
4 | 0.438 | 0.437 | 0.23 | 0.437 | 0.23 |
5 | 0.431 | 0.432 | 0.23 | 0.432 | 0.23 |
6 | 0.417 | 0.416 | 0.23 | 0.417 | 0.00 |
7 | 0.475 | 0.476 | 0.23 | 0.476 | 0.23 |
8 | 0.418 | 0.417 | 0.23 | 0.418 | 0.00 |
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Szabelski, J.; Karpiński, R.; Machrowska, A. Application of an Artificial Neural Network in the Modelling of Heat Curing Effects on the Strength of Adhesive Joints at Elevated Temperature with Imprecise Adhesive Mix Ratios. Materials 2022, 15, 721. https://doi.org/10.3390/ma15030721
Szabelski J, Karpiński R, Machrowska A. Application of an Artificial Neural Network in the Modelling of Heat Curing Effects on the Strength of Adhesive Joints at Elevated Temperature with Imprecise Adhesive Mix Ratios. Materials. 2022; 15(3):721. https://doi.org/10.3390/ma15030721
Chicago/Turabian StyleSzabelski, Jakub, Robert Karpiński, and Anna Machrowska. 2022. "Application of an Artificial Neural Network in the Modelling of Heat Curing Effects on the Strength of Adhesive Joints at Elevated Temperature with Imprecise Adhesive Mix Ratios" Materials 15, no. 3: 721. https://doi.org/10.3390/ma15030721