Experimental Evaluation of Nano Coating on the Draft Force of Tillage Implements and Its Prediction Using an Adaptive Neuro-Fuzzy Inference System (ANFIS)
Abstract
:1. Introduction
2. Materials and Methods
2.1. Material Characteristics
2.2. Test Stand
2.3. ANFIS Modelling
3. Results and Discussion
4. Conclusions
- The ANFIS model showed better performance in predicting the draft force than the stepwise regression models.
- It was found that soil–tool adhesion showed the greatest effect on draft force.
- The results showed that nano-coating of blades was significant in reducing draft force, especially in sticky soils, compared to normal St37 and galvanized steel. At a moisture content of 20%, the draft requirement of St37 and nano-coated blades was 936 and 477 N, respectively, indicating a 49% and 53% draft reduction in studying the blade speed effect on the draft requirement. These high percentages of draft reduction show the importance of Nano-coating in reducing the energy requirement of tillage operations. Also, the soil furrow created by nano blades was more symmetrical and monotonic.
- By understanding the conditions of the blades in the soil and the issues governing the tillage, the use of fiberglass through reinforced polymer fibers demonstrated acceptable performance compared to common galvanized steel against on draft reduction. However, nano-tantalum carbide was the best coating in terms of its resistance and draft force reduction.
- Improving the surface of the blades in the tools involved with the soil should be done in such a way as to reduce production costs and fuel consumption and increase tillage efficiency. These results can be achieved by knowing the working conditions of the blades in the soil and the tribological issues governing the tillage blades. Due to the challenges in conducting field trials as well as the high cost of coatings and coating application processes for large-scale tests, tests were conducted in the soil bin. It is hopeful that in the near future, by studying and applying the new methods of coating, problems regarding agricultural industry equipment, such as friction, wear, corrosion, erosion, etc., can be solved.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Soil Texture | Sand Percentage (%) | Clay Percentage (%) | Silt Percentage (%) |
---|---|---|---|
Sandy-Loamy | 73 | 10 | 17 |
Loamy | 46 | 24 | 30 |
Clay-Loamy | 30 | 40 | 30 |
Input Variables | Linear Output Function (Draft Force), N | ||||
---|---|---|---|---|---|
Rules | Speed, m·s−1 | Rake Angle, Degrees | Cohesion, Pa | Adhesion, Pa | |
Rule1 | slow | small | low | poor | D = 2.8s + 83a − 16c + 3962ad + 2.8 |
Rule8 | slow | small | high | mean | D = 45s + 1349a − 190c + 6741ad + 45 |
Rule18 | slow | avg | high | rich | D = 94s + 4247a − 290c + 5302ad + 94 |
Rule28 | normal | small | low | poor | D = −3.4s − 50.7a − 16.6c + 4136ad − 1.7 |
Rule38 | normal | avg | low | mean | D = −91s − 2061a − 235c + 5069ad − 45 |
Rule48 | normal | big | low | rich | D = −107s − 3220a − 566c + 6359ad − 53 |
Rule58 | fast | small | medium | poor | D = −28s − 279a − 15c + 5152ad − 9.3 |
Rule68 | fast | avg | medium | mean | D = −277s − 4161a − 131c + 3975ad − 92 |
Rule78 | fast | big | medium | rich | D = −867s − 1735a − 470c + 7668ad − 289 |
Rule81 | fast | big | high | rich | D = 1155s + 2310a − 469c + 7832ad + 385 |
Source | DOF | Sum of Squares | Mean of Squares | F-Value | p-Value |
---|---|---|---|---|---|
Speed | 2 | 8,431,423 | 4,215,711 | 3488.02 ** | 0.000 |
Rake angle | 2 | 6,209,571 | 3,104,785 | 2568.85 ** | 0.000 |
Adhesion | 4 | 41,820,740 | 10,455,185 | 8650.46 ** | 0.000 |
Cohesion | 8 | 15,521,397 | 1,940,175 | 1605.27 ** | 0.000 |
Speed*Rake angle | 4 | 123,109 | 30,777 | 25.46 ** | 0.000 |
Speed*Adhesion | 8 | 630,415 | 78,802 | 65.20 ** | 0.000 |
Speed*Cohesion | 16 | 240,828 | 15,052 | 12.45 ** | 0.000 |
Rake angle*Adhesion | 8 | 411,298 | 51,412 | 42.54 ** | 0.000 |
Rake angle*Cohesion | 16 | 193,016 | 12,064 | 9.98 ** | 0.000 |
Adhesion*Cohesion | 32 | 2,230,482 | 69,703 | 57.67 ** | 0.000 |
Speed*Rake angle*Adhesion | 16 | 22,892 | 1431 | 1.18 * | 0.275 |
Speed*Rake angle*Cohesion | 32 | 51,782 | 1618 | 1.34 * | 0.101 |
Speed*Adhesion*Cohesion | 64 | 294,870 | 4607 | 3.81 ** | 0.000 |
Rake angle*Adhesion*Cohesion | 64 | 177,193 | 2769 | 2.29 ** | 0.000 |
Speed*Rake angle*Adhesion*Cohesion | 128 | 191,407 | 1495 | 1.24 * | 0.049 |
Error | 810 | 978,988 | 1209 | ||
Total | 1214 | 77,529,410 |
Model | Type of MF | Number of MF | Optimization Method | Test | |||
---|---|---|---|---|---|---|---|
Input | Output | Input | Epoch | ε (%) | R2 | ||
Grid Partition | Trimf | Linear | Hybrid | 30 | Hybrid | 6.77 | 0.9407 |
Grid Partition | Gaussmf | Linear | Hybrid | 30 | Hybrid | 6.39 | 0.9491 |
Grid Partition | Trapmf | Linear | Hybrid | 30 | Hybrid | 6.27 | 0.9511 |
Grid Partition | Gbellmf | Linear | Hybrid | 30 | Hybrid | 6.1 | 0.9568 |
Model | Unstandardized Coefficients | Standardized Coefficients | t | sig | |
---|---|---|---|---|---|
B | Std. Error | Beta | |||
(Constant) | −576.275 | 40.645 | −14.178 | 0.000 | |
Speed | 100.032 | 6.596 | 0.325 | 15.167 | 0.000 |
Rake angle | 5.890 | 0.440 | 0.287 | 13.397 | 0.000 |
Cohesion | 0.025 | 0.01 | 0.372 | 17.295 | 0.000 |
Adhesion | 0.420 | 0.013 | 0.678 | 31.569 | 0.000 |
Plate Material | Regression Equation | Coefficient of Determination |
---|---|---|
ST37 | y = 12.7x + 519.33 | 0.9941 |
GAS | y = 8.95x + 790.67 | 0.8936 |
GFRP | y = 10x + 511.67 | 0.8698 |
N-TiN | y = 7.7x + 365.33 | 0.9973 |
N_TaC | y = 6.7x + 320.33 | 0.9166 |
Plate Material | Regression Equation | Coefficient of Determination |
---|---|---|
ST37 | y = 1425x + 684.67 | 0.9819 |
GAS | y = 1195x + 522 | 0.9999 |
GFRP | y = 980x + 521.67 | 0.9977 |
N-TiN | y = 755x + 358.33 | 0.9958 |
N_TaC | y = 645x + 325.33 | 0.9895 |
Plate Material | Regression Equation | Coefficient of Determination |
---|---|---|
ST37 | y = 1425x + 684.67 | 0.9819 |
GAS | y = 1195x + 522 | 0.9999 |
GFRP | y = 980x + 521.67 | 0.9958 |
N-TiN | y = 755x + 358.33 | 0.9958 |
N_TaC | y = 645x + 325.33 | 0.9895 |
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Marani, S.M.; Shahgholi, G.; Szymanek, M.; Tanaś, W. Experimental Evaluation of Nano Coating on the Draft Force of Tillage Implements and Its Prediction Using an Adaptive Neuro-Fuzzy Inference System (ANFIS). AgriEngineering 2024, 6, 1218-1234. https://doi.org/10.3390/agriengineering6020069
Marani SM, Shahgholi G, Szymanek M, Tanaś W. Experimental Evaluation of Nano Coating on the Draft Force of Tillage Implements and Its Prediction Using an Adaptive Neuro-Fuzzy Inference System (ANFIS). AgriEngineering. 2024; 6(2):1218-1234. https://doi.org/10.3390/agriengineering6020069
Chicago/Turabian StyleMarani, Saeed Mehrang, Gholamhossein Shahgholi, Mariusz Szymanek, and Wojciech Tanaś. 2024. "Experimental Evaluation of Nano Coating on the Draft Force of Tillage Implements and Its Prediction Using an Adaptive Neuro-Fuzzy Inference System (ANFIS)" AgriEngineering 6, no. 2: 1218-1234. https://doi.org/10.3390/agriengineering6020069