A Data-Driven Approach for Energy Consumption Modeling and Optimization of Welding Robot Systems
Abstract
1. Introduction
2. Welding Robot EC Modeling
2.1. EC Analysis of Welding Robot System
2.2. Experimental Setup and Data Collection
2.3. Modeling Method
3. Welding Robot EC Optimization
3.1. Strategies to Reduce Robot EC
3.2. Optimization Method
- (1)
- Algorithm initialization: Set the population size as and the maximum number of iterations as .
- (2)
- Population initialization: Set the initial iteration step as , and initialize the initial population using the opposition-based learning strategy.
- (3)
- Optimal position selection: Calculate the fitness values of the individuals, and select the position of the individual with the optimal fitness value as the optimal position.
- (4)
- EOBL: Select the top proportion of individuals with the best fitness values as elite individuals, and calculate their reverse solutions using the elite reverse learning strategy. Select solutions with better fitness values through competition.
- (5)
- Iterative calculation: Perform iterative optimization based on random search, encircling prey, and attacking prey.
- (6)
- DE fine-tuning: Based on the current optimal solution, perform crossover and mutation operations. If a solution with better fitness is obtained, replace the current optimal solution.
- (7)
- Termination condition check: If satisfied, terminate the iteration and output the current optimal solution; otherwise, return to step (3).
Algorithm 1 IWOA with DE and EOBL. |
Require: Population size , max iterations , elite ratio , DE parameters , Ensure: Global best solution 1: Initialization: 2: Generate initial population using OBL: 3: 4: Set convergence factor , spiral coefficient 5: Fitness evaluation: 6: Calculate fitness for each and find 7: Elite opposition-based learning: 8: Select top elite individuals 9: For each elite , calculate reverse: 10: 11: Update using greedy selection 12: while do 13: Update , , 14: for each individual do 15: if then 16: Update position using encircling or global search 17: else 18: Update position using bubble-net attack 19: end if 20: end for 21: DE fine-tuning: 22: Generate mutant vector: 23: Perform binomial crossover and update 24: Cauchy mutation: 25: Apply perturbation to 26: Increment iteration: 27: end while 28: Return: |
4. Experimental Results and Analysis
4.1. Experimental Platform
4.2. Experimental Result
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Movement Type | Tests Velocity |
---|---|
PTP/(%) | 2, 5, 8, 10, 15, 20, 30, 40, 50, 70 |
Linear motion/(mm/s) | 2, 4, 6, 8, 10, 15, 20, 30, 50, 75, 100, 150, 200, 300, 500 |
Circular motion/(mm/s) | 2, 4, 6, 8, 10, 15, 20, 30 |
Model | MSE | MAE/(W) | MAPE | |
---|---|---|---|---|
Regression Tree | 0.0025 | 0.96 | 5.3021 | 0.0186 |
Linear Regression | 0.0371 | 0.43 | 28.6886 | 0.0962 |
Neural Networks | 0.0064 | 0.90 | 11.1324 | 0.0370 |
Regression Tree Ensemble | 0.0024 | 0.96 | 6.2041 | 0.0211 |
XGBoost | 0.0012 | 0.98 | 3.8723 | 0.0131 |
Parameters | Value |
---|---|
Number of axes | 6 |
Load | 8 kg |
Body weight | 190 kg |
Maximum arm span | 1465 mm |
Maximum power | 4 kW |
Repetitive positioning accuracy | ±0.08 mm |
Restriction Type | Joint 1 | Joint 2 | Joint 3 | Joint 4 | Joint 5 | Joint 6 |
---|---|---|---|---|---|---|
Joint position/(°) | −90∼90 | −45∼75 | −55∼75 | −85∼35 | −60∼35 | −300∼300 |
Joint velocity/(°/s) | 170 | 420 | 440 | 860 | 860 | 1300 |
Joint acceleration/(°/s2) | 1700 | 4200 | 3440 | 5840 | 6080 | 11,200 |
Restriction Type | x/(mm) | y/(mm) | z/(mm) | Rz/(°) |
---|---|---|---|---|
Workpiece coordinate position | 350∼1000 | −800∼800 | 400∼1200 | −90∼90 |
Item | Workpiece Coordinate Position (x, y, z, Rx, Ry, Rz) | Algorithm Execution Time | Predicted /(J) | Predicted /(J) |
---|---|---|---|---|
Before optimization | (420, 450, 1150, 0, 0, 30) | - | 38,965.35 | 579.86 |
Minimization of | (350.30, −659.31, 890.35, 0, 0, −55.96) | 1590 s | 37,020.31 | 554.21 |
Minimization of | (703.97, −161.84, 1003.01, 0, 0, −13.82) | 1648 s | 36,178.21 | 559.65 |
Item | /(°/s) | /(°/s) | /(°/s) | /(°/s) | /(°/s) | /(°/s) |
---|---|---|---|---|---|---|
Before optimization | 1.19 | 1.62 | 1.69 | 0.66 | 0.82 | 4.68 |
Minimization of | 1.05 | 1.54 | 1.60 | 0.70 | 0.90 | 4.47 |
Minimization of | 1.07 | 1.61 | 1.65 | 0.76 | 0.88 | 4.56 |
Item | Actual /(J) | Actual /(J) | MAE Between Predicted and Actual Power/(W) | Average Power of the /(W) |
---|---|---|---|---|
Before optimization | 39,911.59 | 599.76 | 7.14 | 257.24 |
Minimization of | 38,282.80 | 573.82 | 6.82 | 246.74 |
Minimization of | 37,230.33 | 580.64 | 7.34 | 239.96 |
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Pan, M.; Jia, B.; Zhang, L.; Pan, H.; Chen, L. A Data-Driven Approach for Energy Consumption Modeling and Optimization of Welding Robot Systems. Machines 2025, 13, 532. https://doi.org/10.3390/machines13060532
Pan M, Jia B, Zhang L, Pan H, Chen L. A Data-Driven Approach for Energy Consumption Modeling and Optimization of Welding Robot Systems. Machines. 2025; 13(6):532. https://doi.org/10.3390/machines13060532
Chicago/Turabian StylePan, Minling, Bingqi Jia, Lei Zhang, Haihong Pan, and Lin Chen. 2025. "A Data-Driven Approach for Energy Consumption Modeling and Optimization of Welding Robot Systems" Machines 13, no. 6: 532. https://doi.org/10.3390/machines13060532
APA StylePan, M., Jia, B., Zhang, L., Pan, H., & Chen, L. (2025). A Data-Driven Approach for Energy Consumption Modeling and Optimization of Welding Robot Systems. Machines, 13(6), 532. https://doi.org/10.3390/machines13060532