Layout Optimization of the Six‐Axis Industrial Robot Based on an Improved Whale Algorithm for Reducing Energy Consumption in Industry 5.0
Bibliographic record
Abstract
In the advent of Industry 5.0, the harmonious integration of human ingenuity and robotic precision in complex work environments is pivotal for sustainable industrial growth. The six-axis industrial robot, as an essential part of carrying out cyclic pick-and-place tasks in Industry 5.0, usually works in an extremely complex working environment. This intricate working environment makes the six-axis industrial robot difficult to reach the task points effectively, resulting in a lot of energy consumption. This not only impacts productivity but also leads to excessive energy consumption, which stands at odds with the Industry 5.0 principles of resource conservation. To solve this problem, a novel method to optimize the layout scheme of the six-axis industrial robot with the goal of minimizing the energy loss is creatively proposed in this paper. First, the reachable workspace and feasible workspace under constraints are mathematically modeled and then obtained. Second, the operability and the minimum singular value are utilized to evaluate the energy loss of the feasible workspace. Third, the whale algorithm is designed and improved to obtain the optimal layout scheme of the six-axis industrial robot. Finally, a case of the recliner's production line with the six-axis industrial robot (IRB140; ABB) is provided to validate the effectiveness of the proposed method. The results show that after optimization, the optimal layout scheme has been successfully obtained, and the energy loss has reduced from 0.2917 to 0.2309, a decrease of 20.84%, proving that the proposed method can obtain the optimal layout scheme with lower energy consumption.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".