Grinding is a critical method for enhancing the quality of worm tooth surfaces, and its process optimization has long been a significant research focus;however, existing methods are insufficient in addressing the nonl...
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Grinding is a critical method for enhancing the quality of worm tooth surfaces, and its process optimization has long been a significant research focus;however, existing methods are insufficient in addressing the nonlinearity and complexity inherent in the grinding of complex surfaces. In this study, a three-objectiveoptimization function tailored for grinding complex spiral surfaces is developed and experimentally validated. We have successfully applied the innovative integration of the multi-objective grey wolf optimization algorithm (MOGWO) and the optimization function to optimize the grinding process of the Roller Enveloping Worm Reducer (REWR). To account for actual working conditions, we developed constrained models for grinding ratio and machining rigidity and improved the boundary processing method for MOGWO optimization. The enhanced MOGWO demonstrates superior search capabilities during the optimization process, with its optimal solution outperforming traditional optimizationalgorithms. The optimized grinding process parameters reduce the grinding time by 17.41%, improve the grinding surface quality by 4.46%, and reduce the grinding cost by 1.12% compared with the conventional machining scheme. This provides practical guidance for optimizing the REWR and other complex surface grinding processes.
Since gas turbines have fast start-stop and load regulation characteristics, they can effectively suppress the instability caused by wind power and solar power units, so the collaborative operation of integrated power...
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Since gas turbines have fast start-stop and load regulation characteristics, they can effectively suppress the instability caused by wind power and solar power units, so the collaborative operation of integrated power-gas systems(IPGS) is increasingly important. In this paper, a multi-objective environmental-economic dispatch optimization model for the IPGS with wind power and solar power is considered in a random environment, and the coupling constraint of the IPGS is established. Aiming at the random problem in the model, considering the statistical significance, the random variables are limited to the Wasserstein distribution set, and the distribution robust optimization is introduced. Since multi-objectiveoptimization problems are difficult to solve exactly, this paper improves the multi-objective gray wolfoptimizationalgorithm(IMOGWO) and verifies the effectiveness of the improved algorithm by standard functions. Finally, through numerical experiments for comparison, the results show that: (1) The IMOGWO algorithm is more suitable for this type of problem as it can solve the multi-objective distribution robust model more quickly than the existing algorithms;(2) Distributed robust multi-objective modeling effectively reduces the environmental-economic dispatch cost of the IPGS, improves the dispatch economy, reduces CO2 emissions, and provides a powerful decision-making tool for policy-makers.
The path planning of traditional spot welding mostly uses manual teaching method. Here, a new model of path planning is established from two aspects of welding length and welding time. Then a multi-objectivegreywolf...
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The path planning of traditional spot welding mostly uses manual teaching method. Here, a new model of path planning is established from two aspects of welding length and welding time. Then a multi-objective grey wolf optimization algorithm with density estimation (DeMOGWO) is proposed to solve multi-object discrete problems. The algorithm improves the coding method and operation rules, and sets the density estimation mechanism in the environment update. By comparing with other five algorithms on the benchmark problem, the simulation results show that DeMOGWO is competitive which takes into account both diversity and convergence. Finally, the DeMOGWO algorithm is used to solve the model established of path planning. The Pareto solution obtained can be used to guide the welding sequence of body-in-white(BIW) workpieces.
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