The welding of medium and thick plates has a wide range of applications in the engineering field.Industrial welding robots are gradually replacing traditional welding operations due to their significant advantages,suc...The welding of medium and thick plates has a wide range of applications in the engineering field.Industrial welding robots are gradually replacing traditional welding operations due to their significant advantages,such as high welding quality,high work efficiency,and effective reduction of labor intensity.Ensuring the accuracy of the welding trajectory for the welding robot is crucial for guaranteeing welding quality.In this paper,the author uses the chaos sparrow search algorithm to optimize the trajectory of a multi-layer and multi-pass welding robot for medium and thick plates.Firstly,the Sparrow Search Algorithm(SSA)is improved by introducing tent chaotic mapping and Gaussian mutation of the inertia weight factor.Secondly,in order to prevent the welding robot arm from colliding with obstacles in the welding environment during the welding process,maintain the stability of the welding robot,and ensure the continuous stability of the changes in each joint angle,joint angular velocity,and angular velocity of the joint angle,a welding robot model is established by improving the Denavit-Hartenberg parameter method.A multi-objective optimization fitness function is used to optimize the trajectory of the welding robot,minimizing time and energy consumption.Thirdly,the optimization and convergence performance of SSA and Chaos Sparrow Search Algorithm(CSSA)are compared through 10 benchmark test functions.Based on the six sets of test functions,the CSSA algorithm consistently maintains superior optimization performance and has excellent stability,with a faster decline in the convergence curve compared to the SSA algorithm.Finally,the accuracy of welding is tested through V-shaped multi-layer and multi-pass welding experiments.The experimental results show that the CSSA algorithm has a strong superiority in trajectory optimization of multi-layer and multi-pass welding for medium and thick plates,with an accuracy rate of 99.5%.It is an effective optimization method that can meet the actual needs of production.展开更多
To overcome the limitations of traditional force aggregation methods,this paper proposes a novel clustering model integrating the self-adaptive tent chaos search ant lion optimizer(SATC-ALO)and the self-organizing map...To overcome the limitations of traditional force aggregation methods,this paper proposes a novel clustering model integrating the self-adaptive tent chaos search ant lion optimizer(SATC-ALO)and the self-organizing map(SOM)network.The model introduces a hybrid distance calculation method to measure inter-target distances and enhances the ant lion optimization algorithm through tent chaos sequences,adaptive tent chaos search,tournament selection,and logistic chaos sequences.Aggregation accuracy is evaluated using minimum quantization error and confidence value for the SOM neural network.The model is resolved using SATC-ALO and SOM independently,with experiments demonstrating that SOM achieves fast and accurate grouping,while SATC-ALO offers higher precision but requires longer computational runtime,making it more suitable for hybrid approaches.Both methods are validated as practical solutions for force aggregation tasks.展开更多
基金support by Ningxia Key R&D projects“Integration and demonstration application of intelligent finishing system for large casting riser robot”(No.2021BEE03002)Ningxia Natural Science Foundation Project“Research on detection and location of large casting welding seam based on depth learning”(No.2020AAC03201).
文摘The welding of medium and thick plates has a wide range of applications in the engineering field.Industrial welding robots are gradually replacing traditional welding operations due to their significant advantages,such as high welding quality,high work efficiency,and effective reduction of labor intensity.Ensuring the accuracy of the welding trajectory for the welding robot is crucial for guaranteeing welding quality.In this paper,the author uses the chaos sparrow search algorithm to optimize the trajectory of a multi-layer and multi-pass welding robot for medium and thick plates.Firstly,the Sparrow Search Algorithm(SSA)is improved by introducing tent chaotic mapping and Gaussian mutation of the inertia weight factor.Secondly,in order to prevent the welding robot arm from colliding with obstacles in the welding environment during the welding process,maintain the stability of the welding robot,and ensure the continuous stability of the changes in each joint angle,joint angular velocity,and angular velocity of the joint angle,a welding robot model is established by improving the Denavit-Hartenberg parameter method.A multi-objective optimization fitness function is used to optimize the trajectory of the welding robot,minimizing time and energy consumption.Thirdly,the optimization and convergence performance of SSA and Chaos Sparrow Search Algorithm(CSSA)are compared through 10 benchmark test functions.Based on the six sets of test functions,the CSSA algorithm consistently maintains superior optimization performance and has excellent stability,with a faster decline in the convergence curve compared to the SSA algorithm.Finally,the accuracy of welding is tested through V-shaped multi-layer and multi-pass welding experiments.The experimental results show that the CSSA algorithm has a strong superiority in trajectory optimization of multi-layer and multi-pass welding for medium and thick plates,with an accuracy rate of 99.5%.It is an effective optimization method that can meet the actual needs of production.
基金supported by the Youth Talent Support Program of Xi’an Association for Science and Technology(0959202513098)the National Natural Science Foundation of China(62106284)the Natural Science Foundation of Shanxi Province(2021JQ370).
文摘To overcome the limitations of traditional force aggregation methods,this paper proposes a novel clustering model integrating the self-adaptive tent chaos search ant lion optimizer(SATC-ALO)and the self-organizing map(SOM)network.The model introduces a hybrid distance calculation method to measure inter-target distances and enhances the ant lion optimization algorithm through tent chaos sequences,adaptive tent chaos search,tournament selection,and logistic chaos sequences.Aggregation accuracy is evaluated using minimum quantization error and confidence value for the SOM neural network.The model is resolved using SATC-ALO and SOM independently,with experiments demonstrating that SOM achieves fast and accurate grouping,while SATC-ALO offers higher precision but requires longer computational runtime,making it more suitable for hybrid approaches.Both methods are validated as practical solutions for force aggregation tasks.
