Aim To introduce a new method of adaptive shape optimization (ASOP) based on three-dimensional structure boundary strength and optimize an engine bearing cap with the method. Methods Using the normal substance's p...Aim To introduce a new method of adaptive shape optimization (ASOP) based on three-dimensional structure boundary strength and optimize an engine bearing cap with the method. Methods Using the normal substance's property of thermal expansion and cooling shrinkage,the load which is proportional to the difference between the nodes' stress and their respective objective stress were applied to the corresponding variable nodes on the boundary.The thermal load made the nodes whose stress is greater than their objective stress expand along the boundary's normal direction and the nodes whose stress is less than objec- tive stress shrink in the opposite direction , This process would repeat until the stress on the boundary nodes was converge to the objective stress. Results The satisfied results have been obtained when optimizing an engine bearing cap.The mass of the bearing cap is reduced to 55 percent of the total. Conclusion ASOP is an efficient,practical and reliable method which is suitable for optimizing the shape of the continuous structures.展开更多
To predict the temperature of a motorized spindle more accurately,a novel temperature prediction model based on the back-propagation neural network optimized by adaptive particle swarm optimization(APSO-BPNN)is propos...To predict the temperature of a motorized spindle more accurately,a novel temperature prediction model based on the back-propagation neural network optimized by adaptive particle swarm optimization(APSO-BPNN)is proposed.First,on the basis of the PSO-BPNN algorithm,the adaptive inertia weight is introduced to make the weight change with the fitness of the particle,the adaptive learning factor is used to obtain different search abilities in the early and later stages of the algorithm,the mutation operator is incorporated to increase the diversity of the population and avoid premature convergence,and the APSO-BPNN model is constructed.Then,the temperature of different measurement points of the motorized spindle is forecasted by the BPNN,PSO-BPNN,and APSO-BPNN models.The experimental results demonstrate that the APSO-BPNN model has a significant advantage over the other two methods regarding prediction precision and robustness.The presented algorithm can provide a theoretical basis for intelligently controlling temperature and developing an early warning system for high-speed motorized spindles and machine tools.展开更多
文摘Aim To introduce a new method of adaptive shape optimization (ASOP) based on three-dimensional structure boundary strength and optimize an engine bearing cap with the method. Methods Using the normal substance's property of thermal expansion and cooling shrinkage,the load which is proportional to the difference between the nodes' stress and their respective objective stress were applied to the corresponding variable nodes on the boundary.The thermal load made the nodes whose stress is greater than their objective stress expand along the boundary's normal direction and the nodes whose stress is less than objec- tive stress shrink in the opposite direction , This process would repeat until the stress on the boundary nodes was converge to the objective stress. Results The satisfied results have been obtained when optimizing an engine bearing cap.The mass of the bearing cap is reduced to 55 percent of the total. Conclusion ASOP is an efficient,practical and reliable method which is suitable for optimizing the shape of the continuous structures.
基金The National Natural Science Foundation of China(No.51465035)the Natural Science Foundation of Gansu,China(No.20JR5R-A466)。
文摘To predict the temperature of a motorized spindle more accurately,a novel temperature prediction model based on the back-propagation neural network optimized by adaptive particle swarm optimization(APSO-BPNN)is proposed.First,on the basis of the PSO-BPNN algorithm,the adaptive inertia weight is introduced to make the weight change with the fitness of the particle,the adaptive learning factor is used to obtain different search abilities in the early and later stages of the algorithm,the mutation operator is incorporated to increase the diversity of the population and avoid premature convergence,and the APSO-BPNN model is constructed.Then,the temperature of different measurement points of the motorized spindle is forecasted by the BPNN,PSO-BPNN,and APSO-BPNN models.The experimental results demonstrate that the APSO-BPNN model has a significant advantage over the other two methods regarding prediction precision and robustness.The presented algorithm can provide a theoretical basis for intelligently controlling temperature and developing an early warning system for high-speed motorized spindles and machine tools.
