Based on features of dimension variation propagation in multi-station assembly processes,a new quality evaluation model of assembly processes is established. Firstly,the error source of multi-station assembly system i...Based on features of dimension variation propagation in multi-station assembly processes,a new quality evaluation model of assembly processes is established. Firstly,the error source of multi-station assembly system is analyzed,the relationship of dimension variation propagation in multi-station assembly processes is studied and the state equation for variation propagation is constructed too. Then,the feature parameters which influence variation propagation and accumulation in multi-station assembly processes are found to evaluate quality characteristic of the assembly system. Through the derivation of equation on dimension variation propagation,station coefficient matrices which are combined and conversed to determine the max eigenvalue are educed. The max eigenvalue is multiplied by the weight coefficient to establish the quality evaluation model in multi-station assembly processes. Furthermore,assembly variation indexes are proposed to judge of the assembly technology process. Finally,through the practical example,the application of the model and assembly variation indexes are presented.展开更多
In the non-linear microwave drying process, the incremental improved back-propagation （BP） neural network and response surface methodology （RSM） were used to build a predictive model of the combined effects of ind...In the non-linear microwave drying process, the incremental improved back-propagation （BP） neural network and response surface methodology （RSM） were used to build a predictive model of the combined effects of independent variables （the microwave power, the acting time and the rotational frequency） for microwave drying of selenium-rich slag. The optimum operating conditions obtained from the quadratic form of the RSM are： the microwave power of 14.97 kW, the acting time of 89.58 min, the rotational frequency of 10.94 Hz, and the temperature of 136.407 ℃. The relative dehydration rate of 97.1895% is obtained. Under the optimum operating conditions, the incremental improved BP neural network prediction model can predict the drying process results and different effects on the results of the independent variables. The verification experiments demonstrate the prediction accuracy of the network, and the mean squared error is 0.16. The optimized results indicate that RSM can optimize the experimental conditions within much more broad range by considering the combination of factors and the neural network model can predict the results effectively and provide the theoretical guidance for the follow-up production process.展开更多
基金supported by the National Natural Science Foundation of China ( Grant No.50575072)Scientific Research Fund of Hunan Provincial Education Department(Grant No.07C281)
文摘Based on features of dimension variation propagation in multi-station assembly processes,a new quality evaluation model of assembly processes is established. Firstly,the error source of multi-station assembly system is analyzed,the relationship of dimension variation propagation in multi-station assembly processes is studied and the state equation for variation propagation is constructed too. Then,the feature parameters which influence variation propagation and accumulation in multi-station assembly processes are found to evaluate quality characteristic of the assembly system. Through the derivation of equation on dimension variation propagation,station coefficient matrices which are combined and conversed to determine the max eigenvalue are educed. The max eigenvalue is multiplied by the weight coefficient to establish the quality evaluation model in multi-station assembly processes. Furthermore,assembly variation indexes are proposed to judge of the assembly technology process. Finally,through the practical example,the application of the model and assembly variation indexes are presented.
基金Project(50734007) supported by the National Natural Science Foundation of China
文摘In the non-linear microwave drying process, the incremental improved back-propagation （BP） neural network and response surface methodology （RSM） were used to build a predictive model of the combined effects of independent variables （the microwave power, the acting time and the rotational frequency） for microwave drying of selenium-rich slag. The optimum operating conditions obtained from the quadratic form of the RSM are： the microwave power of 14.97 kW, the acting time of 89.58 min, the rotational frequency of 10.94 Hz, and the temperature of 136.407 ℃. The relative dehydration rate of 97.1895% is obtained. Under the optimum operating conditions, the incremental improved BP neural network prediction model can predict the drying process results and different effects on the results of the independent variables. The verification experiments demonstrate the prediction accuracy of the network, and the mean squared error is 0.16. The optimized results indicate that RSM can optimize the experimental conditions within much more broad range by considering the combination of factors and the neural network model can predict the results effectively and provide the theoretical guidance for the follow-up production process.
