Study Of The Optimal Clamping Scheme Of Aerospace Thin-walled Parts

With the development of manufacture,the technology of manufacturing industry in our country is also being improved,and the aerospace manufacturing industry is especially concerned.When aerospace industry has been paid attention to and flourished,the aerospace thin-walled parts has been favored by the aerospace industry,as to its light weight,durability.In this paper,it is the key for improving the machining quality to investigate the prediction and control of machining deformation,which is of great significance to realize the productivity and accuracy for machining process.Therefore,the finite element model is established for the clamping and the milling process of thin-walled parts.There are two ways to get the optimizing clamping scheme of the thin-walled parts.One of the ways combines the finite element model analysis with the genetic algorithm to optimize the clamping scheme.The other bases on the genetic algorithm and the neural network created by training the data from the finite element model.Compare with the former,the later is fit for optimizing clamping scheme of the thin-walled parts as well.The finite element model is established for the milling process of thin-walled parts in this paper.Thus,the workpiece deformations can be calculated to be the training samples of the back propagation(i.e.,BP)neutral network.The experimental results show that the simulated values are in good agreement with the corresponding experimental data.It confirms that the model is reasonable.It has influence on the model of BP neutral network in a way that the initial weights and thresholds of the neutral network will appear in random.The genetic algorithm can skillfully be developed for the optimal initial weights and thresholds of the neutral network.The optimized neutral network is of better generalization ability and prediction accuracy.And then,the finite groups of training samples,whose number is determined by the finite element model,can be relied on to achieve the prediction method of workpiece deformation.By defining the prediction error of output samples as the individual fitness,the genetic algorithm develops for the optimal initial weights and thresholds of the neutral network.It is able to predict the simulated workpiece deformations within a 6% error margin.Finally,with the above conclusions,the method is established.to optimize theinitial weights and threshold of the neural network.And the deformation of Aerospace thin-walled parts is predicted.Then an optimal model with the objective of minimizing the workpiece deformation is proposed to plan the location of the clamps.When the individual fitness is constructed to be a function of the workpiece deformation,the genetic algorithm based solution technology is suggested for the optimal model of the location of the clamps.The proposed method can improve the calculation efficiency.In addition,it can also provide a basic theory of design and selection of the clamping scheme.