The Research Of Frame Optimization To Concrete Pump's Booms System Based On Improved BP Neural Networks

Current there is very big development in our country's concrete pump vehicle industry. But compared with the abroad, it is still placed in a low level . The production of long boom concrete pump vehicles above 42m need to introduce the whole techniques or import key parts, so it is very necessary to improve the design level of concrete pump vehicle series with long booms and especially to the boom system. 42m concrete pumping vehicle has being produced recently in domestic. This paper researches into the frame optimizing design of boom system of the vehicle. The research combines BP neural networks, genetic algorithm and finite element.Because of the complexity of the booms' system, we often calculate the results again and again when making use of finite element software. The efficiency and the model's accuracy need further improving and the optimum results require further perfecting. The finite element model and computing models are established in the paper, and furthermore, the computing results answer the measuring data well. The intelligent algorithm of modified BP neural network is applied in the paper to design the nonlinear mapping relationship between the stress & the strain and the main structure variables. Subsequently, Simulation Anneal-Genetic Algorithm is used to optimize the data and gives a better result. According to the engineering and optimum needing, a kind of integrated optimum software is set up to design the booms' system. The software not only is a good tool for the boom system's designing, but also verifies the rationality and feasibility of these models. In a word, this paper achieves a series of useful theories and practical results.This article divides eight chapters altogether. Chapter one is introduction, which summaries the history, development and current situation of BP neural networks, genetic algorithm and finite element, talks about the domestic industry of concrete pump vehicle , then explains the proposition, purpose and meaning of this thesis. In chapter two to three, the theories, construction and main defects of BP neural networks are described systematically and a modified method, which can adjust study parameters dynamically and adaptively, is put forward. To the booms ' system, the finite element model and computing model are built inchapter five. Subsequently, the rationality of the models is verified. Based on the improved BP neural networks algorithm, an analysis software tool used for the analysis is introduced in Chapter six. Chapter seven set up the optimizing model of the booms' system, then discusses the realization of the optimum design. Chapter eight is a conclusion of the study work of the thesis and puts forward the direction of further research development in the future.