基于PSOSVR在钕铁硼氢粉碎工艺中的应用研究.docx

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1、论文题目：PSO-SVR在钛铁硼氢粉碎工艺中的应用研究英文题目：Study on the application of PSO-SVR in NdFeBhydrogen crushing process独创性说明本人郑重声明：所呈交的论文是我个人在导师指导下进行的研究工作及取得研究成果。尽我所知，除了文中特别加以标注和致谢的地方外，论文中不包含其他人已经发表或撰写的研究成果，也不包含为获得内蒙古科技大学或其他教育机构的学位或证书所使用过的材料。与我一同工作的同志对本研究所做的任何贡献均已在论文中做了明确的说明并表示了谢意。签名:日期:摘要被铁硼氢粉碎是永磁材料生产工艺中常用的制粉方法，由于被铁

2、硼氢粉碎过程中被粉碎合金的粉碎状态、合金中氢含量等生产运行数据无法在线检测，导致被铁硼氢粉碎工艺难以实现高水平自动控制。操作工人只能靠经验判断，以延长合金吸氢时间来保证合金完全粉碎，造成生产周期延长，浪费资源，生产成本加大。合金吸氢反应过程状态未知，难以调整最优控制参数，会影响合金粉末的品质。为了保证产品质量及生产效益，需要对被铁硼氢粉碎控制系统相关参数实施在线检测，以满足工艺控制的需求。本文对铉铁硼氢粉碎原理、工艺、影响氢粉碎产品质量和生产周期的重要因素以及实现控制的难点作了深入分析研究，针对粉碎过程重要参数无法在线检测问题，提出基于支持向量机的预测方法实现被铁硼合金中氢含量的预报，并根据预

3、报结果来调整相关控制参数，缩短生产周期，提高产品质量。支持向量机建立在统计学习理论基础上，通过核函数计算将低维空间非线性问题转化成高维空间线性问题，能较好地解决小样本、非线性、高维数等问题，是解决非线性问题的重要工具。以其很强的学习能力和泛化能力，受到智能算法领域学者的广泛关注，目前在许多领域得到成功应用。论文的主要内容包括：1、对钛铁硼氢粉碎原理及工艺过程进行研究，深入分析影响产品质量及生产周期的因素。2、简单介绍支持向量机理论基础及基本原理，利用生产过程的历史数据建立支持向量机预测模型，采用MATLAB软件进行仿真和验证。3、基于交叉验证思想，以支持向量机预测模型的均方误差最小为目标，采用

4、粒子群优化算法对支持向量机模型参数进行优选，相比网格搜索法，粒子群算法具有更高的预测精度及更强的泛化能力。4、建立氢粉碎工况数据库，通过ODBC方式与MATLAB进行数据交互，方便MATLAB预测模型调取实时数据，同时将预测结果存入数据库，用于分析使用。5、设计控制系统监控画面，组态连接变量，通过OPC通讯完成预测模型和WINCC之间数据交互，将预测模型用于控制系统中，实现WinCC对难测工艺参数的监控，便于实施相应控制。关键词：氢粉碎；支持向量机；铉铁硼；OPC通讯AbstractHydrogen crushing of neodymium iron boron is a commonly

5、used milling method inthe permanent magnetic material production process. The production operation data, thealloy state and the hydrogen content in alloy, cannot be detected online in the NdFeBhydrogen crushing process; resulting in NdFeB hydrogen ciushing process is difficult toachieve a high level

6、 of automatic control. Workers can only rely on experience to judge,extend the hydrogen absorption time to ensure that the alloy completely crushed, resulting inthe production cycle is prolonged, the resources are wasted, production costs increase. Thestate of alloy in the absorbing hydrogen reactio

7、n process is unknown. So it is difficult toadjust the optimal control parameters, resulting in the quality of the alloy powder will beaffected. In order to guarantee the product quality and production efficiency, it is needed toimplement online detection of NdFeB hydrogen crushing control system par

8、ameters to meetprocess control requirements.This paper makes thorough analysis and research on the NdFeB hydrogen crushingprinciple, process, and important factors affect hydrogen crushing product quality andproduction cycle as well as the difficulty to control. Aiming at the problem that the import

9、antparameters of crushing process can not be online detection, proposed to use the supportvector machine method to realize the prediction of hydrogen content in NdFeB alloy, andaccording to the forecast results to adjust the control parameters, to shorten the productioncycle, improve the quality of

10、the products.Support vector machine is developed based on the theory of statistics, converts lowdimensional nonlinear problem into a linear problem in high dimensional space by kernelfunction calculations, can solve the small sample, is an important soft measurement tools tosolve nonlinear problems.

11、 With its strong learning ability and generalization ability, it iswidely concerned by scholars in the field of intelligent algorithm. It has been successfullyapplied in many fields. The main contents of this paper include:Firstly, this paper analyses the factors affecting product quality and produc

12、tion cyclethrough researching NdFeB hydrogen crushing principle and process.Secondly, this paper introduces the theory foundation and basic principle of supportvector machine briefly, uses historical data of production process to establish support vectormachine prediction model, simulates and verifi

13、es the model by MATLAB software.Thirdly, with the objective of minimum mean square error of support vector machineprediction model based on cross validation, support vector machine model parameters wereoptimized by particle swann optimization algorithm. Comparing with grid search method,particle swa

14、rm optimization algorithm has higher prediction accuracy and bettergeneralization ability.Moreover, hydrogen crushing condition database has been built, exchanges data withMATLAB through ODBC method .It is convenient for MATLAB prediction model toobtain real-time data. The prediction results are sto

15、red in the database for analysis and use.Last but not the least, this paper designed the monitor screen of control system,configured variables, and completed the prediction data interaction between the model andthe WINCC through the OPC communication. The prediction model is Applied in thecontrol sy

16、stem, so WinCC can online monitor unpredictable process parameters. It is toconvenient for workers to implement corresponding control.Key Words： Hydrogen crushing； Support vector machine neodymium ironboron ； OPC communication摘 要II1课题研究的目的及意义-2-1.1 氢粉碎工艺-2-1.1.1 被铁硼氢粉碎反应原理-2-1.1.2 钛铁硼的氢粉碎生产流程-3 -1.2 国内外钦铁硼氢工艺控制