面向民用大飞机切削参数库系统的15-5PH不锈钢切削实验研究

发布时间：2018-03-24 11:43

本文选题：切削参数库　切入点：民用大飞机　出处：《南京理工大学》2016年硕士论文

【摘要】：15-5PH不锈钢具有卓越的力学性能和耐腐蚀性能,是民用大飞机(C919)及军机承力结构件的主要材料。然而该材料良好的机械性能也导致其加工过程中切削温度高、铣削力大、加工硬化现象严重。同时,由于该材料主要依赖进口,国外的技术封锁导致可参考的有价值的内容较少。为了改善加工质量,提高加工效率,迫切需要开展具有自主知识产权的民用大飞机切削参数库系统的构建研究。本文面向民用大飞机参数库系统,针对系统设计、切削结果预测及切削参数优化等关键问题,开展以下工作：首先,在切削参数库系统需求分析的基础上,提出了该切削参数库系统的整体结构设计,包括系统体系设计、系统流程设计和系统功能设计。最终完成民用大飞机切削参数库系统总体框架的构建。其次,通过正交实验设计开展15-5PH不锈钢基础特征件切削加工实验。分析了切削参数对深孔钻削孔径偏差、内侧圆角表面粗糙度和横向铣削力及侧壁表面粗糙度的影响规律,阐明了各切削参数对切削结果的影响程度,为切削参数库系统的实现提供了基础实验数据。再次,基于切削实验获取的实验结果,选择合理的预测算法分别建立了深孔钻削孔径预测模型,内侧圆角精加工表面粗糙度和横向铣削力预测模型以及侧壁精加工表面粗糙度预测模型,并通过实验验证了模型的合理性和准确性。为切削参数库系统切削结果预测模块提供了技术支持。最后,开展了三个基础特征件的切削参数优化。均以最大切削效率为目标函数,深孔钻削以孔径偏差为约束条件,内侧圆角精加工以表面粗糙度和横向铣削力为约束条件,侧壁精加工以表面粗糙度为约束条件,采用惩罚函数方法处理约束条件构建切削参数优化数学模型。并引入遗传算法及粒子群算法对切削参数进行优化求解,通过实验验证了优化方法的准确性和合理性。为切削参数库系统的核心功能——切削参数优化提供了技术支撑。
[Abstract]:15-5PH stainless steel has excellent mechanical properties and corrosion resistance, and is the main material of civil aircraft C919) and military aircraft bearing structure. However, the good mechanical properties of this material also lead to high cutting temperature and high milling force. The phenomenon of work hardening is serious. At the same time, because the material is mainly dependent on imports, the foreign technology blockade results in less valuable contents to be referenced. In order to improve the quality and efficiency of processing, It is urgent to carry out the research on the construction of the civil aircraft cutting parameter database system with independent intellectual property rights. This paper aims at the key problems such as system design, cutting result prediction and cutting parameter optimization. The following works are carried out: firstly, based on the requirement analysis of the cutting parameter library system, the overall structure design of the cutting parameter library system is proposed, including the system architecture design. System flow design and system function design. Finally, the overall framework of civil aircraft cutting parameter library system is constructed. Secondly, The cutting experiments of 15-5PH stainless steel foundation feature parts were carried out by orthogonal experiment. The influence of cutting parameters on the hole diameter deviation, the surface roughness of the inner corner, the transverse milling force and the surface roughness of the side wall were analyzed. The degree of influence of cutting parameters on cutting results is expounded, which provides the basic experimental data for the realization of cutting parameter library system. Thirdly, based on the experimental results obtained from cutting experiments, The prediction model of deep hole drilling aperture, the prediction model of surface roughness and transverse milling force of inner corner finish machining and the prediction model of side wall finish machining surface roughness are established by selecting reasonable prediction algorithm. The rationality and accuracy of the model are verified by experiments, which provide technical support for cutting result prediction module of cutting parameter library system. Finally, The optimization of cutting parameters of the three basic features is carried out. The maximum cutting efficiency is taken as the objective function, the hole diameter deviation is taken as the constraint condition in deep hole drilling, and the surface roughness and transverse milling force are taken as the constraint conditions in the finish machining of the inner circular corner. In the side wall finishing, the surface roughness is taken as the constraint condition, and the penalty function method is used to deal with the constraint condition to construct the mathematical model of cutting parameter optimization, and the genetic algorithm and particle swarm optimization algorithm are introduced to optimize the cutting parameters. The accuracy and rationality of the optimization method are verified by experiments, which provide technical support for the optimization of cutting parameters, which is the core function of the cutting parameter library system.
【学位授予单位】：南京理工大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：V261

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