摘要
利用快刀伺服系统加工可获得纳米级的微结构,桥式柔性铰链是最关键的零件之一,其柔性铰链的误差度直接影响零件的加工精度。通过对多误差源的分析,探索每个误差源对精度的影响程度。包括过对桥式柔性铰链关键尺寸的加工误差,特别是对敏感度较高的长度、宽度、厚度加工误差的分析,建立起关键尺寸与加工精度的数学模型;分析温度变化,建立基于神经网络的加工误差数学模型,并提出温度补偿的策略;分析重力等原因引起的误差变形,建立由于重力引起变形量与加工精度的数学模型。将上述的多误差源的数学建模运用到设计和制造中,能从源头上减少误差对加工精度的影响程度,提高快刀伺服系统加工微结构零件的尺寸精度和表面粗糙度。
Using fast tool servo system to machine the nanometer grade micro-structure,the bridge flexure hinge is a key part. The error of flexure hinge directly affects the machining accuracy of micro-structure. This paper explores the influence degree on the accuracy through analyzing all kinds of error source. The mathematical model of key dimension and machining accuracy is established including the machining error of bridge flexure hinge, which includes the sensitive size of length, width and thickness. After analyzing temperature change, the mathematical model of machining error is established based on neural network and the temperature compensation strategy is put forward. After Analyzing the reasons of deformation error caused by gravity, the mathematical model of deformation and machining precision is established. The above mathematical modeling applied for the design and manufacturing process can reduce their influence on machining accuracy,and it also can help the fast tool servo system to improve micro-structure size precision and surface roughness.
出处
《机电工程技术》
2014年第9期9-13,共5页
Mechanical & Electrical Engineering Technology
基金
广东省引进创新科研团队计划(编号:201001G0104781202)
关键词
柔性铰链
误差
加工精度
微结构
神经网络
flexure hinge
error
machining accuracy
micro-structure
neural networks