考虑刀具偏心的微铣削加工表面粗糙度预测模型研究

基于微铣削刀具轨迹建立了考虑刀具偏心、最小切削厚度的塑性材料微铣削底面粗糙度的预测模型,预测结果表明粗糙度表面形貌呈锯齿状,刀具偏心会影响表面粗糙度的形貌周期和高度,并进行微铣削加工实验,对粗糙度预测模型进行了验证。采用回归统计方法,建立了脆性材料微铣削加工表面粗糙度预测模型,对加工表面形貌进行观察,利用响应曲面法得到了各铣削用量对表面粗糙度的影响规律。     

单晶金刚石微型刀具的工艺技术研究

介绍了单晶金刚石微型刀具的应用和技术特点,研究探讨了单晶金刚石微型刀具的设计原理和制造工艺,并探究了用"金刚石磨金刚石"方法加工金刚石微型刀具的可行性和潜力.     

Nondestructive acquisition of the micro-mechanical properties of high-speed-dry milled micro-thin walled structures based on surface traits

Requirements for the service performance of aeronautic microelectronic components are increasingly strict. However, sever issues, that the acquisition of the service performance such as micro-mechanical properties is destructive, limit the subsequent application of the tested components. Addressing this issue, this paper proposes a nondestructive acquisition method of the micro-mechanical properties of the accelerometer micro-components, based on analyzing surface traits. To select qualified components without damage, we firstly developed a quasi-static micro-tensile tester and then established a combination prediction model of mechanical properties based on micro-milled surface traits. The model works due to the thin-walled structure, which makes the machined surface traits have significant influences on the mechanical properties such as Young’s modulus, yield strength, tensile strength, and elongation at break. Surface roughness, surface structure, and surface anisotropy are extracted to comprehensively present surface traits from different aspects. For improving the practicability of the model, the principal component analysis (PCA) is adopted to reduce high-dimensional traits explanatory variable space into two dimensions, and regression analysis models are comparative established in predicting the mechanical properties. Residuals analysis and error analysis are carried out to show the prediction accuracy. The maximum prediction error is about 10.62%, but the significance levels in the t-test of the predicted Young’s modulus and yield strength are not ideal. Therefore, kernel support vector regression (SVR) is imported to improve the prediction ability of the combination prediction model. The residuals analysis result shows that SVR is effective in enhancing the prediction ability of this model.