防锈铝合金弱刚度复杂构件高速铣削工艺研究
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摘要
防锈铝合金由于具有较强的反射可见光、热和电磁波的能力是制造雷达中复杂结构弱刚度功能件——波导组件的理想材料,但其高质量的切削加工技术研究较少。高速切削加工技术研究虽然近年来受到广泛重视,但在切削机理、刀具材料、切削参数优化以及切削加工数据库等方面还需要进一步研究,目前高速切削加工技术已经列入了2006-2020年国家中长期科学和技术发展规划。本文采用高速铣削加工技术从铣削力、表面质量、切削参数优化以及弱刚度构件变形控制等方面对防锈铝合金弱刚度复杂构件加工技术进行了系统研究,为此类材料的应用与推广提供了技术支持,具有广阔的应用前景和重要的实际意义。
通过对铣削过程的分析建立了三维铣削分力的理论预测模型,并根据单齿铣削过程中剪切面的面积变化规律对铣削力进行了分类。对防锈铝合金AlMn1Cu进行了高速铣削加工试验,对采集的铣削力信号特征进行时域和频域分析,得到了铣削力信号的变化规律,并采用扫面电子显微镜对高速切削表面形貌进行观察与分析,获得了铣削表面形成机理和加工表面形貌的典型特征。
采用单因素试验法、析因试验法以及均匀试验法对AlMn1Cu材料进行了高速铣削加工试验。通过对单因素试验结果分析得到了铣削力和表面粗糙度随切削参数的单因素变化规律,析因试验结果得到了影响高速铣削力和表面粗糙度的重要效应因素,结果表明背吃刀量的影响最显著,而均匀试验结果进一步说明了铣削力和表面粗糙度的变化趋势。从直观分析的结果得到了获得最小表面粗糙度的切削参数组合,并通过试验进行了验证。采用偏最小二乘回归法建立了基于切削参数的铣削力和表面粗糙度预测模型,提高了模型的预测精度。
建立了基于切削参数的铣削力和表面粗糙度神经网络预测模型,在此模型的基础上建立了以最高加工效率为目标并以铣削力和表面粗糙度等技术要求为约束条件的切削参数优化数学模型,并提出了一种采用均匀试验设计的初始化种群技术以及无重复个体的稳态繁殖机制的模拟退火遗传混合优化算法,将该算法应用到切削参数优化计算中取得了较好效果。最终在上述技术的支持下建立了切削参数优化系统,并应用该系统提供了不同表面粗糙度技术要求下的最优切削参数组合。
采用有限元技术和加工试验相结合的方法得到了不同切削路径下弱刚度典型结构(薄壁、超薄腹板和微型孔／槽)的加工变形规律,并从切削参数和刀具的优化选择、切削路径和夹具的优化设计以及加工前后工件的处理等方面提出了控制与减小弱刚度构件加工变形的总体策略。
最后,应用上述研究成果进行了薄壁、雷达波导组件等弱刚度典型构件的高质量切削加工,质量检测结果表明工件的表面粗糙度、形位精度和尺寸精度均达到了技术要求,很好的解决了此类零件的加工问题,同时也为其它弱刚度构件的精密加工提供了参考。
The functional parts with anti-rust aluminum alloy are used in radar with the fine characteristic to reflect visible light, heat and electromagnetic waves. In recent years, the research of high-speed machining technology has been widely carried out. But the further research is needed in the the material removal mechanism, tool material, cutting parameters optimization and machining databases, etc. In this paper, by use of high-speed milling technology, the systematic study was carried out on the weak rigidity workpieces processing techniques form the milling force, surface roughness, cutting parameters optimization and machining deformation control. The results have provided technical support to the application of the material, and this research has a broad application value.
The theoretical prediction model of three-dimensional milling forces, based on the milling process, was presented. According to the variation of area of shear plane during the single-tooth milling process, the transient milling force was classified. The high-speed milling experiments were carried out, and the workpieces materials is AlMn1Cu. According to the characteristics of cutting forces in the time domain and frequency domain, the change regularity of transient milling force was presented. The cutting mechanism and the typical characteristics of machined surface topography were presented by using scanning electron microscopy.
The single-factor, factorial and uniformity test were employed to carry out the high-speed milling experiments for AlMn1Cu. The results of single-factor experiments for AlMn1Cu show the variation of milling force and surface roughness with a certain cutting parameter such as cutting speed, feed-per-tooth and depth of cut. According to the analysis of variance (ANOVA) of factorial experiments, the cutting parameters significantly influencing on the milling forces and the surface roughness were presented. The results show the depth-of-cut is the most statistical significant factor influencing on the milling forces and the surface roughness. The range analysis of factorial experiment indicates the cutting prarameters which the minimum surface roughness is achieved. The predictive mathematic models of milling forces and surface roughness based on the cutting parameters were established by using the partial least-squares regression (PLS), the prediction accuracy of forecasting model is enhanced.
The artificial neural network approach is presented for establishing the predictive models of milling forces and surface roughness based on the cutting parameters. In order to achieve the maximum material removal rate, the optimization model of cutting parameters which the constraints are the technical requirements such as the milling forces and surface roughness is built. A genetic algorithm based on simulated annealing is employed to find the optimum cutting parameters leading to maximum material removal rate in the different range of the technical requirements.
The finite-element method along with the milling force and toolpath was developed to analyze the machining deformation in peripheral milling of a typical weak rigidity structure such as thin-wall, ultra-thin web and micro-holes/slots. The method was proposed to reduce machining deformation of weak rigidity workpiece from the optimum cutting parameters, tool selection, toolpath arrangement and fixture design.
At last, the maching technologies presented in this dissertation have been applied successfully in the practical productions of weak rigidity workpieces, and the testing results of the machined workpieces show that the design technical requirements are met in the machined surface roughness, error of shape and position and dimensional accuracy.
通过对铣削过程的分析建立了三维铣削分力的理论预测模型,并根据单齿铣削过程中剪切面的面积变化规律对铣削力进行了分类。对防锈铝合金AlMn1Cu进行了高速铣削加工试验,对采集的铣削力信号特征进行时域和频域分析,得到了铣削力信号的变化规律,并采用扫面电子显微镜对高速切削表面形貌进行观察与分析,获得了铣削表面形成机理和加工表面形貌的典型特征。
采用单因素试验法、析因试验法以及均匀试验法对AlMn1Cu材料进行了高速铣削加工试验。通过对单因素试验结果分析得到了铣削力和表面粗糙度随切削参数的单因素变化规律,析因试验结果得到了影响高速铣削力和表面粗糙度的重要效应因素,结果表明背吃刀量的影响最显著,而均匀试验结果进一步说明了铣削力和表面粗糙度的变化趋势。从直观分析的结果得到了获得最小表面粗糙度的切削参数组合,并通过试验进行了验证。采用偏最小二乘回归法建立了基于切削参数的铣削力和表面粗糙度预测模型,提高了模型的预测精度。
建立了基于切削参数的铣削力和表面粗糙度神经网络预测模型,在此模型的基础上建立了以最高加工效率为目标并以铣削力和表面粗糙度等技术要求为约束条件的切削参数优化数学模型,并提出了一种采用均匀试验设计的初始化种群技术以及无重复个体的稳态繁殖机制的模拟退火遗传混合优化算法,将该算法应用到切削参数优化计算中取得了较好效果。最终在上述技术的支持下建立了切削参数优化系统,并应用该系统提供了不同表面粗糙度技术要求下的最优切削参数组合。
采用有限元技术和加工试验相结合的方法得到了不同切削路径下弱刚度典型结构(薄壁、超薄腹板和微型孔／槽)的加工变形规律,并从切削参数和刀具的优化选择、切削路径和夹具的优化设计以及加工前后工件的处理等方面提出了控制与减小弱刚度构件加工变形的总体策略。
最后,应用上述研究成果进行了薄壁、雷达波导组件等弱刚度典型构件的高质量切削加工,质量检测结果表明工件的表面粗糙度、形位精度和尺寸精度均达到了技术要求,很好的解决了此类零件的加工问题,同时也为其它弱刚度构件的精密加工提供了参考。
The functional parts with anti-rust aluminum alloy are used in radar with the fine characteristic to reflect visible light, heat and electromagnetic waves. In recent years, the research of high-speed machining technology has been widely carried out. But the further research is needed in the the material removal mechanism, tool material, cutting parameters optimization and machining databases, etc. In this paper, by use of high-speed milling technology, the systematic study was carried out on the weak rigidity workpieces processing techniques form the milling force, surface roughness, cutting parameters optimization and machining deformation control. The results have provided technical support to the application of the material, and this research has a broad application value.
The theoretical prediction model of three-dimensional milling forces, based on the milling process, was presented. According to the variation of area of shear plane during the single-tooth milling process, the transient milling force was classified. The high-speed milling experiments were carried out, and the workpieces materials is AlMn1Cu. According to the characteristics of cutting forces in the time domain and frequency domain, the change regularity of transient milling force was presented. The cutting mechanism and the typical characteristics of machined surface topography were presented by using scanning electron microscopy.
The single-factor, factorial and uniformity test were employed to carry out the high-speed milling experiments for AlMn1Cu. The results of single-factor experiments for AlMn1Cu show the variation of milling force and surface roughness with a certain cutting parameter such as cutting speed, feed-per-tooth and depth of cut. According to the analysis of variance (ANOVA) of factorial experiments, the cutting parameters significantly influencing on the milling forces and the surface roughness were presented. The results show the depth-of-cut is the most statistical significant factor influencing on the milling forces and the surface roughness. The range analysis of factorial experiment indicates the cutting prarameters which the minimum surface roughness is achieved. The predictive mathematic models of milling forces and surface roughness based on the cutting parameters were established by using the partial least-squares regression (PLS), the prediction accuracy of forecasting model is enhanced.
The artificial neural network approach is presented for establishing the predictive models of milling forces and surface roughness based on the cutting parameters. In order to achieve the maximum material removal rate, the optimization model of cutting parameters which the constraints are the technical requirements such as the milling forces and surface roughness is built. A genetic algorithm based on simulated annealing is employed to find the optimum cutting parameters leading to maximum material removal rate in the different range of the technical requirements.
The finite-element method along with the milling force and toolpath was developed to analyze the machining deformation in peripheral milling of a typical weak rigidity structure such as thin-wall, ultra-thin web and micro-holes/slots. The method was proposed to reduce machining deformation of weak rigidity workpiece from the optimum cutting parameters, tool selection, toolpath arrangement and fixture design.
At last, the maching technologies presented in this dissertation have been applied successfully in the practical productions of weak rigidity workpieces, and the testing results of the machined workpieces show that the design technical requirements are met in the machined surface roughness, error of shape and position and dimensional accuracy.
引文
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