颗粒增强金属基复合材料加工过程优化
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摘要
金属基复合材料与超合金，陶瓷、塑料和重构刚相比，具有比强度和比刚度高、耐磨性好以及高温下易操作等许多良好的性能。然而，由于此材料中存在着大量的增强相材料，这些增强相材料的硬度比常用的高速钢和硬质合金刀具要高。在金属基复合材料加工过程中，增强相材料会加剧刀具的快速磨损，导致其加工性能差以及加工成本高，使金属基复合材料的加工面临着极大的挑战，因此限制了这类材料的广泛应用。
本论文的主要研究目标是采用建模和实验相结合的方法力求获得金属基复合材料的最优切削条件，包括切削速度、进给速度、切削深度以及刀具材料；本文还对表面粗糙度、切削力、尺寸精度、轮廓分形维数以及切屑形成进行了研究；并分别建立了其优化的参数模型。实验所用的材料为Al2124/SiCp(45%wt)复合材料，其中增强相颗粒尺寸为5-8μm，加工样件尺寸为直径31.8mm×长78.0mm的棒料。
实验中使用的机床为CG6125C精密车床，该车床的可加工工件的半径为250mm，长度为500mm；所用的切削条件为切削速度：40m/min，60m/min，80m/min和100m/min；进料速度：0.025mm/rev，0.05mm/rev，0.1mm/rev，0.15mm/rev和0.2mm/rev；切削深度：0.1mm，0.2mm和0.3mm。采用响应曲面法和田口设计来预测加工Al2124SiCp (45%wt)过程中的输出目标特性，即轮廓分形维数，直径误差，圆度和表面粗糙度，并分别对其进行了建模与优化；并使用Thirdwave AdvanEdge仿真软件对切屑形成和切削力进行了仿真研究。
研究结果表明：切屑的形成和切削力的仿真结果与实验结果一致，由于不可控的实验条件和加工环境所引起的，使得测得的切削力实验结果高于仿真结果。采用响应曲面法所优化、建模并预测的分形尺寸，表面粗糙度，直径误差和圆度的结果与实验结果比较吻合。由于金属基复合材料加工过程很复杂，不可能同时优化所有的目标响应。在所用实验条件下，使用PCD刀具在切削速度为80m/min，切削深度为0.1mm以及进给速度为0.1mm/rev条件下，对金属基复合材料加工时能得到较高的分形维数值。与其他刀具相比，在所用切削参数下，PCD刀具的刀具磨损量和表面粗糙度均最小，提高切削速度会较低加工表面粗糙度。此外，使用PCD刀具获得的直径和圆度误差也较小，基于组合响应曲面法优化的最佳切削参数为：使用PCD刀具在切削速度为0.1mm，进给速度为0.1232mm/rev和切削速度为71.5152m/min条件下进行切削，并且通过确认实验得到的结果和预测结果比较接近。
在后续的研究中，作者建议对超声振动辅助切削颗粒增强铝基复合材料的分形维数进行分析，并阐述其表面形貌的形成机制，以及该加工过程对尺寸精度（例如圆柱和锥形工件的圆度）的影响。此外，在加工含有不同增强相颗粒体积分数的金属基复合材料时，为获得最优的加工条件，还需要对其建模过程进行更深入的研究。
Present generation of recreation, manufacturing and transport industries amongothers require improved performance to meet the expected targets. As technologicaladvancement expands at a geometrical progression there is need to produce productsthat match this shifting development. Such development cannot be met by monolithicalloys that have previously been used in the industry for some specialized applications.Metal matrix composites present attractive properties such as high specific strength,wear resistance and stiffness coupled with the ability to operate at elevatedtemperatures and compete with super-alloys, ceramics, and redesigned steel. However,their machining presents a significant challenge since a number of reinforcementmaterials are harder than commonly used high-speed steel and carbide tools. Thereinforcement causes rapid abrasive tool wear which impinges its wide applicationdue to poor machinability and consequent high machining costs.
The main objective of this research is to find optimum cutting conditions formachining particulate MMCs. In this research both modeling and experimentaltechniques were used to arrive at the optimum conditions, which include cutting speed,feed rate, depth of cut, and tool material. The study also looked into surface finish,machining forces, dimensional accuracy, profile fractal dimension, and chip formation.This led to developing a model for the optimization of machining condition.Machining investigations were carried out using Al2124SiCp (45%wt) MMC wherethe reinforcement particles size ranged between5and8μm. The material used for thetest was in the form of a bar31.8mm in diameter and78.0mm in length. This materialwas selected due to its relatively low machinability anticipating larger differentiationsin quality characteristics.
The machining of the MMC was performed at four different cutting speeds of40,60,80, and100m/min. The feed rates were0.025,0.05,0.1,0.15and0.2mm/revwhile depths of cut were0.1,0.2and0.3mm. The lathe used for the turning test wasHigh Precision Lathe Model No: CG6125C with a span radius of250mm and lengthof500mm. Modeling of optimization was carried out using Response surfacemethodology and Taguchi approach to predict a number of aspects in machinability ofAl2124SiCp (45%wt) MMC namely, profile fractal dimension, diameter error,circularity and surface roughness. Finite Element Modeling was also carried out usingThirdwave AdvantEdge simulation software to model chip formation and machiningforces of Al2124SiCp (45%wt) MMC as an Equivalent Homogeneous Material.
Results reveal that Thirdwave AdvantEdge simulation on chip formation andmachining forces agreed strongly with experimental findings. The variation inmagnitude of the machining forces were attributed to machining conditions andenvironment that could not be controlled hence a higher value of experimental forcewas recorded as compared to simulated machining force. On response surfaceoptimization modeling the predicted and experimental values on profile fractaldimension, surface roughness, diameter error and circularity showed a high degree ofcorrelation. It was observed that it was not possible to optimize all the responsessimultaneously due to the complexity involved. To achieve high profile fractaldimension values the best combination was cutting speed80m/min and use of PCDtool at depth of cut0.1mm and feed rate0.1mm/rev. On tool wear PCD tool showedthe best results at all cutting parameters. In relation to surface roughness PCD toolsshowed profound performance where improvement was observed with increase inmachining speed. On dimensional accuracy it was observed that PCD tool performedbetter by giving low diameter and circularity values. On combined responseoptimization, the parameters selected were depth of cut0.1mm, feed rate0.1232mm/rev, cutting speed71.5152m/min and PCD cutting tool. Verificationresults showed good relationship between predicted and experimental results.
For further research the researcher suggests analysis of profile fractal dimensionin relation to ultrasonic vibration assisted machining (UVAM) in machining MMCs.This would shed more light and the surface topography of surfaces produced throughUVAM. Assesment of dimensional accuracy components as cylindricity andcircularity of tappered work. Further modeling in optimization of cutting componentsfor MMCs having varying amount of reinforcement to achieve optimal machiningparameters.
本论文的主要研究目标是采用建模和实验相结合的方法力求获得金属基复合材料的最优切削条件，包括切削速度、进给速度、切削深度以及刀具材料；本文还对表面粗糙度、切削力、尺寸精度、轮廓分形维数以及切屑形成进行了研究；并分别建立了其优化的参数模型。实验所用的材料为Al2124/SiCp(45%wt)复合材料，其中增强相颗粒尺寸为5-8μm，加工样件尺寸为直径31.8mm×长78.0mm的棒料。
实验中使用的机床为CG6125C精密车床，该车床的可加工工件的半径为250mm，长度为500mm；所用的切削条件为切削速度：40m/min，60m/min，80m/min和100m/min；进料速度：0.025mm/rev，0.05mm/rev，0.1mm/rev，0.15mm/rev和0.2mm/rev；切削深度：0.1mm，0.2mm和0.3mm。采用响应曲面法和田口设计来预测加工Al2124SiCp (45%wt)过程中的输出目标特性，即轮廓分形维数，直径误差，圆度和表面粗糙度，并分别对其进行了建模与优化；并使用Thirdwave AdvanEdge仿真软件对切屑形成和切削力进行了仿真研究。
研究结果表明：切屑的形成和切削力的仿真结果与实验结果一致，由于不可控的实验条件和加工环境所引起的，使得测得的切削力实验结果高于仿真结果。采用响应曲面法所优化、建模并预测的分形尺寸，表面粗糙度，直径误差和圆度的结果与实验结果比较吻合。由于金属基复合材料加工过程很复杂，不可能同时优化所有的目标响应。在所用实验条件下，使用PCD刀具在切削速度为80m/min，切削深度为0.1mm以及进给速度为0.1mm/rev条件下，对金属基复合材料加工时能得到较高的分形维数值。与其他刀具相比，在所用切削参数下，PCD刀具的刀具磨损量和表面粗糙度均最小，提高切削速度会较低加工表面粗糙度。此外，使用PCD刀具获得的直径和圆度误差也较小，基于组合响应曲面法优化的最佳切削参数为：使用PCD刀具在切削速度为0.1mm，进给速度为0.1232mm/rev和切削速度为71.5152m/min条件下进行切削，并且通过确认实验得到的结果和预测结果比较接近。
在后续的研究中，作者建议对超声振动辅助切削颗粒增强铝基复合材料的分形维数进行分析，并阐述其表面形貌的形成机制，以及该加工过程对尺寸精度（例如圆柱和锥形工件的圆度）的影响。此外，在加工含有不同增强相颗粒体积分数的金属基复合材料时，为获得最优的加工条件，还需要对其建模过程进行更深入的研究。
Present generation of recreation, manufacturing and transport industries amongothers require improved performance to meet the expected targets. As technologicaladvancement expands at a geometrical progression there is need to produce productsthat match this shifting development. Such development cannot be met by monolithicalloys that have previously been used in the industry for some specialized applications.Metal matrix composites present attractive properties such as high specific strength,wear resistance and stiffness coupled with the ability to operate at elevatedtemperatures and compete with super-alloys, ceramics, and redesigned steel. However,their machining presents a significant challenge since a number of reinforcementmaterials are harder than commonly used high-speed steel and carbide tools. Thereinforcement causes rapid abrasive tool wear which impinges its wide applicationdue to poor machinability and consequent high machining costs.
The main objective of this research is to find optimum cutting conditions formachining particulate MMCs. In this research both modeling and experimentaltechniques were used to arrive at the optimum conditions, which include cutting speed,feed rate, depth of cut, and tool material. The study also looked into surface finish,machining forces, dimensional accuracy, profile fractal dimension, and chip formation.This led to developing a model for the optimization of machining condition.Machining investigations were carried out using Al2124SiCp (45%wt) MMC wherethe reinforcement particles size ranged between5and8μm. The material used for thetest was in the form of a bar31.8mm in diameter and78.0mm in length. This materialwas selected due to its relatively low machinability anticipating larger differentiationsin quality characteristics.
The machining of the MMC was performed at four different cutting speeds of40,60,80, and100m/min. The feed rates were0.025,0.05,0.1,0.15and0.2mm/revwhile depths of cut were0.1,0.2and0.3mm. The lathe used for the turning test wasHigh Precision Lathe Model No: CG6125C with a span radius of250mm and lengthof500mm. Modeling of optimization was carried out using Response surfacemethodology and Taguchi approach to predict a number of aspects in machinability ofAl2124SiCp (45%wt) MMC namely, profile fractal dimension, diameter error,circularity and surface roughness. Finite Element Modeling was also carried out usingThirdwave AdvantEdge simulation software to model chip formation and machiningforces of Al2124SiCp (45%wt) MMC as an Equivalent Homogeneous Material.
Results reveal that Thirdwave AdvantEdge simulation on chip formation andmachining forces agreed strongly with experimental findings. The variation inmagnitude of the machining forces were attributed to machining conditions andenvironment that could not be controlled hence a higher value of experimental forcewas recorded as compared to simulated machining force. On response surfaceoptimization modeling the predicted and experimental values on profile fractaldimension, surface roughness, diameter error and circularity showed a high degree ofcorrelation. It was observed that it was not possible to optimize all the responsessimultaneously due to the complexity involved. To achieve high profile fractaldimension values the best combination was cutting speed80m/min and use of PCDtool at depth of cut0.1mm and feed rate0.1mm/rev. On tool wear PCD tool showedthe best results at all cutting parameters. In relation to surface roughness PCD toolsshowed profound performance where improvement was observed with increase inmachining speed. On dimensional accuracy it was observed that PCD tool performedbetter by giving low diameter and circularity values. On combined responseoptimization, the parameters selected were depth of cut0.1mm, feed rate0.1232mm/rev, cutting speed71.5152m/min and PCD cutting tool. Verificationresults showed good relationship between predicted and experimental results.
For further research the researcher suggests analysis of profile fractal dimensionin relation to ultrasonic vibration assisted machining (UVAM) in machining MMCs.This would shed more light and the surface topography of surfaces produced throughUVAM. Assesment of dimensional accuracy components as cylindricity andcircularity of tappered work. Further modeling in optimization of cutting componentsfor MMCs having varying amount of reinforcement to achieve optimal machiningparameters.
引文
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