典型钛合金TC17车削过程鳞刺生成规律及其抑制措施

为研究钛合金车削过程中鳞刺生成规律及有效抑制措施,分析了影响鳞刺生成的主要因素,采用弯矩法解析了鳞刺折断规律,进而建立了切削参数、刀具几何参数与刀尖弯矩的数学描述模型;通过MATLAB对模型进行求解,获知切削速度对刀尖弯矩的影响最小,而切削深度、进给量、刀尖圆弧以及刀具主偏角4个因素决定了刀尖弯矩的大小。为验证描述模型的正确性,进行了典型钛合金TC17外圆周断续切削实验,采集在恒定切削速度、不同切削深度、不同进给量、不同主偏角及不同刀尖圆弧条件下的鳞刺样本数据,并获得鳞刺折断规律曲线。实验结果表明:在小于临界切削深度和大于临界进给量条件下,实验结果与数学描述模型整体趋势一致,证明了数学描述模型的正确性。研究结果可为钛合金的高品质加工提供工艺技术及刀具优选方面的数据支撑。     

陶瓷零件的机械切削加工

用数据包络分析法评价陶瓷的可加工性,构造数学规划模型,求出决策单元的最优解。结合陶瓷材料的机械加工难点,探讨了陶瓷车削、磨削、钻削等机械切削加工工艺的技术要点。通过切削正交试验,得到刀具的前角、后角、主偏角、副偏角、刃倾角、刀尖圆弧半径及切削速度、吃刀深度、进给量的参考值,优化加工工艺参数,达到较好的加工效果。     

淬硬钢高速硬车削表面粗糙度的试验研究

采用PCBN刀具进行高速硬车削AISI P20淬硬钢的切削试验,并通过正交试验分析给出试验范围内的最优加工参数组合。基于所建立的表面粗糙度经验模型,采用数值仿真的方法分析切削速度、进给量、切削深度和刀尖圆弧半径对表面粗糙度的影响规律。结果表明,增大切削速度和刀尖圆弧半径可有效降低表面粗糙度,而当进给量增大时,表面粗糙度显著增加;同时,进给量对表面粗糙度的影响最大,刀尖圆弧半径次之,切削速度也有较大影响,而切削深度的影响则非常微弱。     

基于神经网络的高速硬车削切削力预测研究

采用PCBN刀具进行高速硬车削AISI P20淬硬钢的切削试验,并通过方差分析研究切削速度、进给量、切削深度和刀尖圆弧半径对切削力的影响.基于获得的试验数据,应用人工神经网络方法建立高速硬车削P20淬硬钢时的切削力预测模型.试验与仿真分析显示,切削力随进给量、切削深度和刀尖圆弧半径的增加而增大,而不同切削速度下的切削力值几乎保持不变;同时,切削深度对切削力的影响最为显著,其次为进给量,再次为刀尖圆弧半径,而切削速度的影响则非常微弱.     

切削用量对车削Ti6Al4V切削力影响的研究

通过硬质合金刀具高速干切削Ti6Al4V钛合金的试验,分析了切削用量对切削力的影响.试验结果表明:在切削三要素中,切削深度和进给量对切削力的影响较大,切削速度对于切削力的影响较小.进给量对背向力的影响最大,切削深度对进给力的影响最大.刀尖圆弧半径对于进给力和背向力的变化规律有重要影响.     

硬车削滚动轴承套圈的试验研究

针对滚动轴承套圈硬车削加工过程中表面质量存在的问题,对硬车削过程中切削用量和刀具参数对表面粗糙度的影响进行了研究,采用CBN刀具进行了6205滚动轴承套圈的硬车削加工试验,将进给量、切削速度、切削深度和刀尖圆弧半径作为试验因子,通过正交试验分析了它们对零件加工后表面粗糙度的影响规律,并归纳出了该试验范围内的最佳切削用量和刀具参数组合。研究结果表明,进给量对表面粗糙度的影响最大,刀尖圆弧半径对表面粗糙度的影响次之,切削速度对表面粗糙度的有一定影响,切削深度对表面粗糙度的影响非常小。     

超声椭圆振动切削硬质合金脆性-延性转变临界条件研究

为了实现硬质合金的高效延性加工,联系硬脆材料表面生成裂纹的临界载荷与超声椭圆振动切削硬质合金的主切削力,建立超声椭圆振动切削硬质合金脆性-延性转变的临界切削深度模型,研究切削速度、刀具圆弧半径、椭圆振动频率、振幅、硬质合金的硬度、断裂韧性与临界切削深度的关系;通过仿切削刻划试验,验证了切削速度与硬质合金的硬度、断裂韧性对临界切削深度的影响规律;对比普通切削,超声椭圆振动切削有利于提高硬质合金的临界切削深度,在改善加工表面质量及精度的前提下,提高了加工效率。     

切削用量对立铣加工钛合金Ti6Al4V切削力和切削温度影响规律的有限元仿真研究

利用软件AdvantEdge建立硬质合金涂层立铣刀三维铣削Ti6Al4V钛合金的有限元分析模型,模拟分析切削参数(切削速度、每齿进给量、轴向切削深度及径向切削深度)对刀具切削温度、切削力的影响规律.研究发现:切削力及切削温度随每齿进给量、轴向切削深度及径向切削深度的增加而增加:切削温度对切削深度的变化较敏感,随轴向切削深度和径向切削深度的增加显著增加;随着切削速度的增加,切削力先增大后减小,临界切削速度为120 m/min.     

切削用量对立铣加工钛合金Ti6Al4V切削力和切削温度影响规律的有限元仿真研究

利用软件AdvantEdge建立硬质合金涂层立铣刀三维铣削Ti6Al4V钛合金的有限元分析模型,模拟分析切削参数(切削速度、每齿进给量、轴向切削深度及径向切削深度)对刀具切削温度、切削力的影响规律。研究发现:切削力及切削温度随每齿进给量、轴向切削深度及径向切削深度的增加而增加:切削温度对切削深度的变化较敏感,随轴向切削深度和径向切削深度的增加显著增加;随着切削速度的增加,切削力先增大后减小,临界切削速度为120m/min。     

基于圆弧刃刀具的超精密切削分析

分析圆弧刃刀具切入过程、切削刃钝圆和进给量对加工质量的影响.对金刚石刀具的刀尖圆弧半径、圆弧切削刃钝圆半径、进给量与表面粗糙度之间的关系进行描述.结果表明:正确地选择刀尖圆弧半径、切削刃钝圆半径和进给量是获得高质量加工表面的有力保证.     

高速车削镍基高温合金GH4169的切削力仿真研究

基于Deform 3D仿真软件建立了GH4169高温合金高速车削的有限元模型,采用四因素三水平正交试验方法研究了切削用量和刀具几何参数对切削力的影响规律,并建立了切削力经验公式。研究结果表明:在高速车削GH4169的过程中,对切削力影响最大的参数是切削深度,其次是进给量和前角,最后是刀尖圆弧半径;切削力随切削深度和进给量的增大而增大,随前角的增大呈现先降低又升高的趋势,而刀尖圆弧半径增大时切削力变化不大;最佳参数组合为:进给量0.2mm/r,切削深度0.4mm,前角10°,刀尖圆弧半径0.2mm。     

Optimisation of machining parameters for turning operations based on response surface methodology

Design of experiments has been used to study the effect of the main turning parameters such as feed rate, tool nose radius, cutting speed and depth of cut on the surface roughness of AISI 410 steel. A mathematical prediction model of the surface roughness has been developed in terms of above parameters. The effect of these parameters on the surface roughness has been investigated by using Response Surface Methodology (RSM). Response surface contours were constructed for determining the optimum conditions for a required surface roughness. The developed prediction equation shows that the feed rate is the main factor followed by tool nose radius influences the surface roughness. The surface roughness was found to increase with the increase in the feed and it decreased with increase in the tool nose radius. The verification experiment is carried out to check the validity of the developed model that predicted surface roughness within 6% error.     

FUZZY CLUSTERING MODELLING FOR SURFACE FINISH PREDICTION IN FINE TURNING PROCESS

ABSTRACT

In this paper, fuzzy subtractive clustering based system identification and Sugeno type fuzzy inference system are used to model the surface finish of the machined surfaces in fine turning process to develop a better understanding of the effect of process parameters on surface quality. Such an understanding can provide insight into the problems of controlling the quality of the machined surface when the process parameters are adjusted to obtain certain characteristics. Surface finish data were generated for aluminum alloy 390 (73 BHN), ductile cast iron (186 BHN), and inconel 718 (BHN 335) for a wide range of machining conditions defined by cutting speed, cutting feed rate and cutting tool nose radius. These data were used to develop a surface finish prediction fuzzy clustering model as a function of hardness of the machined material, cutting speed, cutting feed rate, and cutting tool nose radius. Surface finish of the machined part is the output of the process. The model building process is carried out by using fuzzy subtracting clustering based system identification in both input and output space. Minimum error is obtained through numerous searches of clustering parameters. The fuzzy logic model is capable of predicting the surface finish for a given set of inputs (workpiece hardness, cutting speed, cutting feed rate and nose radius of the cutting tool). As such, the machinist may predict the quality of the surface for a given set of working parameters and may also set the process parameters to achieve a certain surface finish. The model is verified experimentally by further experimentation using different sets of inputs. This study deals with the experimental results obtained during fine turning operation. The findings indicate that while the effects of cutting feed and tool nose radius on surface finish were generally consistent for all materials, the effect of cutting speed was not. The surface finish improved for aluminum alloy and ductile cast iron but it deteriorated with speed for inconel.     

FUZZY CLUSTERING MODELLING FOR SURFACE FINISH PREDICTION IN FINE TURNING PROCESS

In this paper, fuzzy subtractive clustering based system identification and Sugeno type fuzzy inference system are used to model the surface finish of the machined surfaces in fine turning process to develop a better understanding of the effect of process parameters on surface quality. Such an understanding can provide insight into the problems of controlling the quality of the machined surface when the process parameters are adjusted to obtain certain characteristics. Surface finish data were generated for aluminum alloy 390 (73 BHN), ductile cast iron (186 BHN), and inconel 718 (BHN 335) for a wide range of machining conditions defined by cutting speed, cutting feed rate and cutting tool nose radius. These data were used to develop a surface finish prediction fuzzy clustering model as a function of hardness of the machined material, cutting speed, cutting feed rate, and cutting tool nose radius. Surface finish of the machined part is the output of the process. The model building process is carried out by using fuzzy subtracting clustering based system identification in both input and output space. Minimum error is obtained through numerous searches of clustering parameters. The fuzzy logic model is capable of predicting the surface finish for a given set of inputs (workpiece hardness, cutting speed, cutting feed rate and nose radius of the cutting tool). As such, the machinist may predict the quality of the surface for a given set of working parameters and may also set the process parameters to achieve a certain surface finish. The model is verified experimentally by further experimentation using different sets of inputs. This study deals with the experimental results obtained during fine turning operation. The findings indicate that while the effects of cutting feed and tool nose radius on surface finish were generally consistent for all materials, the effect of cutting speed was not. The surface finish improved for aluminum alloy and ductile cast iron but it deteriorated with speed for inconel.     

硬质合金刀具车削GCr15轴承钢表面粗糙度试验研究

通过硬质合金刀具车削加工GCr15轴承钢试验,验证了切削用量与刀尖圆弧半径rε对表面轮廓算术平均偏差Ra的影响程度。试验表明,进给量f和刀尖圆弧半径rε是影响Ra的最主要因素,V影响其次、ap影响最小。     

Online tool condition monitoring in turning titanium (grade 5) using acoustic emission: modeling

This paper presents an online prediction of tool wear using acoustic emission (AE) in turning titanium (grade 5) with PVD-coated carbide tools. In the present work, the root mean square value of AE at the chip–tool contact was used to detect the progression of flank wear in carbide tools. In particular, the effect of cutting speed, feed, and depth of cut on tool wear has been investigated. The flank surface of the cutting tools used for machining tests was analyzed using energy-dispersive X-ray spectroscopy technique to determine the nature of wear. A mathematical model for the prediction of AE signal was developed using process parameters such as speed, feed, and depth of cut along with the progressive flank wear. A confirmation test was also conducted in order to verify the correctness of the model. Experimental results have shown that the AE signal in turning titanium alloy can be predicted with a reasonable accuracy within the range of process parameters considered in this study.     

Effects of cutting parameters on tool insert wear in end milling of titanium alloy Ti6Al4V

Titanium alloy is a kind of typical hard-to-cut material due to its low thermal conductivity and high strength at elevated temperatures, this contributes to the fast tool wear in the milling of titanium alloys. The influence of cutting conditions on tool wear has been focused on the turning process, and their influence on tool wear in milling process as well as the influence of tool wear on cutting force coefficients has not been investigated comprehensively. To fully understand the tool wear behavior in milling process with inserts, the influence of cutting parameters on tool wear in the milling of titanium alloys Ti6Al4V by using indexable cutters is investigated. The tool wear rate and trends under different feed per tooth, cutting speed, axial depth of cut and radial depth of cut are analyzed. The results show that the feed rate per tooth and the radial depth of cut have a large influence on tool wear in milling Ti6Al4V with coated insert. To reduce tool wear, cutting parameters for coated inserts under experimental cutting conditions are set as: feed rate per tooth less than 0.07 mm, radial depth of cut less than 1.0 mm, and cutting speed sets between 60 and 150 m/min. Investigation on the relationship between tool wear and cutting force coefficients shows that tangential edge constant increases with tool wear and cutter edge chipping can lead to a great variety of tangential cutting force coefficient. The proposed research provides the basic data for evaluating the machinability of milling Ti6Al4V alloy with coated inserts, and the recommend cutting parameters can be immediately applied in practical production.     

高速切削淬硬模具钢表面粗糙度的研究

通过正交设计方案,对淬硬到60HRC的冷作模具钢Cr12MoV进行高速车削表面粗糙度试验,分析了切削用量和刀具变量对表面粗糙度的影响规律,并建立了表面粗糙度的经验公式。表面粗糙度随着切削速度的增大而减小,随着进给量和背吃刀量的增大而增大,随着刀尖圆弧半径的增大,表面粗糙度先减小后增大。在相同条件下,陶瓷刀具加工后的工件表面粗糙度好于PCBN刀具;由表面粗糙度的经验公式可知,对表面粗糙度影响最大的因素是切削速度,其次是进给量和背吃刀量,而刀尖圆弧半径对其影响较小。     

Effects of Cutting Parameters on Tool Insert Wear in End Milling of Titanium Alloy Ti6A14V

Titanium alloy is a kind of typical hard-to-cut material due to its low thermal conductivity and high strength at elevated temperatures, this contributes to the fast tool wear in the milling of titanium alloys. The influence of cutting conditions on tool wear has been focused on the turning process, and their influence on tool wear in milling process as well as the influence of tool wear on cutting force coefficients has not been investigated comprehensively. To fully understand the tool wear behavior in milling process with inserts, the influence of cutting parameters on tool wear in the milling of titanium alloys Ti6Al4 V by using indexable cutters is investigated. The tool wear rate and trends under different feed per tooth, cutting speed, axial depth of cut and radial depth of cut are analyzed. The results show that the feed rate per tooth and the radial depth of cut have a large influence on tool wear in milling Ti6Al4 V with coated insert. To reduce tool wear, cutting parameters for coated inserts under experimental cutting conditions are set as: feed rate per tooth less than 0.07 mm, radial depth of cut less than 1.0 mm, and cutting speed sets between 60 and 150 m/min. Investigation on the relationship between tool wear and cutting force coefficients shows that tangential edge constant increases with tool wear and cutter edge chipping can lead to a great variety of tangential cutting force coefficient. The proposed research provides the basic data for evaluating the machinability of milling Ti6Al4 V alloy with coated inserts, and the recommend cutting parameters can be immediately applied in practical production.     

CUTTING DISTURBANCES INFLUENCED BY VARIATIONS IN CONTACT SURFACE GEOMETRY

The significant cutting disturbances appearing in hard turning processes cause shifting of the process dynamics. Therefore, in this paper the turning process is evaluated by radial force variation analysis, as a function of depth of cut, tool nose radius and effective lead edge angle, through static and dynamic indicators. The tool/workpiece contact zone is, in the case of hard turning, mostly limited within the tool nose radius region. Therefore in this paper, geometry of the tool/workpiece contact line is analyzed. The depth of cut is calculated as a geometric difference of prior and instantaneous tool pass profiles. The calculated values of the depth of cut are time dependant, and can vary by 60%. Various process monitoring techniques have been used to identify and confirm these variations, as well as quantify the level of process stability. The results obtained confirm the assumption that effective lead edge angle and radial force are influenced by depth of cut, feed rate and tool nose radius. Additionally, it is shown that low values of depth of cut and geometry of prior pass-machined surface valleys shift the hard turning process to a dynamically more sensitive level as compared the case of soft machining.