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.     

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。     

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。     

干式车削不同淬硬状态淬硬钢已加工表面粗糙度及三维形貌的试验研究

使用PCBN刀具对5种不同淬硬状态(40±1HRC,45±1HRC,50±1HRC,55±1HRC,60±1HRC)Cr12MoV模具钢进行干式硬态车削试验,揭示了切削速度、走刀量、切削深度、工件硬度对已加工表面粗糙度及三维形貌的影响规律及机理。研究结果表明:与车削硬度为40±1HRC、45±1HRC、60±1HRC的工件相比,以v=50,250,450,650,850m/min车削硬度为50±1HRC、55±1HRC的工件时,切削速度对表面粗糙度的影响较为显著,最小表面粗糙度可达0.569μm。车削60±1HRC的工件时,随切削深度的增大,表面粗糙度值逐渐减小,当a_p0.15mm时,Ra1.00μm;而走刀量的影响规律与其反之,当f0.15mm时,Ra1.00μm。已加工三维形貌表明,车削较软的工件时,由于刀具切削刃后刀面对被高温软化的已加工表面的二次伤害使得三维形貌突起的棱脊模糊不清;车削较硬的工件时,刀-工界面硬质颗粒的犁耕效应及后刀面的小沟槽复制效应,使已加工表面产生小沟槽。     

硬车削淬硬轴承钢GCr15表面粗糙度的试验研究

采用陶瓷刀具进行淬硬轴承钢GCr15的硬车削加工试验,并通过正交试验分析和方差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,分析切削速度、进给量和刀尖圆弧半径对表面粗糙度的影响规律。试验与仿真分析表明,增大刀尖圆弧半径可有效降低已加工表面的表面粗糙度,而提高切削速度可使表面粗糙度略有下降;当进给量增大时,表面粗糙度几乎线性增加。同时,进给量对表面粗糙度的影响最大,刀尖圆弧半径次之,而切削速度的影响微弱。     

精密干式硬态车削淬硬工具钢时表面粗糙度的参数优化

使用PCBN刀具对不同淬硬状态工具钢Cr12MoV进行了精密干式硬态车削试验,运用正交实验法分析了切削速度、试件硬度、刀具前角、切削深度4个因素间的交互作用,并得到了最优车削参数.试验表明:影响表面粗糙度最显著的因素是切削速度与淬火硬度,切削深度影响最小.     

硬态干式车削淬硬钢SKD11表面粗糙度试验研究

应用单因素法研究了PCBN 刀具硬态干式切削淬硬钢SKD11过程中,进给量、切削速度、背吃刀量、刀尖圆弧半径和倒棱宽度等参数对表面粗糙度的影响规律.     

硬态切削冷挤压凸模表面粗糙度的试验研究

应用正交试验法研究了硬态切削冷挤压凸模时,切削用量与刀尖圆弧半径对加工表面粗糙度的影响规律,找出了影响加工表面粗糙度的主要因素,得出了最优切削加工参数组合,最后通过了验证试验。     

镍基高温合金锥面外圆车削表面粗糙度优化研究

以GH4169镍基高温合金外圆锥面车削加工为研究对象,通过正交实验的方法,研究切削参数对加工表面粗糙度的影响规律。并运用BP神经网络预测的方法建立了表面粗糙度经验模型。经过实验验证,该模型具有较好的预测精度。另外还对工件表面粗糙度与刀尖圆弧半径及切削深度的关系进行了研究。发现较大的刀尖圆弧半径能获得较小的表面粗糙度值,且增大刀尖圆弧半径可进一步减小切削深度对表面粗糙度的影响。该研究结果可为GH4169圆锥面车削加工提供技术指导和理论支持。     

高速硬切削参数对切削力影响的数值模拟

建立了淬硬钢高速切削的有限元模型,通过Johnson-Cook（JC）工件材料模型及JC失效准则来模拟切屑的形成过程;并研究了背吃刀量、刀具前角和刀尖圆弧半径等参数对切削力的影响规律.     

H13淬硬模具钢精车过程的数值模拟

采用热力学耦合有限元方法研究了淬硬钢精车过程中切屑形成规律。运用H13 淬硬模具钢流动应力模型进行数值模拟,考查了H13淬硬模具钢精车过程中工艺参数对工件性能和刀具的影响。结果表明:切削速度愈高,进给量愈小,刀具刀尖半径愈大,则工件加工层上的静水拉应力愈小,表面质量愈好; 淬硬钢精车时径向力起主要作用,大于切削力;切削速度愈大,切削力和径向力则愈小,愈有助于改善工件加工层上的表面质量;切削速度、进给量和刀具刀尖圆角半径愈大,工件和刀具温度愈高,愈易导致刀具前刀面扩散磨损和刀具后刀面磨损。研究结论有助于优化H13淬硬模具钢精车过程中工艺参数选择和改进刀具镶片设计。     

Experimental investigation of the three-component forces in finish dry hard turning of hardened tool steel at different hardness levels

In this paper, the effects of cutting speed, depth of cut, feed, workpiece hardness (51, 55, 58, 62, and 65?±?1 HRC), tool flank wear, and nose radius on three-component forces in finish dry hard turning (FDHT) of the hardened tool steel AISI D2 were experimentally investigated by utilizing the PCBN inserts. Experimental results showed that the feed force is the lowest in three-component forces and influence of cutting parameters on it is less than two others in the FDHT of AISI D2. Values of the radial force are higher than those of the cutting force when cutting speed, depth of cut, and feed range from 75 to 301 m/min, and 0.10 to 0.40 and 0.05 to 0.20 mm, respectively, but lower in the range between 0.8- and 1.6-mm nose radius. Values of the cutting force are higher than those of the radial force as the workpiece hardness varies from 51 to 58?±?1 HRC while lower in the range between 62 and 65?±?1 HRC. Besides, there are relations between the changing laws of three-component forces and the softening effect of chip, cohesion effect in the tool–chip junction zone, and intenerating effect of metal in the workpiece surface. The high flank wear formation increases the contact with workpiece surface and hence induces tearing–drawing and welding effect duo to instantaneous high temperature.     

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.     

Analysis of surface roughness and cutting force components in hard turning with CBN tool: Prediction model and cutting conditions optimization

In this study, the effects of cutting speed, feed rate, workpiece hardness and depth of cut on surface roughness and cutting force components in the hard turning were experimentally investigated. AISI H11 steel was hardened to (40; 45 and 50) HRC, machined using cubic boron nitride (CBN 7020 from Sandvik Company) which is essentially made of 57% CBN and 35% TiCN. Four-factor (cutting speed, feed rate, hardness and depth of cut) and three-level fractional experiment designs completed with a statistical analysis of variance (ANOVA) were performed. Mathematical models for surface roughness and cutting force components were developed using the response surface methodology (RSM). Results show that the cutting force components are influenced principally by the depth of cut and workpiece hardness; on the other hand, both feed rate and workpiece hardness have statistical significance on surface roughness. Finally, the ranges for best cutting conditions are proposed for serial industrial 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。     

Force modelling in shallow cuts with large negative rake angle and large nose radius tools—application to hard turning

In finish turning, the applied feedrate and depth of cut are generally very small. In some particular cases, such as the finishing of hardened steels, the feedrate and depth of cut are much smaller than tool nose radius. If a tool with a large tool nose radius and large negative rake angle is used in finish turning, the ploughing effect is pronounced and needs to be carefully addressed. Unfortunately, the ploughing effect has not yet been systematically considered in force modelling in shallow cuts with large negative rake angle and large nose radius tools in 3-D oblique cutting. In this study, in order to model the forces in such shallow cuts, first the chip formation forces are predicted by transforming the 3-D cutting geometry into an equivalent 2-D cutting geometry, then the ploughing effect mechanistic model is proposed to calculate the total 2-D cutting forces. Finally, the 3-D cutting forces are estimated by a geometric transformation. The proposed approach is verified in the turning of hardened 52100 steel, in which cutting conditions are typified as shallow cuts with negative rake angle and large nose radius tools. The workpiece material property of hardened 52100 steel is represented by the Johnson-Cook equation, which is determined from machining tests. The comparison between the experimental results and the model predictions is presented.     

PCBN刀具硬态切削淬硬轴承钢GCr15表面粗糙度试验与预测

对典型淬硬轴承钢GCr15进行了不同切削用量和不同PCBN刀具几何参数条件下的表面粗糙度切削试验。试验分析表明,进给量和刀尖圆弧半径是影响表面粗糙度的主要因素,背吃刀量对表面粗糙度影响较小,但背吃刀量和切削速度的交互作用对粗糙度有显著影响。运用反应曲面法（RSM）建立了硬态切削表面粗糙度预测模型,通过试验验证了预测模型的准确性。     

Effects of cutting edge geometry, workpiece hardness, feed rate and cutting speed on surface roughness and forces in finish turning of hardened AISI H13 steel

In this study, the effects of cutting edge geometry, workpiece hardness, feed rate and cutting speed on surface roughness and resultant forces in the finish hard turning of AISI H13 steel were experimentally investigated. Cubic boron nitrite inserts with two distinct edge preparations and through-hardened AISI H13 steel bars were used. Four-factor (hardness, edge geometry, feed rate and cutting speed) two-level fractional experiments were conducted and statistical analysis of variance was performed. During hard turning experiments, three components of tool forces and roughness of the machined surface were measured. This study shows that the effects of workpiece hardness, cutting edge geometry, feed rate and cutting speed on surface roughness are statistically significant. The effects of two-factor interactions of the edge geometry and the workpiece hardness, the edge geometry and the feed rate, and the cutting speed and feed rate also appeared to be important. Especially honed edge geometry and lower workpiece surface hardness resulted in better surface roughness. Cutting-edge geometry, workpiece hardness and cutting speed are found to be affecting force components. The lower workpiece surface hardness and honed edge geometry resulted in lower tangential and radial forces.