SURFACE ROUGHNESS PREDICTION MODEL FOR ULTRAPRECISION TURNING ALUMINIUM ALLOY WITH A SINGLE CRYSTAL DIAMOND TOOL

A surface roughness model utilizing regression analysis method is developed for predicting roughness of ultra-precision machined surface with a single crystal diamond tool. The effects of the main variables, such as cutting speed, feed, and depth of cut on surface roughness are also analyzed in diamond turning aluminum alloy. In order to predict and control the surface roughness before ultraprecision machining, constrained variable metric method is used to select the optimum cutting conditions during process planning. A lot of experimental results show that the model can predict the surface roughness effectively under a certain cutting conditions .     

On the prediction of surface roughness in the hard turning based on cutting parameters and tool vibrations

This research work concerns the elaboration of a surface roughness model in the case of hard turning by exploiting the response surface methodology (RSM). The main input parameters of this model are the cutting parameters such as cutting speed, feed rate, depth of cut and tool vibration in radial and in main cutting force directions. The machined material tested is the 42CrMo4 hardened steel by Al₂O₃/TiC mixed ceramic cutting tool under different conditions. The model is able to predict surface roughness of Ra and Rt using an experimental data when machining steels. The combined effects of cutting parameters and tool vibration on surface roughness were investigated while employing the analysis of variance (ANOVA). The quadratic model of RSM associated with response optimization technique and composite desirability was used to find optimum values of cutting parameters and tool vibration with respect to announced objectives which are the prediction of surface roughness. The adequacy of the model was verified when plotting the residuals values. The results indicate that the feed rate is the dominant factor affecting the surface roughness, whereas vibrations on both pre-cited directions have a low effect on it. Moreover, a good agreement was observed between the predicted and the experimental surface roughness. Optimal cutting condition and tool vibrations leading to the minimum surface roughness were highlighted.     

Multi response optimisation of CNC turning parameters via Taguchi method-based response surface analysis

This study presents a new method to determine multi-objective optimal cutting conditions and mathematic models for surface roughness (Ra and Rz) on a CNC turning. Firstly, cutting parameters namely, cutting speed, depth of cut, and feed rate are designed using the Taguchi method. The AISI 304 austenitic stainless workpiece is machined by a coated carbide insert under dry conditions. The influence of cutting speed, feed rate and depth of cut on the surface roughness is examined. Secondly, the model for the surface roughness, as a function of cutting parameters, is obtained using the response surface methodology (RSM). Finally, the adequacy of the developed mathematical model is proved by ANOVA. The results indicate that the feed rate is the dominant factor affecting surface roughness, which is minimized when the feed rate and depth of cut are set to the lowest level, while the cutting speed is set to the highest level. The percentages of error all fall within 1%, between the predicted values and the experimental values. This reveals that the prediction system established in this study produces satisfactory results, which are improved performance over other models in the literature. The enhanced method can be readily applied to different metal cutting processes with greater confidence.     

Surface roughness analysis of hardened steel after high-speed milling

The work refers to analysis of various factors affecting surface roughness after end milling of hardened steel in high-speed milling (HSM) conditions. Investigations of milling parameters (cutting speed v(c) , axial depth of cut a(p) ) and the process dynamics that influence machined surface roughness were presented, and a surface roughness model, including cutter displacements, was elaborated. The work also involved analysis of surface profile charts from the point of view of vibrations and cutting force components. The research showed that theoretic surface roughness resulting from the kinematic-geometric projection of cutting edge in the workpiece is significantly different from the reality. The dominant factor in the research was not feed per tooth f(z) (according to the theoretical model) but dynamical phenomena and feed per revolution f.     

Empirical models and optimal cutting parameters for cutting forces and surface roughness in hard milling of AISI H13 steel

In the present research, an attempt has been made to experimentally investigate the effects of cutting parameters on cutting forces and surface roughness in hard milling of AISI H13 steel with coated carbide tools. Based on Taguchi’s method, four-factor (cutting speed, feed, radial depth of cut, and axial depth of cut) four-level orthogonal experiments were employed. Three cutting force components and roughness of machined surface were measured, and then range analysis and analysis of variance (ANOVA) are performed. It is found that the axial depth of cut and the feed are the two dominant factors affecting the cutting forces. The optimal cutting parameters for minimal cutting forces and surface roughness in the range of this experiment under these experimental conditions are searched. Two empirical models for cutting forces and surface roughness are established, and ANOVA indicates that a linear model best fits the variation of cutting forces while a quadratic model best describes the variation of surface roughness. Surface roughness under some cutting parameters is less than 0.25 μm, which shows that finish hard milling is an alternative to grinding process in die and mold industry.     

高硬刀片干切削淬硬GCr15轴承钢的表面粗糙度预测

采用陶瓷刀片和CBN刀片干切削淬硬GCr15轴承钢,测量了不同切削参数下切削后工件的表面粗糙度;基于微粒群优化算法建立了表面粗糙度预测模型,并与线性回归法建立的经验公式进行了比较;用扫描电子显微镜观察了切屑形态。结果表明:采用微粒群优化算法建立的表面粗糙度预测模型具有一定的可靠性,与线性回归法相比,能更精确地预测出加工工件的表面粗糙度;切削参数中对表面粗糙度影响最大的是进给量,其次是背吃刀量,切削速度的影响最小;锯齿状切屑能降低切削温度,提高工件表面质量;用陶瓷刀片和CBN刀片切削获得的最低表面粗糙度分别可达0.48μm和0.56μm。     

Zr基大块非晶合金在过冷液相区超塑性成形的摩擦行为及机理研究

采用双杯挤压方法研究了成形温度、应变速率等工艺参数对Zr55Al10Ni5Cu30块体非晶合金在过冷液相区塑性成形时模具和零件之间的摩擦行为的影响。采用有限元模拟方法获得大块非晶合金双杯挤压的摩擦因数标定曲线,有限元模拟中非晶合金的变形采用Kawamura的本构模型,将高温压缩实验的数据拟合,获得本构模型中的参数,结果表明非晶合金在过冷液相区内变形的摩擦因数在0.2~0.7之间。当应变速率较低时,随着温度的升高,摩擦因数总体上降低;而当应变速率较高时,随着温度的升高,摩擦因数先略有上升,然后急剧下降。当温度较低时,随着应变速率增大,摩擦因数显著增大,而在高温时,随着应变速率增大,摩擦因数略微有所减小。按照现代摩擦理论对非晶合金在过冷液相区内成形的摩擦机理进行了分析,认为黏着是摩擦的主导因素。     

介观尺度心轴的表面粗糙度预测模型建立及参数优化

为控制惯性约束聚变靶制备中介观尺度心轴的表面粗糙度,提出一种应用旋转设计技术安排试验的方法,通过非线性回归分析,建立基于进给量、背吃刀量、主轴转速和刀尖角四个主要切削参数的介观尺度心轴的表面粗糙度二次预测模型。分析结果表明,该模型的拟合值能较好地反映心轴车削表面粗糙度,并且具有比理论表面粗糙度计算值更高的精度。在主要切削参数中,进给量和刀尖角比背吃刀量和主轴转速对心轴表面粗糙度的影响更显著。利用优化得到的最佳表面粗糙度为目标切削条件,选用直线切削刃超细晶粒硬质合金刀具,在φ0.6 mm的心轴上得到Ra16.53 nm的表面粗糙度。     

微细铣削硬铝时切削用量对表面粗糙度的影响

利用自研的三轴微小型立式数控铣床和微径立铣刀(直径小于1mm),通过双因素及中心复合实验设计的方法,分析微细铣削硬铝LY12过程中的铣削参数(包括每齿进给量、轴向切深、主轴转速、刀具直径以及刀具悬伸量)对工件表面粗糙度的影响,重点探讨了每齿进给量和轴向切深对表面粗糙度的交互影响,并建立了数学模型,为微细铣削工艺参数的选择和表面质量的控制提供了基本依据。     

Surface topography and roughness of high-speed milled AlMn1Cu

The aluminum alloy AlMn1Cu has been broadly applied for functional parts production because of its good properties. But few researches about the machining mechanism and the surface roughness were reported. The high-speed milling experiments are carried out in order to improve the machining quality and reveal the machining mechanism. The typical topography features of machined surface are observed by scan electron microscope(SEM). The results show that the milled surface topography is mainly characterized by the plastic shearing deformation surface and material piling zone. The material flows plastically along the end cutting edge of the flat-end milling tool and meanwhile is extruded by the end cutting edge, resulting in that materials partly adhere to the machined surface and form the material piling zone. As the depth of cut and the feed per tooth increase, the plastic flow of materials is strengthened and the machined surface becomes rougher. However, as the cutting speed increases, the plastic flow of materials is weakened and the milled surface becomes smoother. The cutting parameters (e.g. cutting speed, feed per tooth and depth of cut) influencing the surface roughness are analyzed. It can be concluded that the roughness of the machined surface formed by the end cutting edge is less than that by the cylindrical cutting edge when a cylindrical flat-end mill tool is used for milling. The proposed research provides the typical topography features of machined surface of the anti-rust aluminum alloy AlMn1Cu in high speed milling.     

高速铣削SiC_p/Al复合材料表面形貌功率谱密度研究

为了研究切削参数对高速铣削SiCp/Al复合材料表面微观形貌的影响,本文采用不同切削参数进行了高速铣削实验,利用Talyscan150型表面粗糙度测试仪对加工表面进行测量,对获得的表面数据进行功率谱密度(PSD)分析。结果表明:高速铣削SiC颗粒增强铝基复合材料时,进给量与铣削深度对功率谱密度影响不大,切削速度是主要影响因素,并且随着切削速度的增大,功率谱密度值降低,表面质量提高。加工表面的主要空间波长成分能够反映加工工艺条件对加工表面形貌的影响。     

Effect of work material hardness and cutting parameters on performance of coated carbide tool when turning hardened steel: An optimization approach

In present work performance of coated carbide tool was investigated considering the effect of work material hardness and cutting parameters during turning of hardened AISI 4340 steel at different levels of hardness. The correlations between the cutting parameters and performance measures like cutting forces, surface roughness and tool life, were established by multiple linear regression models. The correlation coefficients found close to 0.9, showed that the developed models are reliable and could be used effectively for predicting the responses within the domain of the cutting parameters. Highly significant parameters were determined by performing an Analysis of Variance (ANOVA). Experimental observations show that higher cutting forces are required for machining harder work material. These cutting forces get affected mostly by depth of cut followed by feed. Cutting speed, feed and depth of cut having an interaction effect on surface roughness. Cutting speed followed by depth of cut become the most influencing factors on tool life; especially in case of harder workpiece. Optimum cutting conditions are determined using response surface methodology (RSM) and the desirability function approach. It was found that, the use of lower feed value, lower depth of cut and by limiting the cutting speed to 235 and 144 m/min; while turning 35 and 45 HRC work material, respectively, ensures minimum cutting forces, surface roughness and better tool life.     

Prediction of cutting force, tool wear and surface roughness of Al6061/SiC composite for end milling operations using RSM

The results of mathematical modeling and the experimental investigation on the machinability of aluminium (Al6061) silicon carbide particulate (SiCp) metal matrix composite (MMC) during end milling process is analyzed. The machining was difficult to cut the material because of its hardness and wear resistance due to its abrasive nature of reinforcement element. The influence of machining parameters such as spindle speed, feed rate, depth of cut and nose radius on the cutting force has been investigated. The influence of the length of machining on the tool wear and the machining parameters on the surface finish criteria have been determined through the response surface methodology (RSM) prediction model. The prediction model is also used to determine the combined effect of machining parameters on the cutting force, tool wear and surface roughness. The results of the model were compared with the experimental results and found to be good agreement with them. The results of prediction model help in the selection of process parameters to reduce the cutting force, tool wear and surface roughness, which ensures quality of milling processes.     

Application of GONNS to predict constrained optimum surface roughness in face milling of high-silicon austenitic stainless steel

Surface roughness is a technical requirement for machined products and one of the main product quality specifications. In the present research, a genetically optimized neural network system (GONNS) is proposed for prediction of constrained optimal cutting conditions in face milling of a high-silicon austenitic stainless steel (UNS J93900) in order to minimize surface roughness. In order to attain minimum operation numbers and decrease the cost of machining, an experimental scheme was arranged by using Taguchi method. The considered parameters were cutting speed, feed, depth of cut, and engagement. Cutting force components and surface roughness were measured, and then analysis of variance is performed. The results show that the feed is the dominant factor affecting the surface roughness. Backpropagation artificial neural network was utilized to create predictive models of surface roughness and cutting forces exploiting the experimental data, and the genetic algorithm was employed to find the constrained optimum of surface roughness. Finally, in order to validate the method, an experiment with the obtained optimal cutting condition was carried out, and the results were compared with the predicted value of surface roughness. The corresponding results show the capability of GONNS to predict constrained surface roughness.     

高速车削钛合金时PCBN刀具寿命的研究

采用对角正交回归试验法,研究了用PCBN刀具高速车削钛合金TC4时切削用量对刀具寿命的影响,并分析工件已加工表面粗糙度。通过扫描电镜观察分析,证实刀具的磨损机理主要是前后刀面的粘结磨损及氧化磨损、后刀面磨损以及切削深度线处的沟槽磨损。     

In-process prediction of surface roughness in turning of Ti–6Al–4V alloy using cutting parameters and vibration signals

In this work, an attempt has been made to use vibration signals for in-process prediction of surface roughness during turning of Ti–6Al–4V alloy. The investigation was carried out in two stages. In the first stage, only acceleration amplitude of tool vibrations in axial, radial and tangential directions were used to develop multiple regression models for prediction of surface roughness. The first and second order regression models thus developed were not found accurate enough (maximum percentage error close to 24%). In the second stage, initially a correlation analysis was performed to determine the degree of association of cutting speed, feed rate, and depth of cut and the acceleration amplitude of vibrations in axial, radial, and tangential directions with surface roughness. Subsequently, based on this analysis, feed rate and depth of cut were included as input parameters aside from the acceleration amplitude of vibrations in radial and tangential directions to develop a refined first order multiple regression model for surface roughness prediction. This model provided good prediction accuracy (maximum percentage error 7.45%) of surface roughness. Finally, an artificial neural network model was developed as it can be readily integrated into a computer integrated manufacturing environment.     

涂层刀具高速铣削模具钢SKD11的表面粗糙度模型预测

为有效控制和预测高硬度模具钢加工的表面质量和加工效率,通过设计正交切削试验,研究了在不同切削参数组合(主轴转速、进给速度、轴向切削深度和径向切削深度)及冷却润滑方式条件下、Ti Si N涂层刀具对模具钢SKD11(62HRC)的高速铣削。应用BP神经网络原理建立表面粗糙度预测模型,并进行试验验证其准确性。研究表明,在不同加工条件下,基于BP神经网络模型建立的涂层刀具铣削模具钢SKD11表面粗糙度模型有较好的预测精度,其预测误差在3.45%-6.25%之间,对于模具制造企业选择加工工艺参数、控制加工质量和降低加工成本有重要意义。     

Optimization of surface roughness and tool wear in hard turning of austempered ductile iron (grade 3) using Taguchi method

In this work, the cutting parameters are optimized in hard turning of ADI using carbide inserts based on Taguchi method. The cutting insert CVD coated with AL₂O₃/MT TICN. Experiments have been carried out in dry condition using L₁₈ orthogonal array. The cutting parameters selected for machining are cutting speed, feed rate and depth of cut with each three levels, nose radius in two levels maintaining other cutting parameters constant. The ANOVA and signal to noise ratio are used to optimize the cutting parameters. The cutting speed is the most dominant factor affecting the surface roughness and tool wear. In optimum cutting condition, the confirmation tests are carried out. The optimum cutting condition results are predicted using signal to noise ratio and regression analysis. The predicted and experimental values for surface roughness and tool wear adhere closer to 9.27% and 1.05% of deviations respectively.     

射流束切削时在边壁约束下的直径增大变形及加工表面质量研究

利用氯化钠晶粒射流切削生物骨材料时,射流束变形可能会导致骨材料切削断面产生预期之外的损伤,不利于生物组织的后期恢复。为探索射流束切削加工时在边壁约束下产生的变形,以及相应加工表面的质量特点,设计了磨料水射流切削可视化实验。使用高速摄影机拍摄磨料水射流加工过程,利用可视化手段观测射流束变形情况,并使用表面粗糙度Ra表征加工表面质量。研究发现,射流束在前端边壁与两侧边壁共同约束下存在沿切削进给方向的直径增大变形,该变形使加工表面粗糙度沿切深的减小幅度增大及出现频率增多。最后对表面粗糙度Ra沿切割深度的变化数据进行二次处理,提出了一种新的建立磨料水射流切削材料表面粗糙度预测模型的思路。     

In-process surface roughness prediction system using cutting vibrations in turning

Machining is a complex process in which many variables can affect the desired results. Among them, surface roughness is a widely used index of a machined product quality and, in most cases, is a technical requirement for mechanical products since, together with dimensional precision, it affects the functional behavior of the parts during their useful life, especially when they have to be in contact with other materials. In-process surface roughness prediction is, thus, extremely important. In this work, an in-process surface roughness estimation procedure, based on least-squares support vector machines, is proposed for turning processes. The cutting conditions (feed rate, cutting speed, and depth of cut), parameters of tool geometry (nose radius and nose angle), and features extracted from the vibration signals constitute the input information to the system. Experimental results show that the proposed system can predict surface roughness with high accuracy in a fast and reliable way.