MACHINING RESIDUAL STRESSES IN AISI 316L STEEL AND THEIR CORRELATION WITH THE CUTTING PARAMETERS

It is well known that machining results in residual stresses in the workpiece. These stresses correlate very closely with the cutting tool geometrical parameters as well as with the machining regime. This paper studies the residual stress induced in turning of AISI 316L steel. Particular attention is paid to the influence of the cutting parameters, such as the cutting speed, feed and depth of cut. In the experiments, the residual stresses have been measured using the X-ray diffraction technique (at the surface of the workpiece and in depth). The effects of cutting conditions on residual stresses are analyzed in association with the experimentally determined cutting forces. The orthogonal components of the cutting force were measured using a piezoelectric dynamometer.     

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.     

Multiple-response optimization of turning machining by the taguchi method and the utility concept using uni-directional glass fiber-reinforced plastic composite and carbide (k10) cutting tool

This paper presents a utility concept for multi-response optimization in turning uni-directional glass fiber-reinforced plastics composite using Carbide (K10) cutting tool. The single response optimization resulted in the non-optimization of other responses. The Taguchi method (Orthogonal L₁₈ array) was employed in the experimental work. The process parameters selected for this study were tool nose radius, tool rake angle, feed rate, cutting speed, depth of cut, and cutting environment. Statistically significant parameters were found to simultaneously minimize surface roughness and maximize the material removal rate by ANOVA. The results were further verified by confirmation experiments.     

精密干式硬态车削淬硬工具钢加工表面温度参数优化

使用PCBN刀具对不同淬硬状态工具钢Cr12MoV进行精密干式硬态车削试验,运用极差法分析切削速度、走刀量、切削深度、试件硬度、刀尖圆弧半径五个因素对工件表面温度影响的显著性,并得到了最优车削参数。试验表明：影响工件表面温度最显著的因素是工件淬火硬度,切削深度与走刀量的影响相当,刀尖圆弧半径的影响最小。     

[刀具及切削工艺]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。     

Multi-response optimization of machining parameters on red mud-based aluminum metal matrix composites in turning process

This paper presents the findings of an experimental investigation into the effects of cutting speed, feed rate, depth of cut, and nose radius in computer numerical control (CNC) turning operation performed on red mud-based aluminum metal matrix composites. This paper investigates optimization design of a turning process performed on red mud-based aluminum metal matrix composites. The major performance characteristics selected to evaluate the process are surface roughness, power consumption, and vibration, and the corresponding turning parameters are cutting speed, feed, depth of cut, and nose radius. Taguchi-based grey analysis, which uses grey relational grade as performance index, is specifically adopted to determine the optimal combination of turning parameters. The principal component analysis (PCA) is applied to evaluate the weighting values corresponding to various performance characteristics. L9 orthogonal array design has been used for conducting the experiments. The outcome of confirmation experiments reveals that grey relational analysis coupled with PCA can effectively be used to obtain the optimal combination of turning parameters. Hence, this confirms that the proposed approach in this study can be a useful tool to improve the turning performance of red mud-based aluminum metal matrix composites in CNC turning process.     

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。     

Influence of machining parameters on the machinability of particulate reinforced Al/SiC–MMC

The paper presents the result of an experimental investigation on the machinability of silicon carbide particulate aluminium metal matrix composite during turning using a rhombic uncoated carbide tool. The influence of machining parameters, e.g. cutting speed, feed and depth of cut on the cutting force has been investigated. The influence of the length of machining and cutting time on the tool wear and the influence of various machining parameters, e.g. cutting speed, feed, depth of cut on the surface finish criteria has been analyzed through the various graphical representations. The combined effect of cutting speed and feed on the flank wear has also been investigated. The influence of cutting speed, feed and depth of cut on the tool wears and built-up edge is analyzed graphically. The job surface condition and wear of the cutting tool edge for the different sets of experiments have been examined and compared for searching out the suitable cutting condition for effective machining performance during turning of Al/SiC-MMC. Test results show that no built-up edge is formed during machining of Al/SiC-MMC at high speed and low depth of cut. From the test results and different SEM micrographs, suitable range of cutting speed, feed and depth of cut can be selected for proper machining of Al/SiC-MMC.     

AN EXPERIMENTAL EVALUATION OF CUTTING TEMPERATURES DURING HIGH SPEED MACHINING OF HARDENED D2 TOOL STEEL

This paper investigates experimentally the effects of different process parameters on the cutting edge temperature during high speed machining of D2 tool steel using polycrystalline cubic boron nitride (PCBN) tools. The cutting edge temperature is measured using thermocouples. The process parameters considered are cutting speed, feed rate, nose radius, rake angle, and tool wear. The effects of different edge preparations including sharp, honed and chamfered are also investigated. The results show that increasing cutting speed and feed rate increases the cutting temperature while increasing nose radius reduces the cutting edge temperature. In addition, there is an optimum rake angle value at which minimum cutting temperature is generated.     

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。     

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.     

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.     

Experimental investigation of precision turning of Monel K-500 under dry conditions

In this paper, final surface accuracy in turning the super alloy Monel K-500 is studied. The experiments were conducted on the basis of the design of experiment methodology considering four inputs of tool nose radius, feed rate, depth of cut, and cutting speed, and three outputs of surface roughness, dimensional deviation, and tool wear. The aim of this work is to identify these three phenomena to achieve a desirable machined surface with acceptable finishing and the least deviation from nominal dimensions under different parametric conditions. It was observed that the quality of the machined surface in the direction of the machining length is not constant and, in some trials, the values of Ra increase considerably at the end of the machining length. The results show that cutting speed can improve surface accuracy, in a way that the more the cutting speed, the less the dimensional deviation. Less depth of cut and tool radius affect dimensional deviation as well. Although it has a small effect on dimensional deviation, feed rate plays the most important role in controlling tool wear. Finally, on the basis of Grey relational analysis, a simultaneous optimization is carried out on surface roughness, dimensional deviation, and tool wear values. In order to minimize these responses, optimal parametric conditions are presented. A satisfying correspondence was observed between the predicted results and the confirmation observations.     

Optimization of end mill tool geometry parameters for Al7075-T6 machining operations based on vibration amplitude by response surface methodology

This paper aims at developing a statistical model to envisage vibration amplitude in terms of geometrical parameters such as radial rake angle, nose radius of cutting tool and machining parameters such as cutting speed, cutting feed and axial depth of cut. Experiments were conducted through response surface methodology experimental design. The material chosen is Aluminum (Al 7075-T6) and the tool used was high speed steel end mill cutter with different tool geometry. Two channels piezoelectric accelerometers were used to measure the vibration amplitude. The second order mathematical model in terms of machining parameters was built up to predict the vibration amplitude and ANOVA was used to verify the competency of the model. Further investigation on the direct and interactive effect of the process parameter with vibration amplitude was carried out for the selection of process parameter so that the vibration amplitude was maintained at the minimum which ensures the stability of end milling process. The optimum values obtained from end milling process are Radial rake angle-12°, Nose radius-0.8 mm, Cutting speed-115 m/min, Cutting feed rate-0.04 mm/tooth, axial depth of cut-2.5 mm. The vibration amplitude exhibited negative relationship with radial rake angle and nose radius. The dominant factors on the vibration amplitude are feed rate and depth of cut. Thus it is envisaged that the predictive models in this study could produce values of the vibration amplitude close to the experimental readings with a 95% confidence interval.     

Experimental investigation and performance analysis of cemented carbide inserts of different geometries using Taguchi based grey relational analysis

In this work, effect of machining parameters cutting speed, feed rate and depth of cut, geometrical parameters cutting insert shape, relief angle and nose radius were investigated and optimized using Taguchi based grey relational analysis. 18 ISO designated uncoated cemented carbide inserts of different geometries were used to turn practically used automotive axles to study the influence of variation in carbide inserts geometry. Performance measures viz., flank wear, surface roughness and material removal rate (MRR) were optimized using grey relational grade, based on the experiments designed using Taguchi’s Design of Experiments (DoE). A weighted grey relational grade is calculated to minimize flank wear and surface roughness and to maximize MRR. Analysis of variance shows that cutting insert shape is the prominent parameter followed by feed rate and depth of cut that contributes towards output responses. An experiment conducted with identified optimum condition shows a lower flank wear and surface roughness with higher MRR. The confirmation results obtained are confirmed by calculating confidence interval, which lies within the width of the interval.     

金属二次切削残余应力的有限元分析

残余应力是影响构件表面完整性的重要因素,合理分析和研究连续切削状态下构件表面的残余应力,对现实生产有很大的指导意义。本文通过建立二次切削有限元模型,将一次切削完成以后的应力、应变场作为初始边界条件加载到二次切削过程中,运用可靠的材料模型及切屑分离手段,合理施加位移和速度边界条件,对金属二次切削过程进行数值模拟,得出了不同切削次序、切削深度和刀尖圆角半径对工件表面残余应力的影响规律。这对进一步研究连续切削过程及机械加工的表面完整性具有较高的参考价值。     

The formation mechanism and the influence factor of residual stress in machining

Residual stresses generated in cutting process have important influences on workpiece performance. The paper presents a method of theoretical analysis in order to explicate the formation mechanism of residual stresses in cutting. An important conclusion is drawn that the accumulated plastic strain is the main factor which determines the nature and the magnitude of surface residual stresses in the workpiece. On the basis of the analytical model for residual stress, a series of simulations for residual stress prediction during cutting AISI 1045 steel are implemented in order to obtain the influences of cutting speed, depth of cut and tool edge radius on surface residual stress in the workpiece. And these influences are explained from the perspective of formation mechanism of residual stress in cutting. The conclusions have good applicability and can be used to guide the parameters selection in actual production.     

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.     

Effect of high speed turning operation on surface roughness of hybrid metal matrix (Al-SiCp-fly ash) composite

This paper explains the effect of turning parameters such as cutting speed, feed rate, depth of cut and cutting tool nose radius on surface roughness of hybrid metal matrix (Al-SiC_p-Fly ash) composite. Experiments have been conducted based on the orthogonal array L16(4)⁵ and surface roughness was tested on the composites turned by an high speed CNC centre lathe. Analysis of variance (ANOVA) was performed to predict the significant parameters and their contribution towards surface finish of the composite. A mathematical model was developed using non-linear regression analysis. Taguchi method and Genetic algorithm have been employed to optimize the turning parameters for optimum surface roughness of the composite. The optimum turning parametric conditions have been checked with the confirmation experiments. It has been noted that the optimum condition of genetic algorithm exhibited better results than the experimental results based on the orthogonal array and the optimum condition of Taguchi method.