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.     

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.     

Performance of coated and uncoated mixed ceramic tools in hard turning process

The present contribution deals with the study of the effects of cutting speed, feed rate and depth of cut on the performance of machining which traditionally named “machinability”. The focus is made on the effect of the pre-cited cutting parameters on the evolution of surface roughness and cutting force components during hard turning of AISI D3 cold work tool steel with CC6050 and CC650 ceramic inserts. Also, for both ceramics a comparison of their wear evolution with time and its impact on the surface equality was proposed. The planning of experiments was based on Taguchi’s L₁₆ orthogonal array. The analysis of variance (ANOVA), the signal-to-noise ratio and response surface methodology (RSM) were adopted. Consequently, the validity of proposed linear regression model was checked and the most important parameter affecting the surface roughness and cutting force components were determined. Furthermore, in order to determine the levels of the cutting regime that lead to minimum surface roughness and minimum machining force the relationship between cutting factors was analyzed. The results revealed that the surface quality obtained with the coated CC6050 ceramic insert is 1.6 times better than the one obtained with uncoated CC650 ceramic insert. However, the uncoated ceramic insert was useful in reducing the machining force.     

Optimization of surface roughness,cutting force and tool wear of nitrogen alloyed duplex stainless steel in a dry turning process using Taguchi method

In this work, the dry turning parameters of two different grades of nitrogen alloyed duplex stainless steel are optimized by using Taguchi method. The turning operations were carried out with TiC and TiCN coated carbide cutting tool inserts. The experiments were conducted at three different cutting speeds (80, 100 and 120 m/min) with three different feed rates (0.04, 0.08 and 0.12 mm/rev) and a constant depth of cut (0.5 mm). The cutting parameters are optimized using signal to noise ratio and the analysis of variance. The effects of cutting speed and feed rate on surface roughness, cutting force and tool wear were analyzed. The results revealed that the feed rate is the more significant parameter influencing the surface roughness and cutting force. The cutting speed was identified as the more significant parameter influencing the tool wear. Tool wear was analyzed using scanning electron microscope image. The confirmation tests are carried out at optimum cutting conditions. The results at optimum cutting condition are predicted using estimated signal to noise ratio equation. The predicted results are found to be closer to experimental results within 8% deviations.     

Prediction of surface roughness in CNC turning by model-assisted response surface method

Various statistical approaches such as classical regression and modern machine learning methods have been applied to measurement data for estimating the status of manufacturing processes, which is now boosted by the movement of Internet of Things (IoT). In this study, we attempt to integrate an analytical tool model of surface roughness and measurement data of CNC turning to develop a modeling approach which does not depend too much on data, but also effectively uses existing analytical models. As in previous researches, we use cutting speed, feed rate, depth of cut and three acceleration components from an accelerometer to predict surface roughness. Co-Kriging method is employed to integrate the above measurements and a well-known model of surface roughness in turning. It was confirmed that the approach improved the prediction accuracy when only small amount of data is available for model construction. Meanwhile, the accuracy of ordinary Kriging method, which only depends on data, is suitable when measurement data sufficiently spans the parameter space, being expected that it may be rare in actual operations. We also attempted to detect outlier of measurements using the Co-Kriging method, which might be a non-trivial task when there is no additional information to evaluate the validity of the measurement data.     

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.     

基于田口法的高速切削参数优化研究与应用

应用田口法对切削速度、背吃刀量以及每齿进给量三个主要影响表面粗糙度的因素进行分析,求出各个因素不同水平的平均表面粗糙度和信噪比(S/N),得到最优切削参数。预测经最优切削参数加工得到的表面粗糙度值,最后通过确认实验验证了其正确性。     

Influence of tool wear on surface roughness in hard turning using differently shaped ceramic tools

Hard turning has been applied in many cases in producing bearings, gears, cams, shafts, axels, and other mechanical components since the early 1980s. Mixed ceramics (aluminum oxide plus TiC or TiCN) is one of the two cutting tool materials (apart from PCBN) widely used for finish machining of hardened steel (HRC 50–65) parts, especially under dry machining conditions and moderate cutting speed ranging from 90 to 120 m/min. This paper reports an extensive characterization of the surface roughness generated during hard turning (HT) operations performed with conventional and wiper ceramic tools at variable feed rate and its changes originated from tool wear. Moreover, it compares some predominant tool wear patterns produced on the two types of ceramic inserts and their influence on the alteration of surface profiles. After the hard turning tests, the relevant changes of surface profiles and surface roughness parameters were successively registered and measured by a stylus profilometer. In this investigation, a set of 2D surface roughness parameters, as well as profile and surface characteristics, such as the amplitude distribution functions, bearing area curves and symmetrical curves of geometrical contact obtained for the machined surface, were determined and analyzed. A novel aspect of this research is that the notch wear progress at the secondary cutting (trailing) edges was found to produce the substantial modifications of the individual irregularities, and constitute the altered surface profiles. Moreover, this research contributes to practical aspects of HT technology due to exploring the relations between the tool state at different times within the tool life and the relevant surface roughness characterization.     

Optimization of surface roughness and flank wear using the Taguchi method in milling of Hadfield steel with PVD and CVD coated inserts

In this paper, the Taguchi method and regression analysis have been applied to evaluate the machinability of Hadfield steel with PVD TiAlN- and CVD TiCN/Al₂O₃-coated carbide inserts under dry milling conditions. Several experiments were conducted using the L₁₈ (2 × 3 × 3) full-factorial design with a mixed orthogonal array on a CNC vertical machining center. Analysis of variance (ANOVA) was used to determine the effects of the machining parameters on surface roughness and flank wear. The cutting tool, cutting speed and feed rate were selected as machining parameters. The analysis results revealed that the feed rate was the dominant factor affecting surface roughness and cutting speed was the dominant factor affecting flank wear. Linear and quadratic regression analyses were applied to predict the outcomes of the experiment. The predicted values and measured values were very close to each other. Confirmation test results showed that the Taguchi method was very successful in the optimization of machining parameters for minimum surface roughness and flank wear in the milling the Hadfield steel.     

Some studies on hard turning of AISI 4340 steel using multilayer coated carbide tool

Hard turning with multilayer coated carbide tool has several benefits over grinding process such as, reduction of processing costs, increased productivities and improved material properties. The objective was to establish a correlation between cutting parameters such as cutting speed, feed rate and depth of cut with machining force, power, specific cutting force, tool wear and surface roughness on work piece. In the present study, performance of multilayer hard coatings (TiC/TiCN/Al₂O₃) on cemented carbide substrate using chemical vapor deposition (CVD) for machining of hardened AISI 4340 steel was evaluated. An attempt has been made to analyze the effects of process parameters on machinability aspects using Taguchi technique. Response surface plots are generated for the study of interaction effects of cutting conditions on machinability factors. The correlations were established by multiple linear regression models. The linear regression models were validated using confirmation tests. The analysis of the result revealed that, the optimal combination of low feed rate and low depth of cut with high cutting speed is beneficial for reducing machining force. Higher values of feed rates are necessary to minimize the specific cutting force. The machining power and cutting tool wear increases almost linearly with increase in cutting speed and feed rate. The combination of low feed rate and high cutting speed is necessary for minimizing the surface roughness. Abrasion was the principle wear mechanism observed at all the cutting conditions.     

A study of the Taguchi optimization method for surface roughness in finish milling of mold surfaces

The objective of this paper is to develop a Taguchi optimization method for low surface roughness in terms of process parameters when milling the mold surfaces of 7075-T6 aluminum material. Considering the process parameters of feed, cutting speed, axial-radial depth of cut, and machining tolerance, a series of milling experiments were performed to measure the roughness data. A regression analysis was applied to determine the fitness of data used in the Taguchi optimization method using milling experiments based on a full factorial design. Taguchi orthogonal arrays, signal-to-noise (S/N) ratio, and analysis of variance (ANOVA) are used to find the optimal levels and the effect of the process parameters on surface roughness. A confirmation experiment with the optimal levels of process parameters was carried out in order to demonstrate the effectiveness of the Taguchi method. It can be concluded that Taguchi method is very suitable in solving the surface quality problem of mold surfaces.     

Analysis of tool wear and surface finish in hard turning

Hard turning is a profitable alternative to finish grinding. The ultimate aim of hard turning is to remove work piece material in a single cut rather than a lengthy grinding operation in order to reduce processing time, production cost, surface roughness, and setup time, and to remain competitive. In recent years, interrupted hard turning, which is the process of turning hardened parts with areas of interrupted surfaces, has also been encouraged. The process of hard turning offers many potential benefits compared to the conventional grinding operation. Additionally, tool wear, tool life, quality of surface turned, and amount of material removed are also predicted. In this analysis, 18 different machining conditions, with three different grades of polycrystalline cubic boron nitride (PCBN), cutting tool are considered. This paper describes the various characteristics in terms of component quality, tool life, tool wear, effects of individual parameters on tool life and material removal, and economics of operation. The newer solution, a hard turning operation, is performed on a lathe. In this study, the PCBN tool inserts are used with a WIDAX PT GNR 2525 M16 tool holder. The hardened material selected for hard turning is commercially available engine crank pin material.     

Study of cutting force and surface roughness in the turning of polytetrafluoroethylene composites with a polycrystalline diamond tool

Despite the importance of the polytetrafluoroethylene (PTFE) composites in many industrial applications, especially for space industry, very little is known about the machinability of these composites. This paper presents an investigation into the turning of PTFE composites using a polycrystalline diamond tool in order to analyze the effect of the cutting parameters and insert radius on the cutting force and surface roughness. A strain gauge based dynamometer for the main cutting force measurement in turning was constructed. The force signals were captured and processed using a strain data acquisition system based on the Sider8 and CATMAN software. Cutting force and surface roughness were measured through longitudinal turning, according to the experimental plan developed based on the Taguchi methodology. The signal-to-noise ratio and the analysis of variance were applied to the experimental data in order to determine the effect of the process variables on the surface roughness and cutting force, and predictive models have been derived.     

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.     

Application of Taguchi method for determining optimum surface roughness in turning of high-alloy white cast iron

This paper focused on optimizing the cutting conditions for the average surface roughness (R_a) obtained in machining of high-alloy white cast iron (Ni-Hard) at two different hardness levels (50 HRC and 62 HRC). Machining experiments were performed at the CNC lathe using ceramic and cubic boron nitride (CBN) cutting tools on Ni-Hard materials. Cutting speed, feed rate and depth of cut were chosen as the cutting parameters. Taguchi L₁₈ orthogonal array was used to design of experiment. Optimal cutting conditions was determined using the signal-to-noise (S/N) ratio which was calculated for R_a according to the “the-smaller-the-better” approach. The effects of the cutting parameters and tool materials on surface roughness were evaluated by the analysis of variance. The statistical analysis indicated that the parameters that have the biggest effect on R_a for Ni-Hard materials with 50 HRC and 62 HRC are the cutting speed and feed rate, respectively. Additionally, the optimum cutting conditions for the materials with 50 HRC and 62 HRC was found at different levels.     

高速车削工件表面形貌及其粗糙度特征的试验研究

借助于扫描电镜照片、已加工样品表面形貌轮廓描绘和试验数据处理等手段,对高速车削工件已加工表面形貌与其表面粗糙度之间的关系以及它们的形成特征进行了分析研究.研究结果表明,切削速度和被切削材料的硬度是决定高速车削过程中被切削层材料变形和已加工表面形貌及其表面粗糙度形成的主要因素,随着被切削材料硬度和切削速度的提高,工件已加工表面质量在一定程度上得到了改善.在已加工表面上出现了犁垄和高速加工所特有的熔融金属涂抹现象,由此决定着已加工表面粗糙度值的变化.     

超声振动切削超薄壁精密零件的粗糙度试验研究

对直径为 47.75mm壁厚为 0 .8～ 1 .5mm的照相机导向镜筒零件进行普通和超声车削试验 ,研究了各切削参数对此类超薄壁零件表面粗糙度的影响规律 ,也试验研究了超声振动切削时切削液及车刀对表面粗糙度的影响     

A neural-network-based methodology for the prediction of surface roughness in a turning process

A neural-network-based methodology is proposed for predicting the surface roughness in a turning process by taking the acceleration of the radial vibration of the tool holder as feedback. Upper, most likely and lower estimates of the surface roughness are predicted by this method using very few experimental data for training and testing the network. The network model is trained using the back-propagation algorithm. The learning rate, the number of neurons in the hidden layer, the error goal, as well as the training and the testing dataset size, are found automatically in an adaptive manner. Since the training and testing data are collected from experiments, a data filtration scheme is employed to remove faulty data. The validation of the methodology is carried out for dry and wet turning of steel using high speed steel and carbide tools. It is observed that the present methodology is able to make accurate prediction of surface roughness by utilising small sized training and testing datasets.     

Determining the effect of process parameters on particle size in mechanical milling using the Taguchi method: Measurement and analysis

Micro and nano-particles have been successfully and widely applied in many industrial applications. The mechanical milling process is a popular technique used to produce micro and nano-particles. Therefore, it is very important to improve milling process efficiency and quality by determining the optimal milling parameters. In this study, the effects of the main mechanical milling parameters: milling time, process control agent (PCA), ball to powder ratio (BPR) and milling speed in the planetary ball milling of nanocrystalline Al 2024 powder were optimized by the Taguchi method. Mean particle size (d₅₀) was used to evaluate the effect of process parameters on the mechanical milling process. The orthogonal array experiment is conducted to economically obtain the response measurements. Analysis of variance (ANOVA) and main effect plot are used to determine the significant parameters and set the optimal level for each parameter. The as-received and milled powders were characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM) and a laser particle size analyzer, respectively. The results indicate that the process control agent significantly affects (84% contribution) the mean particle size (d₅₀) while other parameters have a lower effect (16% contribution). The developed model can be used in the mechanical milling processes in order to determine the optimum milling parameters for minimum particle size.