Determining the optimum process parameter for grinding operations using robust process

We applied combined response surface methodology (RSM) and Taguchi methodology (TM) to determine optimum parameters for minimum surface roughness (Ra) and vibration (Vb) in external cylindrical grinding. First, an experiment was conducted in a CNC cylindrical grinding machine. The TM using L ₂₇ orthogonal array was applied to the design of the experiment. The three input parameters were workpiece revolution, feed rate and depth of cut; the outputs were vibrations and surface roughness. Second, to minimize wheel vibration and surface roughness, two optimized models were developed using computer-aided single-objective optimization. The experimental and statistical results revealed that the most significant grinding parameter for surface roughness and vibration is workpiece revolution followed by the depth of cut. The predicted values and measured values were fairly close, which indicates (R _Ra ² =94.99 and R _Vb ² =92.73) that the developed models can be effectively used to predict surface roughness and vibration in the grinding. The established model for determination of optimal operating conditions shows that a hybrid approach can lead to success of a robust process.     

Experimental investigation of tool vibration and surface roughness in the precision end-milling process using the singular spectrum analysis

The vibrations on the cutting tool have a momentous influence for the surface quality of workpiece with respect to surface profile and roughness during the precision end-milling process. Singular spectrum analysis (SSA) is a new non-parametric technique of time series analysis and forecasting. The significant features of the cutting tool vibration signals from the sensors are extracted and transformed from the SSA-processed vibration signals. In the present study, SSA is applied to extract and transform the raw signals of the vibrations on the cutting tool for investigating the relationship between tool vibration and surface roughness in the precision end-milling process of hardened steel SCM440. In this experimental investigation, the spindle speed, feed rate, and cutting depth were chosen as the numerical factor; the cutting feed direction and holder type were regarded as the categorical factor. An experimental plan consisting of five-factor (three numerical plus two categorical) d-optimal design based on the response surface methodology was employed to carry out the experimental study. A micro-cutting test was conducted to visualize the effect of vibration of tooltip on the performance of surface roughness. With the experimental values up to 95% confidence interval, it is fairly well for the experimental results to present the mathematical models of the tool vibration and surface roughness. Results show that the effects of feed rate and cutting depth provide the reinforcement on the overall vibration to cause the unstable cutting process and exhibit the result of the worst machined surface. The amplitude of vibration signals along the cutting feed direction is generally larger than that along other direction. The spindle speed and tool holder type affect the stability of cutting tooltip during the cutting process.     

Investigation of Tool Wear and Surface Finish by Analyzing Vibration Signals in Turning Assab-705 Steel

Various occurrences in machining influence the machining dynamics and thus produce vibration in the cutting tool-workpiece arrangement. In this investigation, with tri-axial accelerometer mounted on the tool-holder in turning ASSAB-705 steel, vibration signals have been captured with and without cutting. The nature of vibrations arising in the cutting tool at different cutting conditions has been investigated. It has been observed that the RMS amplitude of vibration along all three axes for the increasing cutting speed was mixed in nature; however, an increasing trend was noticed in the vibrations along the feed, V_x and radial, V_y directions. The vibration along the main cutting direction, V_z was mixed, initiated by large vibration and then decreased until a particular cutting speed was reached and finally increased steadily. The feed vibration component, V_x has a similar response to the change of the workpiece surface roughness, while the other two components, V_y and V_z have the more coherent response to the rate of flank wear progression throughout the tool life. The natural frequency of different machine parts vibration has been found to be within the band of 0 Hz – 4.2 kHz, whereas the frequencies of different occurrences in turning varied between 98 Hz and 42 kHz.     

Optimisation of parameters affecting surface roughness of Co28Cr6Mo medical material during CNC lathe machining by using the Taguchi and RSM methods

This study involves modelling of experimental data of surface roughness of Co28Cr6Mo medical alloy machined on a CNC lathe based on cutting parameters (spindle rotational speed, feed rate, depth of cut and tool tip radius). In order to determine critical states of the cutting parameters variance analysis (ANOVA) was applied while optimisation of the parameters affecting the surface roughness was achieved with the Response Surface Methodology (RSM) that is based on the Taguchi orthogonal test design. The validity of the developed models necessary for estimation of the surface roughness values (Ra, Rz), was approximately 92%. It was found that for Ra 38% of the most effective parameters is on the tool tip radius, followed by 33% on the feed rate whereas for Rz tool tip radius occupied 43% with the feed being at 33% rate. To achieve the minimum surface roughness, the optimum values obtained for spindle rpm, feed rate, depth of cut and tool tip radius were respectively, 318 rpm, 0.1 mm/rev, 0.7 mm and 0.8 mm.     

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.     

Comparative assessment of cooling conditions,including MQL technology on machining factors in an environmentally friendly approach

Manufacturers need to continuously improve productivity and reduce the most disadvantages. In the current work, an experimental study has been carried out in order to evaluate the influence of different cutting parameters on the various machining factors such as surface roughness, cutting force, cutting power, metal removal rate, and tool wear during turning of X210Cr12 steel using a multilayer-coated tungsten carbide insert with various nose radii (r). Tests are designed according to Taguchi’s L₁₈ (2¹ × 3⁴) orthogonal array. ANOVA has been performed to determine the effect of the cutting conditions, and mathematical models have been developed through response surface methodology (RSM). The results indicate that the feed rate and the tool nose radius are the main affecting factors on surface roughness while both tangential force and cutting power are affected mainly by the depth of cut followed by the feed rate and the nose radius. Other special tests of long term have been established in order to study the wear evolution and consequently to determine the tool life. The results indicate also that minimum quantity lubrication (MQL) leads to an important improvement in terms of the cutting tool life by a gain of 23~40% compared to wet and dry machining. It has been found that the MQL is an interesting way to minimize lubrication cost and protect operator health and the environment while keeping better machining quality.     

Application of Taguchi and response surface methodologies for surface roughness in machining glass fiber reinforced plastics by PCD tooling

This paper discusses the use of Taguchi and response surface methodologies for minimizing the surface roughness in machining glass fiber reinforced (GFRP) plastics with a polycrystalline diamond (PCD) tool. The experiments have been conducted using Taguchi’s experimental design technique. The cutting parameters used are cutting speed, feed and depth of cut. The effect of cutting parameters on surface roughness is evaluated and the optimum cutting condition for minimizing the surface roughness is determined. A second-order model has been established between the cutting parameters and surface roughness using response surface methodology. The experimental results reveal that the most significant machining parameter for surface roughness is feed followed by cutting speed. The predicted values and measured values are fairly close, which indicates that the developed model can be effectively used to predict the surface roughness in the machining of GFRP composites. The predicted values are confirmed by using validation experiments.     

Performance studies of multilayer hard surface coatings (TiN/TiCN/Al2O3/TiN) of indexable carbide inserts in hard machining: Part-II (RSM,grey relational and techno economical approach)

This paper presents the mathematical modelling and parametric optimization on flank wear and surface roughness based on response surface methodology and grey-based Taguchi method in finish hard turning of AISI 4340 steel (HRC 47 ± 1) using multilayer coated carbide (TiN/TiCN/Al₂O₃/TiN) insert under dry environment. The economical feasibility of utilizing multilayer TiN coated carbide insert has been described. Model adequacy has been checked using correlation coefficients. From main effect, it is evident that, cutting speed is the most significant factor for flank wear followed by depth of cut and feed. Again, feed is the most significant factor for surface roughness followed by cutting speed and depth of cut. The coefficient of determination (R²) is more than 75% for RSM models developed, which shows the high correlation exist between the experimental and predicted values. The experimental vs. predicted values of flank wear and surface roughness (Ra and Rz) are also found to be very close to each other implying significance of models developed. The improvement of grey relational grade from initial parameter combination (d2–f3–v4) to the optimal parameter combination (d1–f1–v3) is found to be 0.3093 using grey relational analysis coupled with Taguchi method for simultaneous optimization of responses. Flank wear (VBc) and surface roughness parameters (Ra and Rz) are decreased 1.9, 2.32 and 1.5 times respectively considering optimal parametric combinations for multi-responses. The calculated total machining cost per part is only Rs. 3.17 due to higher tool life (47 min at their optimal level) of multilayer TiN coated carbide insert. It brings to the reduction of downtime and increases the savings.     

Selection of optimum tool geometry and cutting conditions using a surface roughness prediction model for end milling

Influence of tool geometry on the quality of surface produced is well known and hence any attempt to assess the performance of end milling should include the tool geometry. In the present work, experimental studies have been conducted to see the effect of tool geometry (radial rake angle and nose radius) and cutting conditions (cutting speed and feed rate) on the machining performance during end milling of medium carbon steel. The first and second order mathematical models, in terms of machining parameters, were developed for surface roughness prediction using response surface methodology (RSM) on the basis of experimental results. The model selected for optimization has been validated with the Chi square test. The significance of these parameters on surface roughness has been established with analysis of variance. An attempt has also been made to optimize the surface roughness prediction model using genetic algorithms (GA). The GA program gives minimum values of surface roughness and their respective optimal conditions.     

Multiple-response optimization for micro-endmilling process using response surface methodology

In the present trend, new fabrication methods for producing miniaturized components are gaining popularity due to the recent advancements in micro-electro mechanical systems. Micro-machining differs from the traditional machining with the small size tool, resolution of x?Cy and z stages. This paper focuses RSM for the multiple response optimization in micro-endmilling operation to achieve maximum metal removal rate (MRR) and minimum surface roughness. In this work, second-order quadratic models were developed for MRR and surface roughness, considering the spindle speed, feed rate and depth of cut as the cutting parameters, using central composite design. The developed models were used for multiple-response optimization by desirability function approach to determine the optimum machining parameters. These optimized machining parameters are validated experimentally, and it is observed that the response values are in good agreement with the predicted values.     

Analysis and prediction of tool wear, surface roughness and cutting forces in hard turning with CBN tool

The main of the present study is to investigate the effects of process parameters (cutting speed, feed rate and depth of cut) on performance characteristics (tool life, surface roughness and cutting forces) in finish hard turning of AISI 52100 bearing steel with CBN tool. The cutting forces and surface roughness are measured at the end of useful tool life. The combined effects of the process parameters on performance characteristics are investigated using ANOVA. The composite desirability optimization technique associated with the RSM quadratic models is used as multi-objective optimization approach. The results show that feed rate and cutting speed strongly influence surface roughness and tool life. However, the depth of cut exhibits maximum influence on cutting forces. The proposed experimental and statistical approaches bring reliable methodologies to model, to optimize and to improve the hard turning process. They can be extended efficiently to study other machining processes.     

The use of D-optimal design for modeling and analyzing the vibration and surface roughness in the precision turning with a diamond cutting tool

Using a diamond cutting tool in the precision turning process, the vibration of tool-tip has an undesirable effect on the machined surface??s quality. The objective of this paper is to present the mathematical models for modeling and analyzing the vibration and surface roughness in the precision turning with a diamond cutting tool. Machining parameters including the spindle speed, feed rate and cutting depth were chosen as numerical factor, and the status of lubrication was regarded as the categorical factor. An experimental plan of a four-factor??s (three numerical plus one categorical) D-optimal design based on the response surface methodology was employed to carry out the experimental study. A micro-cutting test is conducted to visualize the effect of vibration of tool-tip on the performance of surface roughness. With the experimental values up to a 95% confidence interval, it is fairly well for the experimental results to present the mathematical models of the vibration and surface roughness. Results show that the spindle speed and the feed rate have the greatest influence on the longitudinal vibration amplitude, and the feed rate and the cutting depth play major roles for the transverse vibration amplitude. As the spindle speed increases, the overall vibration of tool-tip tends to more stable condition which leads to the results of the best machined surface. The effects of the feed rate and cutting depth provide the reinforcement on the overall vibration to cause the unstability of cutting process and exhibit the result of the worst machined surface.     

Optimization of machining parameters in the abrasive waterjet turning of alumina ceramic based on the response surface methodology

A study on the radial-mode abrasive waterjet turning (AWJT) of 96 % alumina ceramic is presented and discussed. An experimental investigation is carried out to explore the influence of process parameters (including water pressure, jet feed speed, abrasive mass flow rate, surface speed, and nozzle tilted angle) on the material removal rate (MRR) when turning 96 % alumina ceramic. The experiments are conducted on the basis of response surface methodology (RSM) and sequential approach using face-centered central composite design. The quadratic model of RSM associated with the sequential approximation optimization (SAO) method is used to find optimum values of process parameters in terms of surface roughness and MRR. The results show that the MRR is influenced principally by the water pressure P and the next is abrasive mass flow rate m _a . The optimization results show that the MRR can be improved without increasing the surface roughness when machining 96 % alumina ceramic in the radial-mode abrasive waterjet turning process.     

Effect of the process parameters on the three-dimensional shape of molten pool during full-penetration laser welding process

Slow tool servo (STS) turning is superior in machining precision and in complicated surface. However, STS turning is a complex process in which many variables can affect the desired results. This paper focuses on surface roughness prediction in lenses STS turning. An exponential model, based on the five main cutting parameters including tool nose radius, feed rate, depth of cut, C-axis speed, and discretization angle, for surface roughness prediction of lenses is developed by means of orthogonal experiment regression analysis. Meanwhile, a prediction model of surface roughness based on least squares support vector machines (LS-SVM) with radial basis function is constructed. Orthogonal experiment swatches are studied, and chaotic particle swarm optimization and leave-one-out cross-validation are applied to determine the model parameters. The comparison of LS-SVM model and exponential model is also carried out. Predictive LS-SVM model is found to be capable of better predictions for surface roughness and has absolute fraction of variance R² of 0.99887, the mean absolute percent error e_M of 8.96 %, and the root mean square error e_R of 10.68 %. The experimental results and prediction of LS-SVM model show that effects of tool nose radius and feed rate are more significant than that of depth of cut on surface roughness of lenses turning.     

Modeling and optimization of hard turning of X38CrMoV5-1 steel with CBN tool: Machining parameters effects on flank wear and surface roughness

The present study, aims to investigate, under turning conditions of hardened AISI H11 (X38CrMoV5-1), the effects of cutting parameters on flank wear (VB) and surface roughness (Ra) using CBN tool. The machining experiments are conducted based on the response surface methodology (RSM). Combined effects of three cutting parameters, namely cutting speed, feed rate and cutting time on the two performance outputs (i.e. VB and Ra), are explored employing the analysis of variance (ANOVA). Optimal cutting conditions for each performance level are established and the relationship between the variables and the technological parameters is determined using a quadratic regression model. The results show that the flank wear is influenced principally by the cutting time and in the second level by the cutting speed. Also, it is that indicated that the feed rate is the dominant factor affecting workpiece surface roughness.     

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.     

Machinability investigation in hard turning of AISI D3 cold work steel with ceramic tool using response surface methodology

The hard turning process has been attracting interest in different industrial sectors for finishing operations of hard materials. In this paper, the effects of cutting speed, feed rate, and depth of cut on surface roughness, cutting force, specific cutting force, and power in the hard turning were experimentally investigated. An experimental investigation was carried out using ceramic cutting tools, composed approximately with (70 %) of Al₂O₃ and (30 %) of TiC, in surface finish operations on cold work tool steel AISI D3 heat-treated to a hardness of 60 HRC. Based on 3³ full factorial designs, a total of 27 tests were carried out. The range of each parameter is set at three different levels, namely, low, medium, and high. Analysis of variance is used to check the validity of the model. Experimental observations show that higher cutting forces are required for machining harder work material. This cutting force gets affected mostly by feed rate followed by depth of cut. Feed rate is the most influencing factor on surface roughness. Feed rate followed by depth of cut become the most influencing factors on power; 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 depth of cut value, higher cutting speed, and by limiting the feed rate to 0.12 and 0.13 mm/rev, while hard turning of AISI D3 hardened steel, respectively, ensures minimum cutting forces and better surface roughness. Higher values of depth of cut are necessary to minimize the specific cutting force.     

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.     

Investigation of the performance of carbide cutting tools with hard coatings in hard milling based on the response surface methodology

Machining of hard materials has become a great challenge for several decades. One of the problems in this machining process is early tool wear, and this affects the machinability of hard materials. In order to increase machinability, cutting tools are widely coated with nanostructured physical vapor deposition hard coatings. The main characteristics of such advanced hard coatings are high microhardness and toughness as well as good adhesion to the substrate. In this paper, the influence of hard coatings (nanolayer AlTiN/TiN, multilayer nanocomposite TiAlSiN/TiSiN/TiAlN, and commercially available TiN/TiAlN) and cutting parameters (cutting speed, feed rate, and depth of cut) on cutting forces and surface roughness were investigated during face milling of AISI O2 cold work tool steel (～61 HRC). The experiments were conducted based on 3¹³ factorial design by response surface methodology, and response surface equations of cutting forces and surface roughness were obtained. In addition, the cutting forces obtained with the coated and uncoated tools were compared. The results showed that the interaction of coating type and depth of cut affects surface roughness. The hard coating type has no significant effect on cutting forces, while the cutting force F _z is approximately two times higher in the case of uncoated tool.     

Prediction of surface roughness and dimensional deviation of workpiece in turning: a machine vision approach

In the past, roughness values measured directly on machined surfaces were used to develop mathematical models that are used in predicting surface roughness in turning. This approach is slow and tedious because of the large number of workpieces required to obtain the roughness data. In this study, 2-D images of cutting tools were used to generate simulated workpieces from which surface roughness and dimensional deviation data were determined. Compared to existing vision-based methods that use features extracted from a real workpiece to represent roughness parameters, in the proposed method, only simulated profiles of the workpiece are needed to obtain the roughness data. The average surface roughness R _a, as well as dimensional deviation data extracted from the simulated profiles for various feed rates, depths of cut, and cutting speeds were used as the output of response surface methodology (RSM) models. The predictions of the models were verified experimentally using data obtained from measurements made on the real workpieces using conventional methods, i.e., surface roughness tester and a micrometer, and good correlation between the two methods was observed.