Taguchi method based optimisation of drilling parameters in drilling of AISI 316 steel with PVD monolayer and multilayer coated HSS drills

This paper focuses on the optimisation of drilling parameters using the Taguchi technique to obtain minimum surface roughness (Ra) and thrust force (Ff). A number of drilling experiments were conducted using the L16 orthogonal array on a CNC vertical machining centre. The experiments were performed on AISI 316 stainless steel blocks using uncoated and coated M35 HSS twist drills under dry cutting conditions. Analysis of variance (ANOVA) was employed to determine the most significant control factors affecting the surface roughness and thrust force. The cutting tool, cutting speed and feed rate were selected as control factors. After the sixteen experimental trials, it was found that the cutting tool was the most significant factor on the surface roughness and that the feed rate was the most significant factor on the thrust force. The results of the confirmation experiments showed that the Taguchi method was notably successful in the optimisation of drilling parameters for better surface roughness and thrust force.     

Measurement and analysis of thrust force in drilling of particle board (PB) composite panels

Particleboard is a wood based composite extensively used in wood working. Drilling is the most commonly used machining process in furniture industries. The surface characteristics and the damage free drilling are significantly influenced by the machining parameters. The thrust force developed during drilling play a major role in gaining the surface quality and minimizing the delamination tendency. The objective of this study is to measure and analyze the cutting conditions which influences the thrust force in drilling of particle board panels. The parameters considered are spindle speed, feed rate and point angle. The drilling experiments are performed based on Taguchi’s design of experiments and a response surface methodology (RSM) based mathematical model is developed to predict the influence of cutting parameters on thrust force. The results showed that high spindle speed with low feed rate combination minimizes the thrust force in drilling of pre-laminated particle board (PB) panels.     

Statistical model to determine surface roughness when milling hastelloy C-22HS

The aim of this study is to develop the surface roughness prediction models, with the aid of statistical methods, for hastelloy C-22HS when machined by PVD and CVD coated carbide cutting tools under various cutting conditions. These prediction models were then compared with the results obtained experimentally. By using response surface method (RSM), first order models were developed with 95 % confidence level. The surface roughness models were developed in terms of cutting speed, feed rate and axial depth using RSM as a tool of design of experiment. In general, the results obtained from the mathematical models were in good agreement with those obtained from the machining experiments. It was found that the feed rate, cutting speed and axial depth played a major role in determining the surface roughness. On the other hand, the surface roughness increases with a reduction in cutting speed. PVD coated cutting tool performs better than CVD when machining hastelloy C-22HS. It was observed that most of the chips from the PVD cutting tool were in the form of discontinuous chip while CVD cutting tool produced continuous chips.     

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.     

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.     

Integration of fuzzy logic with response surface methodology for thrust force and surface roughness modeling of drilling on titanium alloy

In recent years, a lot of extensive research work has been carried out in drilling operations for achieving better hole quality. Drilling operation is one of the machining processes, and it widely used in aeronautical and automotive industries for assembling the parts. The surface roughness is one of the significant factors in drilling operation because the poor surface finish will affect the material condition during the assembly. The spindle speed and feed rate are the important factors to affect the surface finish. In addition, the detailed analysis of the thrust force is also to be investigated for characterizing the cutting process. However, for examining the machining characteristics more trial runs are required, and it increases the time and cost of the experiment. In this paper, the integration of fuzzy logic (FL) with response surface methodology (RSM) has been introduced to reduce the cost and the time consumption for investigation. The low, middle, and upper levels of spindle speed with low and upper levels of feed rate combinations were examined on cutting force and surface finish through the experimental setup with the systematic manner. The FL model for thrust force and surface finish were obtained from the collected experimental data. The FL model has developed another two combinations of data without experimentation through universal partitioning. The results show that the predicted FL values are within the range of experimental value. Therefore, the FL model values were selected for further investigation with RSM. The result of FL-RSM model values are also within the range of experimental value. The proposed FL-RSM model and FL model are validated with experimental results. Finally, the validated results show that hybrid FL-RSM produces the effective output than the FL model.     

Application of the central composite design in optimization of machining parameters in drilling hybrid metal matrix composites

In this study, the application of response surface methodology (RSM) and central composite design (CCD) for modeling, optimization, and an analysis of the influences of dominant machining parameters on thrust force, surface roughness and burr height in the drilling of hybrid metal matrix composites produced through stir casting route. Experiments are carried out using Al 356-aluminum alloy reinforced with silicon carbide of size 25 μm and Mica of size 45 μm. Drilling test is carried out using carbide drill of 6 mm diameter. The design of experiment concept has been used to optimize the experimental conditions. The experimental data are collected based on a three-factor-three-level full central composite design. The multiple regression analysis using RSM is used to establish the input–output relationships of the process. The mathematical models are developed and tested for adequacy using analysis of variance and other adequacy measures using the developed models. The main and interaction effect of the input variables on the predicted responses are investigated. The predicted values and measured values are fairly close, which indicate that the developed models can be effectively used to predict the responses in the drilling of hybrid metal matrix composites. The optimized drilling process parameters have been obtained by numerical optimization using RSM by ensuring the minimum thrust force of 84 N, surface roughness of 1.67 μm, and the burr height of 0.16 mm. After the drilling experiments, a scanning electron microscope (SEM) is used to investigate the machined surface and tool wear.     

OPTIMAL MQL AND CUTTING CONDITIONS DETERMINATION FOR DESIRED SURFACE ROUGHNESS IN TURNING OF BRASS USING GENETIC ALGORITHMS

The evolving concept of minimum quantity of lubrication (MQL) in machining is considered as one of the solutions to reduce the amount of lubricant to address the environmental, economical and ecological issues. This paper investigates the influence of cutting speed, feed rate and different amount of MQL on machining performance during turning of brass using K10 cemented carbide tool. The experiments have been planned as per Taguchi's orthogonal array and the second order surface roughness model in terms of machining parameters was developed using response surface methodology (RSM). The parametric analysis has been carried out to analyze the interaction effects of process parameters on surface roughness. The optimization is then carried out with genetic algorithms (GA) using surface roughness model for the selection of optimal MQL and cutting conditions. The GA program gives the minimum values of surface roughness and the corresponding optimal machining parameters.     

Modeling and optimization of burr height in drilling of Al-7075 using Taguchi method and response surface methodology

This investigation presents the use of Taguchi and response surface methodologies for minimizing the burr height and the surface roughness in drilling Al-7075. The Taguchi method, a powerful tool to design optimization for quality, is used to find optimal cutting parameters. Response surface methodology is useful for modeling and analyzing engineering problems. The purpose of this paper was to investigate the influence of cutting parameters, such as cutting speed and feed rate, and point angle on burr height and surface roughness produced when drilling Al-7075. A plan of experiments, based on L₂₇ Taguchi design method, was performed drilling with cutting parameters in Al-7075. All tests were run without coolant at cutting speeds of 4, 12, and 20 m/min and feed rates of 0.1, 0.2, and 0.3 mm/rev and point angle of 90^°, 118°, and 135°. The orthogonal array, signal-to-noise ratio, and analysis of variance (ANOVA) were employed to investigate the optimal drilling parameters of Al-7075. From the analysis of means and ANOVA, the optimal combination levels and the significant drilling parameters on burr height and surface roughness were obtained. The optimization results showed that the combination of low cutting speed, low feed rate, and high point angle is necessary to minimize burr height. The best results of the surface roughness were obtained at lower cutting speed and feed rates while at higher point angle. The predicted values and measured values are quite close to each other; therefore, this result indicates that the developed models can be effectively used to predict the burr height and the surface roughness on drilling of Al-7075.     

Construction of a surface roughness prediction model for high speed machining

In manufacturing environment prediction of surface roughness is very important for product quality and production time. For this purpose, the finite element method and neural network is coupled to construct a surface roughness prediction model for high-speed machining. A finite element method based code is utilized to simulate the high-speed machining in which the cutting tool is incrementally advanced forward step by step during the cutting processes under various conditions of tool geometries (rake angle, edge radius) and cutting parameters (yielding strength, cutting speed, feed rate). The influences of the above cutting conditions on surface roughness variations are thus investigated. Moreover, the abductive neural networks are applied to synthesize the data sets obtained from the numerical calculations. Consequently, a quantitative prediction model is established for the relationship between the cutting variables and surface roughness in the process of high-speed machining. The surface roughness obtained from the calculations is compared with the experimental results conducted in the laboratory and with other research studies. Their agreements are quite well and the accuracy of the developed methodology may be verified accordingly. The simulation results also show that feed rate is the most important cutting variable dominating the surface roughness state.     

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.     

Optimization of tool geometry parameters for turning operations based on the response surface methodology

This investigation focuses on the influence of tool geometry on the surface finish obtained in turning of AISI 1040 steel. In order to find out the effect of tool geometry parameters on the surface roughness during turning, response surface methodology (RSM) was used and a prediction model was developed related to average surface roughness (Ra) using experimental data. The results indicated that the tool nose radius was the dominant factor on the surface roughness. In addition, a good agreement between the predicted and measured surface roughness was observed. Therefore, the developed model can be effectively used to predict the surface roughness on the machining of AISI 1040 steel with in 95% confidence intervals ranges of parameters studied.     

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.     

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.     

Specific cutting force, tool wear and chip morphology characteristics during dry drilling of austempered ductile iron (ADI)

Dry machining is being recognized as ecological machining due to its less environmental impact and manufacturing cost. However, the choice of dry machining is mainly influenced by the workpiece material properties, machining operation and cutting conditions. The recent emergence of austempered ductile iron (ADI) can be considered a significant economic advantage to the increasing industrial demand for cost- and weight-efficient materials. However, due to its microstructure-induced inherent properties, ADI is considered hard-to-machine material. Thus, the dry drilling of ADI is investigated in this paper. The ADI material used in the present study is produced using an innovative process route for near net shape casting production. Drilling experiments are conducted on a DMU80P Deckel Maho five-axis machining centre using PVD-coated carbide tools under dry cutting environment. The dry drilling of ADI under different cutting conditions is evaluated in terms of specific cutting force and tool wear analysis. The influence of cutting conditions on chip morphology and surface roughness is also investigated. The experimental results revealed that the combination of the low feed rate and higher cutting speed leads to the higher mechanical and thermal loads on the tool's cutting edge, resulting in higher specific cutting force values. This behaviour is further supported by the chip morphology analysis, which revealed the formation of segmented chips at higher cutting speed with segment spacing increase with an increase in feed rate. Depending upon the cutting parameters, different modes of tool failures including crater wear, flank wear, chipping, breakage and built-up edge were observed. Surface roughness analysis revealed the influence of tool wear and chip morphology on the machined surface finish.     

Determination of optimum parameters for multi-performance characteristics in drilling by using grey relational analysis

The theory of grey systems is a new technique for performing prediction, relational analysis and decision making in many areas. In this paper, the use of grey relational analysis for optimising the drilling process parameters for the work piece surface roughness and the burr height is introduced. Various drilling parameters, such as feed rate, cutting speed, drill and point angles of drill were considered. An orthogonal array was used for the experimental design. Optimal machining parameters were determined by the grey relational grade obtained from the grey relational analysis for multi-performance characteristics (the surface roughness and the burr height). Experimental results have shown that the surface roughness and the burr height in the drilling process can be improved effectively through the new approach .     

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.     

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.     

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.     

Selection of an optimal parametric combination for achieving a better surface finish in dry milling using genetic algorithms

In machining, coolants improve machinability, increase productivity by reducing tool wear and extend tool life. However, due to ecological and human health problems, manufacturing industries are now being forced to implement strategies to reduce the amount of cutting fluids used in their production lines. A trend that has emerged to solve these problems is machining without fluid – a method called dry machining – which has been made possible due to technological innovations. This paper presents an experimental investigation of the influence of tool geometry (radial rake angle and nose radius) and cutting conditions (cutting speed and feed rate) on machining performance in dry milling with four fluted solid TiAlN-coated carbide end mill cutters based on Taguchi’s experimental design method. The mathematical model, in terms of machining parameters, was developed for surface roughness prediction using response surface methodology. The optimization is then carried out with genetic algorithms using the surface roughness model developed and validated in this work. This methodology helps to determine the best possible tool geometry and cutting conditions for dry milling.