IJMET_09_12_092.pdf

—Tool selection is a critical part during manufacturing process. The tool geometry plays a vital role in the art of machining to produce the part to meet the quality requirements. The tool parameters which play major roles are tool material, tool geometry, size of the tool and coating of the tool. Out of these, selection of right kind of tool geometry plays a major role by reducing cutting forces and induced stresses, energy consumptions and temperature. All this will leads to reduced distortions and the selection of wrong tool geometry results in enhanced tool cost and loss in production. However these tool geometric features are often neglected during machining considerations and procurement of tools. Thus the objective of the study is to analyze the contribution of tool geometry in peripheral milling operation and to find the optimized helix angle to get minimum cutting force (useful in thin wall machining) and thereby ensuring perpendicularity and best surface finish to reduce the chatter vibration and deflection by optimizing the machining parameters such as spindle speed, feed per tooth and side cut. The experiments are conducted on CNC milling machine on aluminium alloy 2014 using solid carbide end mills of 10 mm diameter with various helix angles by making all other geometric features constant. Taguchi method is used for design of experiment. The optimum level of parameters has been identified using Grey relational analysis (GRA) and also the percentage contribution is identified using ANOVA.

IRJET, 2022

End milling is an operation which employs an end mill cutter for face milling, edge milling and slot milling. It is a very widely used machining operation that is extensively used in shipyard, automotive & aerospace industries. Different types of tool paths can be used during the end milling of the AISI D3 material. Each toolpath has its own effects on the performance characteristics of slot milling. Some toolpaths may provide better surface roughness while other toolpaths may provide lower cutting force. The manufacturer wants to produce the machining components with better surface finish. At the same time the manufacturer wants lower cutting force and less amount of energy to be spent while machining the components. Hence, it is important to determine the best toolpath and cutting conditions which will provide lower surface roughness of them machined component, lower cutting force on the tool and less amount of energy spent for the machining. This research result would identify the best toolpath and optimum cutting parameter which will provide better surface finish as well as low cutting force and specific cutting energy.

2013

In today's world AISI 304 stainless steel contributes to almost half of the world's production and consumption for industrial purposes. Austenitic steels are hard materials to machine due to their high strength, high ductility and low thermal conductivity. This paper reports an experimental study on performance characteristics of AISI 304 stainless steel during CNC milling process. In milling process the surface roughness (SR) and material removal rate (MRR) are the most important performance characteristics, which are influenced by many factors like cutting speed, feed rate and depth of cut. The selection of these parameters at optimum level plays a vital role in getting minimum surface roughness and maximum MRR. This paper presents multi-objective optimization of milling process parameters using Grey-Taguchi method in machining of AISI 304 stainless steel. The experiments are conducted based on Taguchi's L27 orthogonal array by taking cutting speed, feed rate and depth of cut at three levels. The Grey relational analysis is used to obtain the relation between the machining parameters and performance characteristics. The complete experimental results are discussed and presented in this paper.

This study investigates the optimization of CNC end milling operation parameters for stainless steel 304 using Taguchi methodology and Grey Relational Analysis approach. Surface Roughness (Ra), Material Removal Rate (MRR) were selected as the quality, productivity target respectively. The experiments were conducted based on Taguchi " s L 9 orthogonal array by selecting cutting speed (mm/min), feed rate (mm/rev) and depth of cut (mm) at three levels. The grey relational analysis was used to obtain multi objective relation between the machining parameters and performance characteristics. Response table and graphs were used to find the optimal levels of parameters in CNC end milling process and found to be V c 2-f1-d3. The confirmation experiments were carried out to validate the optimal results. Thus, the machining parameters for CNC end milling were optimized for achieving the combined objectives like lower surface roughness and higher rate of material removal rate on Stainless Steel 304 in this work. It has been observed that depth of cut is most significant factor followed by feed and cutting speed.

The current study aims at investigating the influence of different machining parameters such as cutting speed (Vc), feed (f) and depth of cut (t) on different performance measures during dry turning of AISI 304 austenitic stainless steel. ISO P30 grade uncoated cemented carbide inserts was used a cutting tool for the current purpose. L27 orthogonal array design of experiments was adopted with the following machining parameters: Vc= 25, 35, 45 m/min., f= 0.1, 0.15, 0.2 mm/rev. and t= 1, 1.25, 1.5 mm. Three important characteristics of machinability such as material removal rate (MRR), cutting force (Fc) and surface roughness (Ra) were measured. Attempt was further made to simultaneously optimize the machining parameters using grey relational analysis. The recommended parametric combination based on the studied performance criteria (i.e. MRR, Fc and Ra) was found to be Vc =45m/min, f=0.1mm/rev, t=1.25mm. A confirmatory test was also carried out to support the analysis and an improvement of 88.78% in grey relational grade (GRG) was observed.

The present study applied Taguchi method through a case study in straight turning of AISI 202 stainless steel bar on CNC Machine ( Mfd by ACE DESIGNERS) using Titanium Carbide tool for the optimization of Material removal rate, Surface Roughness and tool wear process parameter.The study aimed at evaluating the best process environment which could simultaneously satisfy requirements of both quality as well as productivity with special emphasis on maximizing material removal rate and minimizing surface roughness and tool flank wear at various combination of cutting speed, feed, depth of cut. The predicted optimal setting ensured maximum MRR and minimum surface roughness and tool wear. Since optimum material removal rate is desired, so higher the better criteria of Taguchi signal to noise ratio is used for MRR -SN s = -10 log(Sy 2 /n) For surface roughness and tool wear -SN L = -10 log(S(1/y 2 )/n) The results have been verified with the help of S/N Ratios calculation and various graphs have been plotted to show the below mentioned observations. a) MRR first increases with increase in cutting speed and then decreases. b) With the increase in feed, MRR increases. c) With the increase in depth of cut, MRR first increases and then decreases. d) With the increase in cutting speed, Surface Roughness first decreases and then increases. e) With the increase in feed, Surface Roughness increases. f) With the increase in depth of cut, Surface Roughness first increases and then decreases.

IOP Conference Series: Materials Science and Engineering, 2018

In pocket milling, a need for optimization of output quality characteristics is necessary for difficult to machine materials such as Ti-6Al-4V due to its applications in dies, molds, aerospace and mechanical industries. This paper shows the incorporation of tool path orientation as a process parameter along with spindle speed, feed rate and depth of cut to improve the output quality characteristics. Optimization was accomplished by varying the one direction tool path orientation together with machining parameters using Taguchi based Grey Relational analysis. The output quality characteristics determined are radial tool deflection, surface roughness and material removal rate (MRR). The optimum combination of process parameters obtained from the grey relational analysis was then validated by performing confirmation tests.

International Journal of Engineering Research and Technology (IJERT), 2013

https://www.ijert.org/multi-response-optimization-of-milling-parameters-on-aisi-304-stainless-steel-using-grey-taguchi-method https://www.ijert.org/research/multi-response-optimization-of-milling-parameters-on-aisi-304-stainless-steel-using-grey-taguchi-method-IJERTV2IS80770.pdf In today's world AISI 304 stainless steel contributes to almost half of the world's production and consumption for industrial purposes. Austenitic steels are hard materials to machine due to their high strength, high ductility and low thermal conductivity. This paper reports an experimental study on performance characteristics of AISI 304 stainless steel during CNC milling process. In milling process the surface roughness (SR) and material removal rate (MRR) are the most important performance characteristics, which are influenced by many factors like cutting speed, feed rate and depth of cut. The selection of these parameters at optimum level plays a vital role in getting minimum surface roughness and maximum MRR. This paper presents multi-objective optimization of milling process parameters using Grey-Taguchi method in machining of AISI 304 stainless steel. The experiments are conducted based on Taguchi's L27 orthogonal array by taking cutting speed, feed rate and depth of cut at three levels. The Grey relational analysis is used to obtain the relation between the machining parameters and performance characteristics. The complete experimental results are discussed and presented in this paper.

As the advancement in the technology some new material are comes in a trend which offers high strength, hardness and resistance to heat. Face milling is a very common method for metal cutting and the finishing of machined parts. The input machining parameters being consider in this research are spindle speed, tool feed and depth of cut. The stainless steel 202 used as work piece. The tool used for the face milling operation is High Speed Steel face milling cutter. The Taguchi's L16 orthogonal array has been used to design the combinations of parameters for the experiments. The surface roughness of SS 202 has been measured after face milling operations and also material removal rate has been measured after the operations. The machining experiments are performed on Computer Numerical Control Vertical Milling Machine (640). Grey Relational analysis is used for finding the optimal values from the all experimental values. The optimum levels of input parameters have been found by Grey analysis are spindle speed 2000 rpm, 400 mm/rev tool feed and 0.2 mm depth of cut for maximum material removal rate and minimum surface roughness.

IRJET, 2022

Milling process plays a vital role in the machining processes. The efficiency of the milling process can be increased by different methods and by developing empirical relations between different parameters. Experimentally determined values can be optimized by using different techniques. The empirical models and investigate the optimal machinability parameters of milling process during machining EN 31 tool steel. In this consequence, milling experiments were conducted on vertical milling center based on central composite design with 27 experiments. The response surface methodology was adopted to develop the mathematical models for the responses and ANOVA is used to check the adequacy of the developed models and were found that the developed second order models can explain the variation in the temperature up to the extent of 98.06% and 99.07%. Then these experimentally measured values were carried to the optimization. GRA was successfully implemented to the measured experimental runs. Therefore, the present work enables the industries to perform the CNC milling operations on the hardened EN 31 material within the optimal levels of tool temperatures by maximizing the metal removal rate.