Effect of Cutting Parameters on Surface Roughness When Milling Hardened AISI D2 Steel (56 HRC) Using Taguchi Techniques

2012 ◽  
Author(s):  
Ahmed Zaidan Mohammed Shammari ◽  
Kamal Ati Amwead ◽  
Auday Shaker Hadi
Keyword(s):  
Surface Roughness ◽  
End Milling ◽  
Signal To Noise Ratio ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Cold Extrusion ◽  
Depth Of Cut ◽  
Control Factors ◽  
Aisi D2 ◽  
Steel Aisi

The tool steel identifying AISI D2 is commonly used for cold working operations, such as sheet metal forming, cold extrusion and forging operation. To perform in these applications, they must have excellent strength, hardness, and wear resistance. The aim of the present study is to find optimal process parameters for end milling of hardened steel AISI D2 (56 HRC) using Taguchi method. A L25 array, Taguchi’s signal-to-noise ratio and ANOVA are employed to determine effects of many control factors (spindle speed, feed rate, and depth of cut) on surface roughness. In this paper, results show that the spindle speed is most influencing parameters.

Tool Vibration due to High Speed Micro End Milling Parameters

2013 ◽  
Vol 372 ◽  
pp. 364-368 ◽  
Author(s):  
Abdul Rahman Mohamed ◽  
Nur Atiqah ◽  
Mohammad Yeakub Ali ◽  
M.S.H. Chowdhury
Keyword(s):  
High Speed ◽  
End Milling ◽  
Signal To Noise Ratio ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Signal To Noise ◽  
Tool Vibration ◽  
Milling Parameters ◽  
Micro End Milling

This paper presents the effect of high speed micro end milling parameters on tool vibration during machining of poly (methyl methacrylate) (PMMA). The main focus is to achieve minimum tool vibration by controlling the cutting parameters; spindle speed, feed rate and depth of cut. An empirical model for tool vibration has been developed using Taguchi method. The orthogonal array, signal-to-noise ratio and analysis of variance revealed that high spindle speed is the most influential parameter to increase the level of tool vibration.

Application of Taguchi’s Method for Study of Machining Parameters on Surface Roughness of 7136 Aluminum Alloy in End Milling

2015 ◽  
Vol 809-810 ◽  
pp. 123-128 ◽  
Author(s):  
Alina Bianca Bonţiu Pop
Keyword(s):  
Surface Roughness ◽  
Aluminum Alloy ◽  
Process Parameters ◽  
End Milling ◽  
Signal To Noise Ratio ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Future Research ◽  
Depth Of Cut ◽  
Machining Parameters

Starting with the necessity to identify the optimum values of the cutting parameters which are affecting the surface quality, it is appropriate to use the design of experiment techniques to conduct the experiments. Previous researches [1] focused on the investigation of the effects of machining parameters on surface roughness. In this paper, the experiments were conducted based on the established Taguchi’s technique, L8 orthogonal array using Minitab-17 statistical software. Three machining parameters are chosen as process parameters: Cutting Speed, Feed per tooth and Depth of cut. The orthogonal matrix includes these three factors set for analysis, each with 2 levels associated. The level of influence that the process parameters exert on the surface roughness is analyzed by Taguchi method data analysis. In this case the signal to noise ratio was tacked into account. Also, the recommended configuration regarding the optimum values of these parameters was determined as well as the interactions between them, in order to obtain better surface roughness for 7136 aluminum alloy machining. The final results will be used as data for future research.

Prediction of Surface Roughness and Optimization of Cutting Parameters in CNC Turning of Rotational Features

2020 ◽  
Vol 38 (8A) ◽  
pp. 1143-1153
Author(s):  
Yousif K. Shounia ◽  
Tahseen F. Abbas ◽  
Raed R. Shwaish
Keyword(s):  
Surface Roughness ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Feed Rate ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Non Linear ◽  
Optimization Of Cutting Parameters

This research presents a model for prediction surface roughness in terms of process parameters in turning aluminum alloy 1200. The geometry to be machined has four rotational features: straight, taper, convex and concave, while a design of experiments was created through the Taguchi L25 orthogonal array experiments in minitab17 three factors with five Levels depth of cut (0.04, 0.06, 0.08, 0.10 and 0.12) mm, spindle speed (1200, 1400, 1600, 1800 and 2000) r.p.m and feed rate (60, 70, 80, 90 and 100) mm/min. A multiple non-linear regression model has been used which is a set of statistical extrapolation processes to estimate the relationships input variables and output which the surface roughness which prediction outside the range of the data. According to the non-linear regression model, the optimum surface roughness can be obtained at 1800 rpm of spindle speed, feed-rate of 80 mm/min and depth of cut 0.04 mm then the best surface roughness comes out to be 0.04 μm at tapper feature at depth of cut 0.01 mm and same spindle speed and feed rate pervious which gives the error of 3.23% at evolution equation.

Parameter Optimization of Ball End Milling Process on Inconel 718 Using RSM and TLBO Algorithm

2015 ◽  
Vol 15 (3) ◽  
pp. 293-300 ◽  
Author(s):  
Nandkumar N. Bhopale ◽  
Nilesh Nikam ◽  
Raju S. Pawade
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Surface Integrity ◽  
Inconel 718 ◽  
Tool Path ◽  
End Milling ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Ball End Milling

AbstractThis paper presents the application of Response Surface Methodology (RSM) coupled with Teaching Learning Based Optimization Technique (TLBO) for optimizing surface integrity of thin cantilever type Inconel 718 workpiece in ball end milling. The machining and tool related parameters like spindle speed, milling feed, axial depth of cut and tool path orientation are optimized with considerations of multiple response like deflection, surface roughness, and micro hardness of plate. Mathematical relationship between process parameters and deflection, surface roughness and microhardness are found out by using response surface methodology. It is observed that after optimizing the process that at the spindle speed of 2,000 rpm, feed 0.05 mm/tooth/rev, plate thickness of 5.5 mm and 15° workpiece inclination with horizontal tool path gives favorable surface integrity.

A Comprehensive Study on Surface Roughness in Machining of AISI D2 Hardened Steel

2012 ◽  
Vol 576 ◽  
pp. 60-63 ◽  
Author(s):  
N.A.H. Jasni ◽  
Mohd Amri Lajis
Keyword(s):  
Surface Roughness ◽  
High Speed ◽  
End Milling ◽  
Cutting Speed ◽  
Hardened Steel ◽  
Depth Of Cut ◽  
Feed Speed ◽  
Radial Depth ◽  
Aisi D2 ◽  
Coated Carbide

Hard milling of hardened steel has wide application in mould and die industries. However, milling induced surface finish has received little attention. An experimental investigation is conducted to comprehensively characterize the surface roughness of AISI D2 hardened steel (58-62 HRC) in end milling operation using TiAlN/AlCrN multilayer coated carbide. Surface roughness (Ra) was examined at different cutting speed (v) and radial depth of cut (dr) while the measurement was taken in feed speed, Vf and cutting speed, Vc directions. The experimental results show that the milled surface is anisotropic in nature. Surface roughness values in feed speed direction do not appear to correspond to any definite pattern in relation to cutting speed, while it increases with radial depth-of-cut within the range 0.13-0.24 µm. In cutting speed direction, surface roughness value decreases in the high speed range, while it increases in the high radial depth of cut. Radial depth of cut is the most influencing parameter in surface roughness followed by cutting speed.

Investigation of Surface Roughness and Material Removal Rate for High Speed Micro End Milling on PMMA

2015 ◽  
Vol 1115 ◽  
pp. 12-15
Author(s):  
Nur Atiqah ◽  
Mohammad Yeakub Ali ◽  
Abdul Rahman Mohamed ◽  
Md. Sazzad Hossein Chowdhury
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Feed Rate ◽  
Material Removal ◽  
High Speed ◽  
End Milling ◽  
Removal Rate ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Micro End Milling

Micro end milling is one of the most important micromachining process and widely used for producing miniaturized components with high accuracy and surface finish. This paper present the influence of three micro end milling process parameters; spindle speed, feed rate, and depth of cut on surface roughness (Ra) and material removal rate (MRR). The machining was performed using multi-process micro machine tools (DT-110 Mikrotools Inc., Singapore) with poly methyl methacrylate (PMMA) as the workpiece and tungsten carbide as its tool. To develop the mathematical model for the responses in high speed micro end milling machining, Taguchi design has been used to design the experiment by using the orthogonal array of three levels L18 (21×37). The developed models were used for multiple response optimizations by desirability function approach to obtain minimum Ra and maximum MRR. The optimized values of Ra and MRR were 128.24 nm, and 0.0463 mg/min, respectively obtained at spindle speed of 30000 rpm, feed rate of 2.65 mm/min, and depth of cut of 40 μm. The analysis of variance revealed that spindle speeds are the most influential parameters on Ra. The optimization of MRR is mostly influence by feed rate. Keywords:Micromilling,surfaceroughness,MRR,PMMA

Improvement of Surface Roughness in End Milling of Ti6Al4V by Coupling RSM with Genetic Algorithm

2011 ◽  
Vol 264-265 ◽  
pp. 1154-1159
Author(s):  
Anayet Ullah Patwari ◽  
A.K.M. Nurul Amin ◽  
S. Alam
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
Titanium Alloys ◽  
End Milling ◽  
Fitness Function ◽  
Cutting Speed ◽  
Weight Ratio ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Resistance Surface

Titanium alloys are being widely used in the aerospace, biomedical and automotive industries because of their good strength-to-weight ratio and superior corrosion resistance. Surface roughness is one of the most important requirements in machining of Titanium alloys. This paper describes mathematically the effect of cutting parameters on Surface roughness in end milling of Ti6Al4V. The mathematical model for the surface roughness has been developed in terms of cutting speed, feed rate, and axial depth of cut using design of experiments and the response surface methodology (RSM). Central composite design was employed in developing the surface roughness models in relation to primary cutting parameters. The experimental results indicate that the proposed mathematical models suggested could adequately describe the performance indicators within the limits of the factors that are being investigated. The developed RSM is coupled as a fitness function with genetic algorithm to predict the optimum cutting conditions leading to the least surface roughness value. MATLAB 7.0 toolbox for GA is used to develop GA program. The predicted results are in good agreement with the experimental one and hence the model can be efficiently used to achieve the minimum surface roughness value.

Estimation of fractal dimension and surface roughness based on material characteristics and cutting conditions in the end milling of carbon steels

2015 ◽  
Vol 231 (8) ◽  
pp. 1423-1437 ◽  
Author(s):  
Xue Zuo ◽  
Hua Zhu ◽  
Yuankai Zhou ◽  
Jianhua Yang
Keyword(s):  
Surface Roughness ◽  
Fractal Dimension ◽  
Feed Rate ◽  
End Milling ◽  
Cutting Parameters ◽  
Carbon Steels ◽  
Spindle Speed ◽  
Numerical Control ◽  
Material Hardness ◽  
Milled Surface

Cutting parameters and material properties have important effects on the quality of milled surface, which can be characterized by fractal dimension and surface roughness. The relationships between two surface parameters (surface roughness and fractal dimension) and material hardness, elongation, spindle speed and feed rate were investigated, respectively, in this study. Four carbon steels, that is, AISI 1020, Gr 50, 1045 and 1566, were milled with five spindle speeds and four feed rates on a computer numerical control machine. The surface topographies were measured with a three-dimensional profiler. The surface profiles were obtained by re-sampling the data points on the surface topography in the measurement direction. The surface roughness and fractal dimension were calculated from the two-dimensional profiles, where the fractal dimension was obtained by the root-mean-square method. The results showed that for specific spindle speed and feed rate, the roughness of the milled surface decreased with the workpiece hardness, whereas the elongation and fractal dimension increased with the hardness. Based on the material hardness and elongation, spindle speed and feed rate, empirical formulae were established to quantitatively estimate the surface roughness and fractal dimension. Moreover, the spindle speed and feed rate can be easily calculated from the empirical formulae to achieve a surface with the desired surface roughness and fractal dimension. The empirical formulae have been demonstrated with the experiments and were shown to be applicable in estimating the surface roughness and fractal dimension for all carbon steels in end milling. The results are instructive for the fractal dimension estimation of the machined surfaces of carbon steel, which has not been previously studied.

Optimization of Machining Parameters of Valve Steel SUH03 (X45CrSiMo10-2) Using Gray Based Taguchi Method

2015 ◽  
Vol 813-814 ◽  
pp. 376-381 ◽  
Author(s):  
B. Yazhini ◽  
S. Rajeswari ◽  
Sivasakthivel
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Signal To Noise Ratio ◽  
Cutting Parameters ◽  
Gray Relational Analysis ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Nose Radius ◽  
Valve Steel ◽  
Optimal Cutting Parameters

This paper embarks the machining parameters of Turning by optimization using Taguchi’s approach. The optimization is very essential in order to obtain the expected surface quality. The results of cutting parameters of optimization is seen in the Surface Roughness, Tool wear and MRR of the material. The L18 Orthogonal array has been chosen for the optimization of Valve Steel SUH03.The uncoated carbide inserts were used and the four parameters Speed, Feed, Depth of Cut and Nose Radius has been taken as input parameters. The Signal to Noise ratio and Analysis of Variance software has been analyzed using Minitab software through which the optimal cutting parameters of the best surface roughness, tool wear and MRR has been obtained. The final results have been compared by the Gray relational analysis to find the optimum machining conditions of all the parameters.

scholarly journals Studies on Surface Roughness in Stable and Unstable End-Milling

2020 ◽  
Author(s):  
Mahdi Eynian ◽  
Sunday Ogheneochuko Usino ◽  
Ana Esther Bonilla Hernández
Keyword(s):  
Surface Roughness ◽  
End Milling ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Sudden Increase ◽  
Stability Lobe ◽  
Stable Conditions ◽  
End Mills ◽  
The Stability ◽  
Stability Lobe Diagrams

Surface roughness is an important aspect of a machined piece and greatly influences its performance. This paper presents the surface roughness of end-milled aluminium plates in stable and unstable machining conditions at various spindle speed and depth of cuts machined with cylindrical end-mills. The surface roughness is measured using high-resolution surface replicas with a white light interferometry (WLI) microscope. The measurements of the end-milled floors show that the surface roughness as long as the cutting is performed in stable conditions is insensitive to the depth of cut or spindle speed. In contrast, within chattering conditions, which appear according to stability lobes, surface roughness values increase almost 100%. While at the valleys of the stability lobe diagram, there is a gradual increase in roughness, at the peaks of the stability lobe, the transition from the stable to unstable condition occurs with a sudden increase of the roughness values. In the study of down-milled walls, while the roughness increases with the depth of cut within both the stable and the chattering regions, the transition from the stable to chattering condition can lead to a much larger increase in the surface roughness. These results could be used for strategic selection of operation considering the needs of robustness and possible variation of dynamic parameters that can affect the position of the cutting conditions within the stability lobe diagrams.

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