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.

Surface Roughness Optimization of some Machining Parameters in Turning Operations Using Taguchi Method

2009 ◽  
Vol 62-64 ◽  
pp. 613-620 ◽  
Author(s):  
Ishaya Musa Dagwa
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Signal To Noise Ratio ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Confirmatory Test ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Turning Operations ◽  
Factors Affecting

In this study, an attempt has been made to optimize cutting parameters (cutting speed, depth of cut, and feed rate) in conventional turning operations. A Taguchi orthogonal array (L933) was used in surface roughness optimization of a solid round bar of mild steel material. The experimental runs were randomized; two skilled machinists were involved in the turning operation using the same machining parameters. ANOVA analysis was performed to identify the percentage contribution of the factors affecting surface roughness during machining. The optimal cutting combination was determined by using the signal-to-noise ratio and the following results were obtained; speed (level 2) = 55.m/min, depth of cut (level 3) = 0.08mm, and feed rate (levels 3) = at 0.08mm/rev. A prediction of surface roughness was carried out using the optimal setting followed by a confirmatory test on the lathe. The result shows that the confirmatory runs compared favourably (96.44%) with the predicted surface roughness.

Surface Roughness Prediction Based on Cutting Parameters and Nose Radius in Precision Turning

2007 ◽  
Vol 329 ◽  
pp. 539-544 ◽  
Author(s):  
Ying Chun Liang ◽  
Yuan Sheng Zhai ◽  
H.X. Wang ◽  
Qing Shun Bai ◽  
Y. Zhao
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Ratio Test ◽  
Nose Radius ◽  
Precision Turning ◽  
Proposed Model ◽  
Roughness Prediction ◽  
Optimal Cutting Parameters

In precision turning, the quality of surface finish is an important requirement for machined workpiece. Thus, the choice of optimal cutting parameters is very important for controlling the required surface quality. The focus of the present study is to find a correlation between surface roughness and cutting parameters (feed rate, depth of cut) and nose radius in turning 3J33 maraging steel, and to derive mathematical models for the predicted surface roughness based on both of cutting parameters and nose radius. The experimental design is carried out using the quadratic rotary combination design. The regression analysis shows feed rate and nose radius influence surface roughness significantly. With F-ratio test the proposed model is adequate. The method could be useful in predicting roughness parameters as a function of cutting parameters and nose radius.

Machinability Study on SiC Particle Reinforced Aluminum Alloy Composite (SiCp/Al) Material With CVD Diamond Coated End Mills

2017 ◽  
Author(s):  
Alexandro Vargas ◽  
Tony Nguyen ◽  
Jiancheng Liu
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Cutting Tools ◽  
Cutting Parameters ◽  
Burr Formation ◽  
Depth Of Cut ◽  
Silicon Carbide Particle ◽  
End Mills ◽  
Optimal Cutting Parameters ◽  
Tool Rotation

Particle-reinforced metal matrix composites (pMMC) such as silicon carbide particle reinforced aluminum alloys (SiCp/Al) require special cutting tools due to the high hardness and abrasive properties of the ceramic particles. Diamond coated cutting tools are ideal for machining this type of pMMC. Previous research studies focus on the machinability of pMMCs with low ceramic content. The aim of this research is to determine the optimal cutting parameters for machining SiCp/Al material containing high silicon carbide particle reinforcement (>25%). Material removal rate (MRR) was used to determine the optimal cutting parameters with the tool wear and surface roughness as constraints. Cutting speed, feed rate, and depth of cut were used as design parameters for the design of experiment. High burr formation and cutting forces were observed during the experiments. Experimental milling tests are conducted using CVD diamond coated end mills and non-diamond tungsten carbide end mills. It was found that low tool rotation speeds, feed rates and depths of cut are necessary to achieve smoother surface finishes of Ra < 1 μm. A high MRR to low tool wear and surface roughness ratio was obtainable at a tool rotation speed of 6500 r/min, feed rate of 762 mm/min, and depth of cut of 3 mm. Results showed that a smooth surface roughness of the workpiece material was achieved with non-diamond tungsten carbide end mills, however, this was at the expense of extreme tool wear and high burr formation. An endurance test was run to test for complete tool failure.

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 Finish and Optimization of Machining Parameters in Turning

2012 ◽  
Vol 463-464 ◽  
pp. 679-683
Author(s):  
Devi Prasad ◽  
Prasad Krishna ◽  
Shrikantha Rao
Keyword(s):  
Surface Roughness ◽  
Surface Finish ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Validity And Reliability ◽  
Nose Radius ◽  
Radial Acceleration ◽  
Feed Forward Back Propagation ◽  
Taguchi Design Of Experiments

Surface roughness plays a crucial role in the functional capacity of machined parts. In this work, experiments were carried out on a conventional lathe for different cutting parameters namely feed, spindle speed, depth of cut and tool nose radius according to Taguchi Design of Experiments. Radial acceleration readings were taken with an accelerometer. Optimum cutting parameters and their level of significance were found using Taguchi analysis (ANOVA). Regression analysis was carried out to identify whether the experimental roughness values have fitness characteristic with the process parameters. Recurrence Plots (RP) were obtained using the sensor signals which determine surface roughness qualitatively and Recurrence Quantification Analysis (RQA) technique was used to quantify the RP obtained. Surface finish was predicted using a feed forward back propagation neural network with RQA parameters, cutting parameters and acceleration data as inputs to the network. The validity and reliability of the methods were verified experimentally.

Effects of Insert Nose Radius and Processing Cutting Parameter on the Surface Roughness of Aisi 316 Stainless Steel

2010 ◽  
Vol 447-448 ◽  
pp. 51-54
Author(s):  
Mohd Fazuri Abdullah ◽  
Muhammad Ilman Hakimi Chua Abdullah ◽  
Abu Bakar Sulong ◽  
Jaharah A. Ghani
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Surface Finish ◽  
Feed Rate ◽  
Cutting Speed ◽  
Chip Thickness ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Nose Radius ◽  
Aisi 316

The effects of different cutting parameters, insert nose radius, cutting speed and feed rates on the surface quality of the stainless steel to be use in medical application. Stainless steel AISI 316 had been machined with three different nose radiuses (0.4 mm 0.8 mm, and 1.2mm), three different cutting speeds (100, 130, 170 m/min) and feed rates (0.1, 0.125, 0.16 mm/rev) while depth of cut keep constant at (0.4 mm). It is seen that the insert nose radius, feed rates, and cutting speed have different effect on the surface roughness. The minimum average surface roughness (0.225µm) has been measured using the nose radius insert (1.2 mm) at lowest feed rate (0.1 mm/rev). The highest surface roughness (1.838µm) has been measured with nose radius insert (0.4 mm) at highest feed rate (0.16 mm/rev). The analysis of ANOVA showed the cutting speed is not dominant in processing for the fine surface finish compared with feed rate and nose radius. Conclusion, surface roughness is decreasing with decreasing of the feed rate. High nose radius produce better surface finish than small nose radius because of the maximum uncut chip thickness decreases with increase of nose radius.

Multiresponse Optimization of Al Alloy-SiC Composite Machining Parameters for Minimum Tool Wear and Maximum Metal Removal Rate

2013 ◽  
Vol 135 (2) ◽  
Author(s):  
Rajesh Kumar Bhushan
Keyword(s):  
Tool Wear ◽  
Metal Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Al Alloy ◽  
Machining Parameters ◽  
Metal Removal Rate ◽  
Nose Radius ◽  
Multiresponse Optimization

Optimization in turning means determination of the optimal set of the machining parameters to satisfy the objectives within the operational constraints. These objectives may be the minimum tool wear, the maximum metal removal rate (MRR), or any weighted combination of both. The main machining parameters which are considered as variables of the optimization are the cutting speed, feed rate, depth of cut, and nose radius. The optimum set of these four input parameters is determined for a particular job-tool combination of 7075Al alloy-15 wt. % SiC (20–40 μm) composite and tungsten carbide tool during a single-pass turning which minimizes the tool wear and maximizes the metal removal rate. The regression models, developed for the minimum tool wear and the maximum MRR were used for finding the multiresponse optimization solutions. To obtain a trade-off between the tool wear and MRR the, a method for simultaneous optimization of the multiple responses based on an overall desirability function was used. The research deals with the optimization of multiple surface roughness parameters along with MRR in search of an optimal parametric combination (favorable process environment) capable of producing desired surface quality of the turned product in a relatively lesser time (enhancement in productivity). The multi-objective optimization resulted in a cutting speed of 210 m/min, a feed of 0.16 mm/rev, a depth of cut of 0.42 mm, and a nose radius of 0.40 mm. These machining conditions are expected to respond with the minimum tool wear and maximum the MRR, which correspond to a satisfactory overall desirability.

OPTIMIZATION OF PROCESS PARAMETERS AFFECTING CUTTING FORCE, POWER CONSUMPTION AND SURFACE ROUGHNESS USING TAGUCHI-BASED GRAY RELATIONAL ANALYSIS IN TURNING AISI 1040 STEEL

2021 ◽  
Author(s):  
MAHIR AKGÜN
Keyword(s):  
Surface Roughness ◽  
Power Consumption ◽  
Cutting Force ◽  
Cutting Speed ◽  
System Optimization ◽  
Gray Relational Analysis ◽  
Depth Of Cut ◽  
Nose Radius ◽  
Significance Level ◽  
Relational Analysis

This study focuses on optimization of cutting conditions and modeling of cutting force ([Formula: see text]), power consumption ([Formula: see text]), and surface roughness ([Formula: see text]) in machining AISI 1040 steel using cutting tools with 0.4[Formula: see text]mm and 0.8[Formula: see text]mm nose radius. The turning experiments have been performed in CNC turning machining at three different cutting speeds [Formula: see text] (150, 210 and 270[Formula: see text]m/min), three different feed rates [Formula: see text] (0.12 0.18 and 0.24[Formula: see text]mm/rev), and constant depth of cut (1[Formula: see text]mm) according to Taguchi L18 orthogonal array. Kistler 9257A type dynamometer and equipment’s have been used in measuring the main cutting force ([Formula: see text]) in turning experiments. Taguchi-based gray relational analysis (GRA) was also applied to simultaneously optimize the output parameters ([Formula: see text], [Formula: see text] and [Formula: see text]). Moreover, analysis of variance (ANOVA) has been performed to determine the effect levels of the turning parameters on [Formula: see text], [Formula: see text] and [Formula: see text]. Then, the mathematical models for the output parameters ([Formula: see text], [Formula: see text] and [Formula: see text]) have been developed using linear and quadratic regression models. The analysis results indicate that the feed rate is the most important factor affecting [Formula: see text] and [Formula: see text], whereas the cutting speed is the most important factor affecting [Formula: see text]. Moreover, the validation tests indicate that the system optimization for the output parameters ([Formula: see text], [Formula: see text] and [Formula: see text]) is successfully completed with the Taguchi method at a significance level of 95%.

scholarly journals Surface Quality Assessment after Milling AZ91D Magnesium Alloy Using PCD Tool

Materials ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 617 ◽  
Author(s):  
Ireneusz Zagórski ◽  
Jarosław Korpysa
Keyword(s):  
Surface Roughness ◽  
Magnesium Alloy ◽  
High Speed ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Roughness Parameters ◽  
Operational Parameters

Surface roughness is among the key indicators describing the quality of machined surfaces. Although it is an aggregate of several factors, the condition of the surface is largely determined by the type of tool and the operational parameters of machining. This study sought to examine the effect that particular machining parameters have on the quality of the surface. The investigated operation was the high-speed dry milling of a magnesium alloy with a polycrystalline diamond (PCD) cutting tool dedicated for light metal applications. Magnesium alloys have low density, and thus are commonly used in the aerospace or automotive industries. The state of the Mg surfaces was assessed using the 2D surface roughness parameters, measured on the lateral and the end face of the specimens, and the end-face 3D area roughness parameters. The description of the surfaces was complemented with the surface topography maps and the Abbott–Firestone curves of the specimens. Most 2D roughness parameters were to a limited extent affected by the changes in the cutting speed and the axial depth of cut, therefore, the results from the measurements were subjected to statistical analysis. From the data comparison, it emerged that PCD-tipped tools are resilient to changes in the cutting parameters and produce a high-quality surface finish.

scholarly journals Experimental Investigation on Surface Roughness and Tool Wear in Dry Machining of TiC Reinforced Aluminium LM6 Composite

2013 ◽  
Vol 773-774 ◽  
pp. 339-347 ◽  
Author(s):  
Muhammad Yusuf ◽  
M.K.A. Ariffin ◽  
N. Ismail ◽  
S. Sulaiman
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Feed Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Carbide Tool ◽  
Dry Machining ◽  
Workpiece Surface ◽  
High Cutting Speed

With increasing quantities of applications of Metal Matrix Composites (MMCs), the machinablity of these materials has become important for investigation. This paper presents an investigation of surface roughness and tool wear in dry machining of aluminium LM6-TiC composite using uncoated carbide tool. The experiments carried out consisted of different cutting models based on combination of cutting speed, feed rate and depth of cut as the parameters of cutting process. The cutting models designed based on the Design of Experiment Response Surface Methodology. The objective of this research is finding the optimum cutting parameters based on workpiece surface roughness and cutting tool wear. The results indicated that the optimum workpiece surface roughness was found at high cutting speed of 250 m min-1 with various feed rate within range of 0.05 to 0.2 mm rev-1, and depth of cut within range of 0.5 to 1.5 mm. Turning operation at high cutting speed of 250 m min-1 produced faster tool wear as compared to low cutting speed of 175 m min-1 and 100 m min-1. The wear minimum (VB = 42 μm ) was found at cutting speed of 100 m min-1, feet rate of 0.2 mm rev-1, and depth of cut of 1.0 mm until the length of cut reached 4050 mm. Based on the results of the workpiece surface roughness and the tool flank wear, recommended that turning of LM6 aluminium with 2 wt % TiC composite using uncoated carbide tool should be carried out at cutting speed higher than 175 m min-1 but at feed rate of less than 0.05 mm rev-1 and depth of cut less than 1.0 mm.

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