Optimisation of Machining Parameters for High Feed End Milling on Inconel 718 Super Alloy

2014 ◽  
Vol 592-594 ◽  
pp. 584-590 ◽  
Author(s):  
Vinay Varghese ◽  
K. Annamalai ◽  
K. Santhosh Kumar
Keyword(s):  
Inconel 718 ◽  
End Milling ◽  
Removal Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Milling Operation ◽  
High Feed ◽  
Grey Relational ◽  
Super Alloy

This study investigates about machining practices used worldwide for machining of Inconel 718 super alloy. The effect of machining parameters like cutting speed, feed and depth of cut on machining responses like surface roughness and material removal rate when end milling Inconel 718 is studied using nine trials carried out based on L9 orthogonal array. A Taguchi based grey relational analysis was used for optimisation of machining parameters for high feed end milling operation on Inconel 718. An analysis of variance (ANOVA) was used to find the most significant factor. Validation of results through confirmation tests was performed and experimental results show that surface quality and productivity can be improved efficiently with this approach.

The Use of the Taguchi-Grey Based to Optimize High Speed End Milling with Multiple Performance Characteristics

2006 ◽  
Vol 505-507 ◽  
pp. 835-840 ◽  
Author(s):  
Shen Jenn Hwang ◽  
Yunn Lin Hwang ◽  
B.Y. Lee
Keyword(s):  
High Speed ◽  
End Milling ◽  
Removal Rate ◽  
Cutting Speed ◽  
Performance Characteristics ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Machining Performance ◽  
Grey Relational Grade ◽  
Grey Relational

This paper presents a new approach for the optimization of the high speed machining (HSM) process with multiple performance characteristics based on the orthogonal array with the grey relational analysis has been studied. Optimal machining parameters can then be determined by the grey relational grade as the performance index. In this study, the machining parameters such as cutting speed, feed rate and axial depth of cut are optimized under the multiple performance characteristics including, tool life, surface roughness, and material removal rate(MMR). As shown experimental results, machining performance in the HSM process can be improved effectively through this approach.

Investigation on Laser Assisted Micro Ball Milling of Inconel 718

2014 ◽  
Vol 660 ◽  
pp. 79-83 ◽  
Author(s):  
E.A. Rahim ◽  
N.M. Warap ◽  
Zazuli Mohid ◽  
R. Ibrahim
Keyword(s):  
Inconel 718 ◽  
Cutting Tool ◽  
Cutting Speed ◽  
Processing Parameters ◽  
Micro Milling ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
New Techniques ◽  
Milling Parameters ◽  
Super Alloy

Micro milling of super alloy materials such as nickel based alloys is challenging due to the excellent of its mechanical properties. Therefore, new techniques have been suggested to enhance the machinability of nickel based alloys by pre-heating the workpiece’s surface to reduce its strength. Determining the processing parameters and their effects to the processing characteristics are crucially important. However, not only the micro-milling parameters need to be considered, but the pre-heating parameters are also need to take into consideration as well. These parameters are expected to improve the machinability. In this study, the experiment of LAMM in Inconel 718 was conducted with considering laser power, cutting speed, depth of cut, feed rate and laser-to-cutting tool distance. From the result, the effectiveness of laser assisted and cutting parameter in term of cutting force and tool wear was identified by comparing between conventional and LAMM. Finally, the optimum range of machining parameters can be determined.

Surface Roughness Prediction in End Milling of Machinable Glass Ceramic and Optimization by Response Surface Methodology

2011 ◽  
Vol 189-193 ◽  
pp. 1376-1381
Author(s):  
Moola Mohan Reddy ◽  
Alexander Gorin ◽  
Khaled A. Abou El Hossein
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Glass Ceramic ◽  
End Milling ◽  
Removal Rate ◽  
Cutting Speed ◽  
Lower Surface ◽  
Depth Of Cut ◽  
Machining Parameters

This paper presents the prediction of a statistically analyzed model for the surface roughness,R_a of end-milled Machinable glass ceramic (MGC). Response Surface Methodology (RSM) is used to construct the models based on 3-factorial Box-Behnken Design (BBD). It is found that cutting speed is the most significant factor contributing to the surface roughness value followed by the depth of cut and feed rate. The surface roughness value decreases for higher cutting speed along with lower feed and depth of cut. Additionally, the process optimization has also been done in terms of material removal rate (MRR) to the model’s response. Ideal combinations of machining parameters are then suggested for common goal to achieve lower surface roughness value and higher MRR.

scholarly journals Multiple Response Optimization of machining parameters on turning of AA 6063 T6 aluminum alloy which established on Taguchi L9 orthogonal array coupled with Grey relational analysis

2021 ◽  
pp. 974-982
Author(s):  
D. S. Sai Ravi Kiran ◽  
Sanapala Sri Ram ◽  
Tangeti Bhaskararao ◽  
Boddu Eswar Venkat Sai ◽  
Kari Suraj Kumar ◽  
...  
Keyword(s):  
Aluminum Alloy ◽  
Orthogonal Array ◽  
Removal Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Dry Cutting ◽  
Grey Relational Grade ◽  
Grey Relational

With numerous responses established on Taguchi L9, orthogonal array coupled with current work proposes a novel methodology for optimizing machining parameters on turning of AA 6063 T6 aluminum alloy. Experimental assessments are accomplished on AA 6063 T6 aluminum alloy. Turning trails are carried out under dry cutting conditions using an uncoated carbide insert. Cutting parameters such as cutting speed, feed rate, and depth of cut are optimized in this effort while numerous responses such as surface roughness(Ra) and material removal rate are taken into consideration (MRR). From the grey analysis, a grey relational grade(GRG) is calculated. The optimal amounts of parameters have been identified based on the values of grey relational grade, and then ANOVA is used to determine the significant influence of parameters. To authenticate the test result, a confirmation test is executed. The result of the experiments shows that by using this method. the turning process responses can be significantly improved.

Investigation on Wear Behavior and Chip Formation During Up-Milling and Down-Milling Operations for Inconel 718

2014 ◽  
Vol 66 (3) ◽  
Author(s):  
M. A. Hadi ◽  
J. A. Ghani ◽  
C. H. Che Haron ◽  
M. S. Kasim
Keyword(s):  
Tool Wear ◽  
Chip Formation ◽  
Inconel 718 ◽  
End Milling ◽  
Wear Behavior ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Milling Operations ◽  
Milling Operation ◽  
Coated Carbide

A comprehensive study and FEM simulation of ball nose end milling on tool wear behavior and chip formation had been performed on Inconel 718 (nickle-based superalloy) under minimum quantity lubricant (MQL) condition. In this paper, the investigation was focusing on the comparison of up-milling and down-milling operations using a multi-layer TiAlN/AlCrN-coated carbide inserts. A various cutting parameters; depth of cut, feed rate and cutting speed were considered during the evaluation. The experimental results showed that down-milling operation has better results in terms of tool wear compared to up-milling operation. Chipping on cutting tool edge responsible to notch wear with prolong machining. It was observed that the chips formed in up-milling operation were segmented and continuous, meanwhile down-milling operation produced discontinuous type of chips.

Multi-Objective Optimization using Grey Relational Taguchi Analysis in Machining

2016 ◽  
Vol 6 (4) ◽  
pp. 45-64 ◽  
Author(s):  
Nirmal S. Kalsi ◽  
Rakesh Sehgal ◽  
Vishal S. Sharma
Keyword(s):  
Metal Cutting ◽  
Removal Rate ◽  
Cutting Tools ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Recent Approach ◽  
Grey Relational

Multi-objective optimization is becoming important day by day due to increase in complexity of the processes and expectations of more reliable solutions. In view of the complexity of the process, controlling the machining parameters without compromising on the response parameters is a tedious process. In the recent approach, researchers have used many combinations of available techniques to solve multi performance characteristic problems depending upon the situation and accuracy desired in the results, to make the results more reliable. In this paper, the authors have pronounced and used a combination of grey relational and Taguchi based analysis to optimize a multi-objective metal cutting process to yield maximum performance of cutting tools in turning. Main cutting force, power consumption, tool wear and material removal rate were evaluated used L18 orthogonal array considering cutting speed, feed rate and depth of cut, using cryogenically treated and untreated tungsten carbide cutting tool inserts.

Optimisation of End Milling Machining Parameters Using the Taguchi Method and ANOVA of AlSi/AlN Metal Matrix Composite Material

2016 ◽  
Vol 701 ◽  
pp. 200-204 ◽  
Author(s):  
Mohamad Sazali Said ◽  
Jaharah A. Ghani ◽  
Mohd Asri Selamat ◽  
Nurul Na'imy Wan ◽  
Hassan C.H. Che
Keyword(s):  
Matrix Composite ◽  
Tool Life ◽  
Feed Rate ◽  
End Milling ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
High Feed ◽  
Machining Parameter ◽  
Cutting Speeds

Abstract. The purpose of this research is to determine the optimum machining parameter for Aluminium silicon alloy (AlSi) matrix composite, which has been reinforced with aluminium nitride (AlN), with three types of carbide inserts present. Experiments were conducted at various cutting speeds, feed rates and depths of cut, according to the Taguchi orthogonal array L27. The signal-to-noise (S/N) ratio and analysis of variance are applied to study the characteristic performance of cutting speeds, feed rates, depths of cut and types of tool in measuring the tool life during the milling operation. The analysis of wear was done using a Sometech SV-35 video microscope according to ISO 3686. Through Taguchi analysis, it is concluded that a combination of high feed rate, high depth of cut, low cutting speed and insert TiB2 give a longer tool life. Therefore, the cutting speed of 230 m/min, feed rate of 0.8 mm/tooth, depth of cut of 0.5 mm and type of insert of TiB2 were the optimum machining parameters. These optimum parameters will help the automotive industry to have a competitive machining operation from both economical and manufacturing perspectives.

Influence of Tool Nose Radius and Cutting Performance during Face Milling of Magnesium Alloy for Output Responses by Taguchi based Grey Relation Analysis

2021 ◽  
Vol 13 (2) ◽  
Author(s):  
Durai Kumaran ◽  
S.P. Sundar Singh Sivam ◽  
Harshavardhana Natarajan ◽  
P.R. Shobana Swarna Ratna
Keyword(s):  
Removal Rate ◽  
Cutting Speed ◽  
Face Milling ◽  
Machining Process ◽  
Tool Geometry ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
New Approach ◽  
Grey Relational Grade ◽  
Grey Relational

In order to take advantage of the machining characteristics of magnesium, it is useful to consider recommended tool design and angles. The geometry of the tool can have a large influence on the machining process. Tool geometry can be used to aid with chip flow and clearance, reduce excessive heat generation, reduce tool build up, enable greater feed rates to be employed and improved tool life. This paper presents a new approach for the optimization of machining parameters on face milling of ZE41 with multiple responses based on orthogonal array with grey relational analysis. Machining tests are carried out by inserting 12 mm diameter of insert having 1 flute under dry condition. In this study, machining parameters namely cutting speed, feed and depth of cut and tool node radius are optimized with the considerations of multi responses such as surface roughness, material removal rate, tool wear and thrust force. A grey relational grade is obtained from the grey analysis. Based on the grey relational grade, optimum levels of parameters have been identified and significant contribution of parameters is determined by ANOVA. Confirmation test is conducted to validate the test result. Experimental results have shown that the responses in Machining process can be improved effectively through the new approach.

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.

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