Multiobjective Optimization of End Milling HCHCr Using AlCrN PVD Coated Cutters With MOGA and RSMO

2014 ◽  
Author(s):  
Shantisagar K. Biradar ◽  
Geeta S. Lathkar
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Multiobjective Optimization ◽  
End Milling ◽  
Dominant Role ◽  
Optimization Technique ◽  
Cutting Speed ◽  
Optimization Techniques ◽  
Depth Of Cut ◽  
Machining Parameters

Here the End milling is studied for optimization of responses such as surface roughness and tool wear while machining HCHCr. These two conflicting responses decide the quality of process; therefore the multiobjective optimization technique is used. The Response Surface Optimizer (RSMO) and Multiobjective Genetic Algorithm (MOGA) were used as the multiobjective optimization techniques. The PVD coating of 2.5 micron AlCrN was used on four flute HSS End milling cutter. Input machining parameters were cutting speed, feed rate, depth of cut and percentage concentration of the solid lubricant MoS2 mixed with SAE-20 base oil. The experimentation was carried out using two level full factorial design concept while ANOVA technique has been used to verify the adequacy of mathematical model. It was found that the cutting speed (V) is having most dominant role on surface roughness and tool wear. The sensitivity analysis was carried out for studying sensitivity of input parameters for the responses.

scholarly journals Machinability, Modelling and Statistical Analysis of In-Situ Al–Si–TiB2 Composites

2019 ◽  
Vol 3 (1) ◽  
pp. 28 ◽  
Author(s):  
Jimmy Karloopia ◽  
Shaik Mozammil ◽  
Pradeep Jha
Keyword(s):  
Surface Roughness ◽  
Statistical Analysis ◽  
Tool Wear ◽  
Cutting Forces ◽  
Elevated Temperatures ◽  
Optimization Technique ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Conventional Machining

Aluminum and its alloys have numerous applications in manufacturing, aerospace, and automotive industries. At elevated temperatures, they start to fail in fulfilling their roles and functions. Aluminum-based metal matrix composites (MMCs) are good alternatives for metal and alloys due to their excellent properties. However, the conventional machining of several composites shows complications for a number of reasons, such as high tool wear, poor surface roughness, high machining cost, cutting forces, etc. Numerous studies have already been conducted on the machinability of various MMCs, but the machinability of Al–Si–TiB2 composite is still not well studied. It is of utmost importance that several process parameters of conventional machining are precisely controlled as well as optimized. In this study an effort was made to optimize input parameters such as cutting speed, depth of cut, and feed to obtain well-finished final components with the minimum cutting force and tool wear. These progressions are involved with multiple response characteristics, therefore the exploration of an appropriate multi-objective optimization technique was indeed essential. The performance characteristics of cutting forces and surface roughness were considered for optimization of the machining parameters. Analysis of variance (ANOVA) was employed for the optimization and statistical analysis.

scholarly journals Optimization of Machining Parameters on 7075 Aluminum Alloy using Taguchi and ANOVA for Surface Roughness

2020 ◽  
Vol 8 (5) ◽  
pp. 90-95
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Electrical Discharge Machining ◽  
Optimization Technique ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Anova Analysis ◽  
Tool Wear Rate ◽  
Made In

Optimization of the parameter to provide best solution to reduce the tool wear , surface roughness, cutting forces presented using optimization technique .In present work an experimental study is made. In this Taguchi design of experiment methodology for optimization of parameters on 7075Aluminium alloy using tungsten coated electrode . Experiments were conducted based on L27 standard orthogonal array with three processes parameters are cutting speed, feed, depth of cut . Electrical discharge machining is generally calculated on the basis of Surface Roughness (SR),Tool wear rate (TWR) and cutting force (CF) .The ANOVA(Analysis Of Variance) is used to study the performance characteristics in turning operation . ANOVA placed an important role for producing higher roughness . Finally the software ,MINITAB 17 was used and results obtained

Machining characteristics of Inconel 718 under several cutting conditions based on Taguchi method

2012 ◽  
Vol 227 (9) ◽  
pp. 1889-1897 ◽  
Author(s):  
R Thirumalai ◽  
JS Senthilkumaar ◽  
P Selvarani ◽  
S Ramesh
Keyword(s):  
Surface Roughness ◽  
Inconel 718 ◽  
Flank Wear ◽  
Optimization Technique ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Machining Parameters ◽  
Dry Machining ◽  
Noise Factors

Extensive researchers have conducted several experiments in the past for selecting the optimum parameters in machining nickel based alloy – Inconel 718. These experiments conducted so far are dealt with dry machining and flooded coolant machining of nickel alloy Inconel 718. In this research study, the usage of refrigerated coolant is also dealt with and it is compared with dry machining and flooded coolant machining. Cutting speed, feed and depth of cut are considered as the machining parameters. The effectiveness of the refrigerated coolant in machining the heat resistant super alloy material Inconel 718 with respect to these machining parameters are described in this article. The machinability studies parameters were generated with surface roughness and flank wear. The performance of uncoated carbide cutting tool was investigated at various cutting condition under dry, flooded coolant and refrigerated coolant machining. The relationship between the machining parameters and the performance measures were established and using analysis of variance significant machining parameters determined. This article made an attempt to Taguchi optimization technique to study the machinability performances of Inconel 718. Taguchi approach is an efficient and effective experimental method in which a response variable can be optimized, given various control and noise factors, using fewer experiments than a factorial design. Taguchi’s optimization analysis indicates that the factors level, its significance to influence the surface roughness and flank wear for the machining processes. Confirmation tests were conducted at an optimal condition to make a comparison between the experimental results foreseen from the mentioned correlations.

scholarly journals Statistical analysis of energy consumption, tool wear and surface roughness in machining of Titanium alloy (Ti-6Al-4V) under dry, wet and cryogenic conditions

2019 ◽  
Vol 10 (2) ◽  
pp. 561-573 ◽  
Author(s):  
Muhammad Ali Khan ◽  
Syed Husain Imran Jaffery ◽  
Mushtaq Khan ◽  
Muhammad Younas ◽  
Shahid Ikramullah Butt ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Effective Means ◽  
Sustainable Manufacturing ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Specific Cutting Energy ◽  
Cutting Energy ◽  
Positive Effect

Abstract. Productivity and economy are key elements of any sustainable manufacturing system. While productivity is associated to quantity and quality, economy focuses on energy efficient processes achieving an overall high output to input ratio. Machining of hard-to-cut materials has always posed a challenge due to increased tool wear and energy loss. Cryogenics have emerged as an effective means to improve sustainability in the recent past. In the present research the use of cooling conditions has been investigated as an input variable to analyze its effect on tool wear, specific cutting energy and surface roughness in combination with other input machining parameters of feed rate, cutting speed and depth of cut. Experimental design was based on Taguchi design of experiment. Analysis of Variance (ANOVA) was carried out to ascertain the contribution ratio of each input. Results showed the positive effect of coolant usage, particularly cryogenic, on process responses. Tool wear was improved by 33 % whereas specific cutting energy and surface roughness were improved by 10 % and 9 % respectively by adapting the optimum machining conditions.

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.

Surface Roughness Optimization in End Milling Using the Multi Objective Genetic Algorithm Approach

2012 ◽  
Vol 576 ◽  
pp. 103-106 ◽  
Author(s):  
Muataz H.F. Al Hazza ◽  
Erry Yulian Triblas Adesta ◽  
Muhammad Riza ◽  
M.Y. Suprianto
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
End Milling ◽  
Cutting Speed ◽  
Root Mean Square Roughness ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Machining Optimization

In finishing end milling, not only good accuracy but also good roughness levels must be achieved. Therefore, determining the optimum cutting levels to achieve the minimum surface roughness is important for it is economical and mechanical issues. This paper presents the optimization of machining parameters in end milling processes by integrating the genetic algorithm (GA) with the statistical approach. Two objectives have been considered, minimum arithmetic mean roughness (Ra) and minimum Root-mean-square roughness (Rq). The mathematical models for the surface roughness parameters have been developed, in terms of cutting speed, feed rate, and axial depth of cut by using Response Methodology Method (RSM). Due to complexity of this machining optimization problem, a multi objective genetic algorithm (MOGA) has been applied to resolve the problem, and the results have been analyzed.

Effect of SiCp Reinforcement on Machinability of A356 Alloy Metal Matrix Composites

2016 ◽  
Vol 852 ◽  
pp. 142-148
Author(s):  
K. Jayakumar
Keyword(s):  
Surface Roughness ◽  
Metal Matrix Composites ◽  
Metal Matrix ◽  
End Milling ◽  
Cutting Speed ◽  
A356 Alloy ◽  
Experimental Result ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Matrix Composites

Machining of Aluminum Metal Matrix Composites (AMMCs) is a challenge for manufacturing industries due to their heterogeneous constituents which vary from soft matrix to hard reinforcements and their interfaces. To overcome the difficulties in machining of MMCs, researchers are continuously working to find the optimum process or machining parameters. In this work, End milling studies were carried out in A356 alloy powder-SiC particles (1 μm) in 0, 5, 10, 15 volume % reinforced AMMCs synthesised by vacuum hot pressing (VHP) route.The influence of machining parameters such as cutting speed, feed and depth of cut on the prepared composites in terms of surface roughness (Ra) and material removal rate (MRR) are measured from experimental study. Experiments were conducted as per Taguchi L16 orthogonal array with 4 factors and 4 levels.From the experimental result, it was identified that surface roughness varied from 0.214 μm to 4.115 μm and MRR varied from minimum of 1.11 cm3/min to maximum of 9.65 cm3/min. It is also observed that, MRR increased with increase in machining parameters and reinforcement quantity. Similarly, surface roughness decreased for increase of cutting speed, SiC particle (SiCp) reinforcement and increased for increase in feed and depth of cut. The optimum condition were observed in higher speed, lower feed and higher depth of cut on MMC with higher SiC content (15%) for getting higher machinability.

Preheating in End Milling of AISI D2 Hardened Steel with Coated Carbide Inserts

2009 ◽  
Vol 83-86 ◽  
pp. 56-66 ◽  
Author(s):  
Mohd Amri Lajis ◽  
A.K.M. Nurul Amin ◽  
A.N. Mustafizul Karim ◽  
A.M.K. Hafiz
Keyword(s):  
Tool Wear ◽  
Surface Finish ◽  
End Milling ◽  
Cutting Speed ◽  
Hardened Steel ◽  
Depth Of Cut ◽  
Inductive Heating ◽  
Machining Parameters ◽  
Aisi D2 ◽  
Coated Carbide

This study was conducted to investigate the effect of preheating through inductive heating mechanism in end milling of AISI D2 hardened steel (60-62 HRC) by using coated carbide tool inserts. Apart from preheating, two other machining parameters such as cutting speed and feed were varied while the depth of cut constant was kept constant. Tool wear phenomenon and machined surface finish were found to be significantly affected by preheating temperature and other two variables. End milling operation was performed on a Vertical Machining Centre (VMC). Preheating of the work material to a higher temperature range resulted in a noticeable reduction in tool wear rate leading to a longer tool life. In addition, improved surface finish was obtained with surface roughness values lower than 0.4 μm, leaving a possibility of skipping the grinding and polishing operations for certain applications.

Processing and End Milling Behavioural Study of A356-SiCp Composite

2012 ◽  
Vol 710 ◽  
pp. 338-343 ◽  
Author(s):  
K. Jayakumar ◽  
Jose Mathew ◽  
M.A. Joseph ◽  
R. Suresh Kumar ◽  
P. Chakravarthy
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
End Milling ◽  
Cutting Speed ◽  
Machining Process ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Pressing Method ◽  
Reinforced Composite ◽  
Effect Of Particle Size

Machining process such as milling receives less attention in the study of machinability of composites due to its interrupted cutting and the complexity of the process. In the present study, A356 aluminium alloy powder reinforced with 10 volume % SiC particles of various sizes (1,12.5 and 25 µm) were synthesized by vacuum hot pressing method and the effect of particle size on the composites were analysed for its mechanical properties and machinability. End milling of these composites were carried out and the surface roughness and resultant cutting force were analysed with the change of machining parameters and varying SiC particle sizes. The minimum cutting force and surface roughness were obtained for a finer particle (1 µm) reinforced composite with higher cutting speed, low feed and depth of cut.

The Effect of Cutting Speed and Feed Rate on Surface Roughness and Tool Wear when Machining Machining D2 Steel

2017 ◽  
Vol 909 ◽  
pp. 80-85 ◽  
Author(s):  
Mohd Rasidi Ibrahim ◽  
Tharmaraj Sreedharan ◽  
Nurul Aisyah Fadhlul Hadi ◽  
Mohammad Sukri Mustapa ◽  
Al Emran Ismail ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Surface Quality ◽  
Feed Rate ◽  
Input Parameter ◽  
Cutting Temperature ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
D2 Steel

Machining parameters is a main aspect in performing turning operations using lathe machines. Cutting parameters such as cutting speed, feed rate and depth of cut gives big influence on the dynamic behavior of the machining system. In machining parts, surface quality and tool wear are the most crucial customer requirements. This is because the major indication of surface quality on machined part is the surface roughness and the value of tool wear. Hence, to improve the surface roughness and minimize the forming of tool wear, the optimum feed rate and cutting speed will be determined. The input parameter such as cutting speed, feed rate and depth of cut always influence the tool wear, surface roughness, cutting force, cutting temperature, tool life and dimensional accuracy. The D2 steel was being investigated from the perspective of the effect of cutting speed and feed rate on its surface roughness and tool wear. The results show that cutting speed is the main parameter which affects the surface roughness where the most optimum parameter would be at cutting speed of 173, 231 and 288 m/min with feed rate of 0.15 mm/rev. The tool wear strongly affected by feed rate where at 0.15 mm/rev the tool wear value is the lowest. The combination of high cutting speed and low feed rate was the best parameter to achieve smooth surface roughness.

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