Volume-6, Issue-6, November-December 2016 International Journal of Engineering and Management Research Page Number: 216-220 Optimization of Different Cutting Parameter for Aluminum-6351 S.K.Aher 1, R.S.Shelke 2 1 P.G.Student, S.V.I.T., College of Engineering Chincholi, Nashik, INDIA 2 Professor, S.V.I.T., College of Engineering Chincholi, Nashik, INDIA ABSTRACT This paper shows the application of Taguchi based for multi objective optimization of process parameters in CNC end milling process and different cutting tool materials considering various process parameters. Keywords-- CNC, MRR, DOE I. INTRODUCTION CNC Vertical End Milling Machining is a widely accepted material removal process used to manufacture components with complicated shapes and profiles. During the End milling process, the material is removed by the end mill cutter. The quality of the surface plays a very important role in the performance of milling as a good-quality milled surface significantly improves fatigue strength, corrosion resistance, or creep life. The surface generated during milling is affected by different factors such as vibration, spindle run out, temperature, tool geometry, feed, cross-feed, tool path and other parameters. During finish milling, the depth of cut is small. The most important interactions, that effect surface roughness of machined surfaces, are between the cutting feed and depth of cut, and between cutting feed and spindle speed. Surface Roughness is affected negatively if the applied force is increased. Surface roughness at the same feed rate becomes higher when a small nose radius is used. Technological parameter range plays a very important role on surface roughness. There is a need of a tool that should allow the evaluation of the surface roughness before the machining of the part and which, at the same time, can easily be used in the production-floor environment contributing to the minimization of required time and cost and the production of desired surface quality. In order to obtain better surface roughness, the proper setting of cutting parameters is crucial before the process takes place. Several factors will influence the final surface roughness in a CNC milling operation. The final surface roughness might be considered as the sum of two independent effects: 1. The ideal surface roughness is a result of the geometry of tool and feed rate. 2. The natural surface roughness is a result of the irregularities in the cutting operation Factors such as spindle speed, feed rate, tool diameter and depth of cut that control the chip formations, or the material properties of both tool and work piece, vibrations of the machine tool, defects in the structure of the work material. Material removal rate (MRR) is an important control factor of machining operation and the control of machining rate. It is also critical for production planners. MRR is a measurement of productivity & it can be expressed by analytical derivation as the product of the width of cut, the feed velocity of milling cutter and depth of cut. Cutting feed is the most dominated factor for surface finish. In material removal processes like milling, etc. an improper selection of cutting conditions causes surfaces with high roughness and dimensional errors. Traditionally, the experience of the operator plays a major role in the selection of optimum metal cutting conditions. However, attaining optimum values each time by even a skilled operator is difficult. The non-linear nature of the machining process has compelled engineers to search for more effective methods to attain optimization. It is therefore imperative to investigate the machinability behavior of different materials by changing the machining parameters to obtain optimal results. Process modeling and optimization are the two important issues in manufacturing products. The selection of optimal cutting parameters, like depth of cut, feed and speed, is a very important issue for every machining process. In workshop practice, cutting parameters are selected from machining databases or specialized handbooks, but the range given in these sources are actually starting values, and are not the optimal values. This experiment gives the effect of different machining parameters (spindle speed, feed, depth of cut, tool material) on material removal rate and Surface removal rate in end milling. The demand for high quality and fully 216 Copyright 2016. Vandana Publications. All Rights Reserved.
automated production focus attention on the surface condition of the product, surface finish of the machined surface is most important due to its effect on product appearance, function, and reliability. For these reasons it is important to maintain consistent tolerances and surface finish. Among several CNC industrial machining processes, milling is a fundamental machining operation. End milling is the most common metal removal operation encountered. It is widely used in a variety of manufacturing in industries. This experimental investigation outlines the Taguchi optimization methodology, which is applied to optimize MRR and surface roughness in end milling operation II. PROBLEM STATEMENT The project work focus on optimization of aluminum considering the various process parameters. The aim is To optimize the parameters to avoid trial and error for the different working conditions. III. OBJECTIVES To find the optimum values for the input parameters like speed (N), feed (f),depth of cut(d),tool material and its effect on the surface finish for achieving the minimum surface roughness. Objective function for first objective is to Minimize Surface roughness (R a ) subjected to minimum and maximum range of input parameters like speed (N), feed (f), depth of cut (d), and tool material. To select the order of input parameters to get the maximum MRR. The Objective function is to; Maximize Material removal rate (MRR) subjected to minimum and maximum range of input parameters like speed (N), feed (f), and depth of cut (d) & tool material. DOE is applicable to both physical processes and computer simulation models. Experimental design is an effective tool for maximizing the amount of information gained from a study while minimizing the amount of data to collected. Factorial experimental designs investigate the effects of many different factors, by varying them simultaneously instead of changing only one factor at a time. Factorial designs allow estimation of the sensitivity to each factor also to the combined effect of two or more factors. Experimental design methods successfully applied to several ballistic missile defense sensitivity studies to maximize the amount of information with a minimum number of computer simulation runs. 4.1Design of Experiment (DOE) There are three aspects of the process that are analyzed by a designed experiment. Factors These are inputs to the process. Factors can be classified as either Controllable or Uncontrollable variables. Levels - These are settings of each factor. Response Is the output of the experiment. Experimenters often desire to avoid optimizing the process for one response at the expense of another. For this reason, important outcomes are measured and analyzed to determine the factors and their settings that will provide the best overall outcome for the critical-to-quality characteristics-both measurable variables and assessable attributes.[5] DOE is done in following way, Number of Experimental factors: 4 Number of blocks: 1 Number of responses: 2 Number of run s: 9, including 9 slots over the entire length of work piece Error degrees of freedom: 8 IV. DOE METHODOLOGY The methods which is planned for the investigation is DOE coupled with the optimization techniques, the details of DOE and optimization techniques is describe in details as: Design of experiment (DOE) is a powerful technique used for exploring new processes, gaining increased knowledge of the existing processes and optimizing these processes for achieving world class performance. Statistical thinking and statistical methods play an important role in planning, conducting, analyzing, and interpreting data from engineering experiments. When several variables influence a certain characteristic of a product, the best strategy is then to design an experiment so that valid, reliable and sound conclusions can be drawn effectively, efficiently and economically. DOE is a series of tests in which purposeful changes are made to the input variables of a system or process and the effects on response variables are measured. Table I. Selection of machining parameters Material For the present work the material use is Aluminum 6351 with the dimensions 160mm 65mm 16 mm. The physical properties of the material are as follows. 217 Copyright 2016. Vandana Publications. All Rights Reserved.
Table II. Physical properties of materials V. EXPERIMENTAL ANALYSIS 5.1 Optimum level for the individual Objective Table III 5.2Responses for mean Table IV Responses from mean Material>DOC>Feed>Speed. From the fig 4.14 it can clearly seen the main effect of speed, feed, and depth on cut & tool diameter on the material removal rate and following conclusions drawn gradual increase in Ra is observed and as the speed further increases from 2000 to 2500 again gradual decrease in Ra observed. mm/min increases in the Ra is observed i.e.to again increase the federate so Ra value should be decreases. 3. As the doc increased from 1 to 1.5 increases in the Ra value is observed i.e.to increase the doc so the Ra should be increases 4. As the cutting tool material HSS to COBALT so Ra value decreases i.e. again change the CARBIDE TOOL so Ra value should decreases. From the graphs and plots following conclusions can be drawni) The optimum levels for the optimization are (3 1 1 3) and the optimum parameter are- 2500 400 1 carbide. ii) The significant parameter are Tool Material>DOC>Feed>Speed. From the fig 4.14 it can clearly seen the main effect of speed, feed, and depth on cut & tool diameter on the material removal rate and following conclusions drawn gradual increase in Rz is observed and as the speed further increases from 2000 to 2500 again gradual decrease in Rz observed. mm/min increases the Rz value is observed i.e after increase feed 600mm/min to 800mm/min so the Rz value should be decreases. 3. As the doc increased from 1 to 1.5 increases the Rz value is observed i.e.to after increase the doc 1.5 to 2 so the Rz value should be increases. 4. As the changes cutting tool material for HSS to HSS cobalt so Rz value decreases so again change carbide as cutting tool again roughness value decreases 5.3 MRR Analysis Sample Graph I: Signal to Noise From the Graph and the S/N ratio table following conclusions can be drawn. i) Optimum Level for the optimization are (3 1 1 3 )With the value matching with table. ii) The Significant parameter are Tool Table V. MMR Analysis 218 Copyright 2016. Vandana Publications. All Rights Reserved.
Response table for mean Table VI. Response of MRR From the graphs and plots following conclusions can be drawni) The optimum levels for the optimization are (2 3 3 3) and the optimum parameter are- 2000 800 2 Carbide. ii) The significant parameter are DOC>Feed>Tool Material>Speed. The main effect of speed, feed, and depth on cut & tool diameter on the material removal rate and following conclusions drawn: gradual decrease in MRR is observed and as the speed further increases from 2000 to 2500 again gradual deccrease in MRR observed. mm/min increases in MRR is observed i.e.to increase the feed rate so MRR should0 be increases. 3. As the doc increased from 1 to 1.5 increases in the MRR is observed i.e.to increase the MRR the federate should be increases 4. As the changes cutting tool material for HSS to HSS cobalt so MRR increases so again change carbide as cutting tool again MRR increases. VI. EXPERIMENTAL VALIDATION In order to get the validation the confirmation trials are conducted on the both work piece, considering the objective of the minimum surface roughness and the maximum MRR, the details of the experimentation and its results are as follows; Table VI: Experimental validation for AL 6351 VII. CONCLUSION Existing experiment and its analysis provides following remarkable point Considering the objective like MRR and roughness nine experiments were successfully conducted and then its analysis is done with the help of Minitab software The present work has successfully demonstrated the application of Taguchi based for multi objective optimization of process parameters in CNC end milling process for three different cutting tool materials subjected to various conditions. For the conformation purpose conformation test is carried out on same material with same experimental set up, results for this closely matches with optimum level given by minitab. Thus this experimentation successfully optimize the end milling process for same material for three different cutting tool materials considering various process parameters, which will help to improve the efficiency by selecting the optimum parameters. REFERENCES [1] Joshi, Amit and Kothiyal, Pradeep, Investigating effect of machining parameters of CNC milling on surface finish by taguchi method International Journal on Theoretical and Applied Research in Mechanical Engineering, Volume-2, Issue-2, pp. 113-119, 2013 [2] Bajic, D., Lele, B. and Zivkovic, D., Modeling of machined surface roughness and optimization of cutting parameters in face milling. Vol.47, pp.331-334, 2008. [3] Chockalingam, P.and Wee Lee Hong, Surface Roughness and Tool Wear Study on Milling of AISI 304 Stainless Steel Using Different Cooling Conditions. International Journal of Engineering and Technology Vol. 2, No. 8, pp.1386-1392, 2012. [4] Zhang, Julie Z., Chen, Joseph C. and Kirby, E. Daniel, Surface roughness optimization in an end-milling operation using the Taguchi design method. Journal of Materials Processing Technology Vol.184, pp. 233 239, 2007. [5] Gologlu, Cevdet and Sakarya, Nazim, The effects of cutter path strategies on surface roughness of pocket milling of 1.2738 steel based on Taguchi method. Journal of materials processing technology Vol.206,pp. 7 15, 2008. [6] Kopac J. and Krajnik P., 2007. Robust design of flank milling parameters based on grey-taguchi method, Journal of Material Processing Technology, Vol. 191, No. 1-3, pp. 400-403. [7] Anish Nair & P Govindan, Optimization of CNC end milling of brass using hybrid taguchi method using PCA and grey relational analysis International Journal of Mechanical and Production Engineering Research and Development (IJMPERD) ISSN 2249-6890 Vol. 3, Issue 1, Mar 2013, 227-240. [8] Shahzad Ahmad, Harish Kumar Sharma, Atishey Mittal, Process Parametric Optimization of CNC Vertical Milling Machine Using ANOVA Method in Mild Steel A Review, International Journal of Engineering Sciences & Research Technology pp 137-146, http: // www.ijesrt.com 219 Copyright 2016. Vandana Publications. All Rights Reserved.
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