Parametric Optimization of Surface Roughness & Material Remove Rate of AISI D2 Steel For Turning

Transcription

1 RESEARCH ARTICLE OPEN ACCESS Parametric Optimization Surfa Roughness & Material Remove Rate AISI D Steel For Turning Hitesh Patel*, Jigar Patel**, Chandresh Patel*** * PG Fellow, ** Assistant Pressor *** Assistant Pressor Department Mechanical Engineering, S.P.B.Patel Engineering College, Mehsana, Gujarat. ABSTRACT In order to produ any product with desired quality by machining, proper selection pross parameters is essential. This can be accomplished by Full ial method. The aim the present work is to investigate the effect pross parameter on surfa finish and material removal rate (MRR) to obtain the optimal setting these pross parameters and the analysis varian is also used to analysis the influen cutting parameters during machining. In this work AISI D steel work pies are turned on conventional all gear lathe by using carbide tool. In the present work, Design Experiment (DOE) with full factorial design has been explored to produ 7 specimens on AISI D Steel by straight turning operation MRR will be calculated from MRR equation. Collected data related to surfa roughness have been utilized for optimization. ANOVA analysis gives the perntage contribution each pross parameter. Cutting speed is the most effective parameter which affects surfa roughness in straight turning operation. Feed rate and depth cut are most effective on material removal rate for turning operations. Grey relational analysis is used for finding out the optimal condition for surfa roughness and material removal rate for straight turning operation. Grey relational analysis give the better surfa finish at low speed, low feed rate and high depth cut. Keywords - Full ial Method, Machining Parameters, Surfa Roughness, Material Removal Rate (MRR), ANOVA Analysis I. INTRODUCTION Turning is a machining pross in which a cutting tool, typically a non-rotary toolbit, describes a helical tool path by moving more or less linearly while the workpie rotates. Many articles addressed experimental and theoretical optimization prosses discussed during dissertation work. Some them given here. M. Kaladhar et al.[1] (011) investigate the effects pross parameters on surfa finish and material removal rate (MRR) to obtain the optimal setting these pross parameters on AISI 304 steel. The Analysis Of Varian (ANOVA) is also used to analyze the influen cutting parameters during machining. The results revealed that the feed and nose radius is the most significant pross parameters on work pie surfa roughness. However, the depth cut and feed are the significant factors on MRR. Muammer Nalbant et al. [] investigate the effects machining AISI 1030 steel (i.e. orthogonal cutting) uncoated, PVD- and CVD-coated mented carbide insert with different feed rates, cutting speeds by keeping depth cuts constant without using cooling liquids has been accomplished. Anil Gupta et al. [3] presents the application Taguchi method with logical fuzzy reasoning for multiple output optimization high speed CNC turning AISI P-0 tool steel using TiN coated tungsten carbide coatings. B FNIDE et al. [4] investigate the application response surfa methodology for determining statistical models cutting fors in hard turning AISI H11 hot work tool steel. The feed rate influens tangential cutting for more than radial and axial fors. The cutting speed affects radial for more than tangential and axial fors. From above discussion we found that how these parameters affect in turning prosses.in our project we use AISI D tool steel which also known as HCHC tool steel. The chemical composition AISI D tool steel is given in below Table 1. Table 1. Chemical Composition AISI D Steel Carbon 1.55% Silicon 0.30% Manganese 0.35% Chromium 1.00% 1.00% Molybdenum 0.75% Vanadium 0.90% AISI D is recommended for tools requiring very high wear resistan, combined with moderate 9 P a g e

2 toughness (shock-resistan).aisi Dcan be supplied in various finishes, including the hot-rolled, premachined and fine machined condition II. DESIGN OF EXPERIMENT Design experiments was developed in the early 190s by Sir Ronald Fisher at the Rothamsted Agriculture field Research Station in London, England. His initial experiments were conrned with determining the effect various fertilizers on different plots land. The final condition the crop was not only dependent on the fertilizer but also on the number other factors (such as underlying soil condition, moisture content the soil, etc.) each the respective plots. Fishers used DOE which could differentiate the effect fertilizer and the effect other factors. Sin that time the DOE has been widely acpted in agricultural as well as Engineering Scien. Design experiments has become an important methodology that maximizes the knowledge gained from experimental data by using a smart positioning points in the spa. This methodology provides a strong tool to design and analyze experiments; it eliminates redundant observations and redus the time and resours to make experiments. We have used factorial design, and used full factorial design. For a full factorial design, if the numbers levels are same then the possible design N is N = L m Where, L = number levels for each factor, and m= number factors. Table s and their levels in CNC straight turning Cutting Speed (m/min) Feed Rate (mm/rev) Level 1 Level Level Level D.O.C (mm) From the above table according to design experiments with full factorial design total numbers experiments to be performed are 7. spindle speed 93 rpm, spindle speed range 5 to 93 rpm and maximal turning length 350mm. Table 3 Data obtained from experimental work for surfa roughness Sr Feed Rate Depth Cut MRR Cutti ng Spee d Surfa roug hness m/mi mm/r mm μm. mm 3 /min n IV. ANOVA ANALYSIS The perntage contribution Cutting Speed is 79.0%, Feed Rate is.90%, and D.O.C is 1.54%. Parametric analysis is carried out for the quality the sample. i.e. Surfa Roughness. This parametric analysis (ANOVA) shows the perntage contribution parameters individually as shown in Table 4 III. EXPERIMENTAL SET UP In order to achieve the goal this experimental work the cutting tests were carried out in a conventional heavy duty lathe. The lathe turning ntre has. kw spindle motor power and a maximal machining diameter 15mm, maximal 70 P a g e

3 Mean Mean Jigar Patel et al Int. Journal Engineering Research and Applications Table 4: PERCENTAGE CONTRIBUTION OF PROCESS PARAMETER FOR SURFACE ROUGHNESS Sour Variati on A B C Error (O) D O F Sum Squa res S Varian (Mean Square ) Total Main Effect Plots for ANOVA The analysis is made with the help a stware package MINITAB 1. The main effect plots are shown in Fig. 1 and Fig. These show the variation individual response with the three parameters i.e. cutting speed, feed, and depth cut separately. In the plots, the x-axis indicates the value each pross parameter at three level and y-axis the response value. Horizontal line indicates the mean value the response. The main effects plots are used to determine the optimal design conditions to obtain the optimum surfa finish. Main Effects Plot for roughness Data Means Varian Ratio F Pern tage Contri bution P 79.0%.90% 1.54% % % cutting speed depth cut 1.5 Main Effects Plot for MRR Data Means feed ratee Fig. Main Effect Plot For MRR V. GRAY RELATIONAL ANALYSIS In grey relational generation, the normalized data corresponding to Lower-the-Better (LB) criterion can be expressed as: For Higher-the-Better (HB) criterion, the normalized data can be expressed as: Where xi (k) is the value after the grey relational generation, min is the smallest value for the kth response, and max is the largest value for the kth response. An ideal sequen is x0 (k) for the responses. However, if there is a specific target value, then the original sequen is normalized using, cutting speed feed ratee depth cut Table 5. Grey relational analysis Sourc e Cutti ng speed Feed rate D OF Sum Square SS Adj SS Adj MS V.R P Fig. 1 Main Effect Plot For Surfa Roughness Dept h cut Error Total 5 S = R-Sq =.1% R-Sq(adj) =.07% 71 P a g e

4 Sr Table. ANOVA for grey relational grade GRC SR VI. CONCLUSION In this dissertation work, various cutting parameters like, cutting speed, feed and depth cut have been evaluated to investigate their influen on GRC MRR GRG Grade surfa roughness and material removal rate. Based on the result obtained, it can be concluded as follows: SURFACE ROUGHNESS: Cutting speed, feed rate and depth cut significantly effects on surfa roughness. Cutting speed is found the most significant effect on surfa roughness. Increase in Cutting speed value surfa roughness is increase. Feed rate is found to have effect on surfa roughness. Increase in Feed rate, value surfa roughness is decrease. Depth cut is found to have effect on surfa roughness. Increase in depth cut value surfa roughness is increase. The optimal combination low feed rate and low depth cut with high cutting speed is beneficial for reducing machining for so surfa roughness is decrease when high cutting speed, low feed rate and low depth cut. The perntage contribution cutting speed is 79.0 %, feed.90 % and depth cut 1.54 % on surfa roughness for straight turning operation. MATERIAL REMOVAL RATE: The volume material removed can be achieved better when machining was done at high depth cut and high feed rate. Feed and Depth cut are found the most significant effect on material removal rate. Increase in feed and depth cut, value material removal rate is increase. The perntage contribution cutting speed is 5.9 %, feed % and depth cut 0.59 % on material removal rate for straight turning operation. REFERENCES [1] M. Kaladhar, K. Venkata Subbaiah, Ch. Srinivasa Rao. Determination Optimum Pross Parameters during turning AISI 304 Austenitic Stainless Steels using Taguchi method and ANOVA International Journal Lean Thinking Volume 3, Issue 1 (June 01) pp [] Muammer Nalbant, Hasan Gokkaya, Ihsan Toktas, Gokhan Sur. The experimental investigation the effects uncoated, PVDand CVD-coated mented carbide inserts and cutting parameters on surfa roughness in CNC turning and its prediction using artificial neural networks Robotics and Computer- Integrated Manufacturing 5 (009) pp [3] Anil Gupta, Hari Singh, Aman Aggarwal. Taguchi-fuzzy multi output optimization (MOO) in high speed CNC turning AISI P- 7 P a g e

5 0 tool steel Expert Systems with Applications 3 (011) pp.. [4] B Fnides, M A Yallese, T Mabrouki, J F Rigal. Application response surfa methodology for determining cutting for model in turning hardened AISI H11 hot work tool steel Sadhana Vol. 3, Part 1 (011) pp [5] Sukumar, Poornima. Optimization machining parameters in CNC turning martensitic stainless steel using RSM and GA International Journal Modern Engineering Research (IJMER) Vol., Issue., (Apr. 01) pp [] Yansong Guo, Jef Loenders, Joost Duflou, Bert Lauwers. Optimization energy consumption and surfa quality in finish turning Prodia CIRP 1 ( 01 ) pp [7] Gaurav Bartarya, S.K.Choudhury. Effect cutting parameters on cutting for and surfa roughness during finish hard turning AISI5100 grade steel Prodia CIRP 1 (01) pp [] Suleiman Abdulkareem, Usman Jibrin Rumah, Apasi Adaokoma. Optimizing Machining Parameters during Turning Pross International Journal Integrated Engineering, Vol. 3 1 (011) pp.3-7. [9] R A Mahdavinejad, S Saeedy. Investigation the influential parameters machining AISI 304 stainless steel Sadhana Vol. 3, Part (011) pp [10] Pragnesh. R. Patel, Pr. V. A. Patel. Effect machining parameters on Surfa roughness and Power consumption for 03 Al alloy TiC Composites (MMCs) International Journal Engineering Research and Applications Vol. (01) pp [11] Kamal Hassan, Anish Kumar, M.P.Garg. Experimental investigation Material removal rate in CNC turning using Taguchi method International Journal Engineering Research and Applications Vol. (01) pp P a g e