Optimization of CNC End Milling Process Parameters for Aluminium 6061 Alloy using Carbide Tool Mater by IJIRST

IJIRST –International Journal for Innovative Research in Science & Technology| Volume 3 | Issue 11 | April 2017 ISSN (online): 2349-6010

Optimization of CNC End Milling Process Parameters for Aluminium 6061 Alloy using Carbide Tool Material by Design of Experiments Hemantsinh Pratapsinh Rao Student Department of Mechanical Engineering S V B I T, Gandhinagar-382650

Prof. Rajat Dave Assistant Professor Department of Mechanical Engineering S V B I T, Gandhinagar-382650

Prof. Riddhish Thakore Assistant Professor Department of Mechanical Engineering S V B I T, Gandhinagar-382650

Abstract Quality and productivity play important role in today’s manufacturing market. Now a day’s due to very stiff and cut throat competitive market condition in manufacturing industries. The main objective of industries reveal with producing better quality product at minimum cost and increase productivity. CNC end milling is most vital and common operation use for produce machine part with desire surface quality and higher productivity with less time and cost constrain. To obtain main objective of company regards quality and productivity. In the present research project an attempt is made to understand the effect of machining parameters such as cutting speed (m/min), feed rate (mm/min),depth of cut (mm) that are influences on responsive output parameters such as Surface Roughness(Ra) and Material Removal Rate(MRR) by using optimization philosophy. The effort to investigate optimal machining parameters and their contribution on producing better Surface quality and higher Productivity. Thus by Analysing experimental and theoretically data optimization of process parameters are to be carried out. Keywords: CNC end milling, Optimization, Surface roughness (Ra), Material removal rate(MRR), Carbide tool material, Alluminium alloy _______________________________________________________________________________________________________ I.

INTRODUCTION

Milling is the process of machining flat, curved, or irregular surfaces by feeding the work piece against a rotating cutter containing a number of cutting edges. The milling machine consists basically of a motor driven spindle, which mounts and revolves the milling cutter, and a reciprocating adjustable worktable, which mounts and feeds the work piece. Among several CNC industrial machining processes, milling is a fundamental machining operation. End milling and face Milling is the most common metal removal operation encountered. It is broadly used in a variety of manufacturing industries including the aerospace, automotive sectors, where quality is vital factor in the production of slots, pockets, precision moulds and dies.

Fig. 1.1: Milling Operation

Optimization of CNC End Milling Process Parameters for Aluminium 6061 Alloy using Carbide Tool Material by Design of Experiments (IJIRST/ Volume 3 / Issue 11/ 016)

Fig. 1.2: End Mill

The end mill has edges in the side surface and the bottom surface. The fundamental usage is that the end mill is rotated, and makes a plane of a material in the right-and-left direction or a plane of a bottom side of the end mill. We can make various shapes of mechanical parts with the end mill. The edge of the end mill is very weak. In case beginning of cuttings, we have to take care so that the end mill may touch to a material as slowly as possible A large variety of cutter materials has been developed to meet the required conditions in milling operation the common materials used for making of milling cutters are carbon steel, high speed steel, sintered carbides, ceramics and satellites. The mechanism behind the formation of surface roughness is very dynamic, complicated, and process dependent. Several factors will influence the final surface roughness in a CNC end milling operation such as controllable factors (spindle speed, feed rate and depth of cut) and uncontrollable factors (tool geometry and material properties of both tool and work piece). Milling is one of the most common process in manufacturing and is very commonly employed in numerical control machines for material removal operations. II. END MILLING OPERATION End milling is the most common metal removal operation encountered. It is widely used to mate with other part in die, aerospace, automotive, and machinery design as well as in manufacturing industries. The cutter called end mill has diameter less than the work piece width. III. LITERATURE REVIEW Many investigators have suggested various methods to explain the effect of process parameter on surface roughness and MRR in CNC end milling process. B. Ramesh, et al [1] were carried out “Determination of optimum parameter levels for multi performance characteristics in conventional milling of beryllium copper alloy by using response surface methodology”. They described that the utilization of Replication Surface Methodology (RSM) to investigate the relative influence of milling process parameters (spindle celerity, victual and depth of cut) on quality characteristics (surface roughness and material abstraction rate), adequacy analysis of replication surface models and to procure optimal process parameter levels in the culled range which leads to procure high machining quality and productivity in conventional milling of beryllium copper alloy plate utilizing 6 mm carbide end mill. Predicated on the experimental results and methodology utilized, the conclusions can be drawn for straight grooving operation are that Surface roughness and material abstraction rate increases as speed and victual increases. K.D Theja, et al [2] were carried out “Prediction & Optimization of End Milling Process Parameters Using Artificial Neural Networks”. They described that on a potent and precise 3-axis CNC vertical machining centre, mode employing a perpetually variable spindle expedite to a maximum of 6000 rpm and with a maximum spindle power of 5.5kW. The victual rates can be set up to a maximum of 10m/min. Experiments were conducted as per the deign matrix. Cutting haste, Victual and Depth of cut were taken as the process parameters and the output replications i.e. Material abstraction rate and Implement wear resistance were taken as the output replications. Full factorial design was acclimated to carry out the experimental design. Artificial Neural networks (ANN) program available in Matlab software is utilized to establish the relationships between the input process parameters and the output variables. The developed ANN model can be further integrated with optimization algorithms like GA to optimize the End milling parameters. Dimple Rani, et al [3] were carried out “Optimization and Modelling of End Milling Process Parameters by Using Taguchi Method”. They described that an integrated optimization approach utilizing Taguchi method. The average value of surface roughness and S/N ratio were calculated and were found to be within the range. Taguchi parameter design can provide a systematic procedure that can efficaciously and efficiently identify the optimum surface roughness in the process control of individual end milling machines. As speed increases surface roughness decreases and victual increases surface roughness

Optimization of CNC End Milling Process Parameters for Aluminium 6061 Alloy using Carbide Tool Material by Design of Experiments (IJIRST/ Volume 3 / Issue 11/ 016)

additionally increases. For achieving good surface finish on the D2 work piece, higher cutting haste, lower aliment and lower depth of cut are preferred. P. M. Thakur, R. Rajesh [4] were carried out “Optimal selection of process parameters in CNC end milling of Al-7075-T6 aluminium alloy using a Taguchi-Fuzzy approach”. They described that integrated Taguchi-Fuzzy for optimization of process parameters of CNC end milling of Al-7075 T6 alloy with multiple performance characteristics. A fuzzy reasoning of multiple performances has been performed by fuzzy logic unit and a multi replication performance index (MRPI) was developed for each run. Predicated on experimental results and corroboration tests the conclusion can be drawn that the following parameter setting has been identified as to yield the best cumulation of parameters: A3B1C3D2. The experimental results showed that there was consequential amelioration in surface roughness and MRR. The most paramount parameters affecting replications have been identified as nasal perceiver radius and depth of cut. Amit Joshi, Pradeep Kothiyal [5] were carried out “Investigating Effect of Machining Parameters of CNC Milling on Surface Finish by Taguchi Method”. They described from the graph of S-N ratio it can be observed that optimal value of surface finish is obtained at first level of factor A third level of factor B and second level of factor C. Optimal value of surface finish is 3.0723μm. From the ANOVA it can be visually perceived that percentage contribution of aliment rate is maximum and it signifies Victual rate is the most dominating factor for modelling surface finish. Taguchi robust design is opportune for modelling surface finish in CNC milling. Abhishek Kumbhar, et al [6] were carried out “Multi-objective Optimization of Machining Parameters in CNC End Milling of Stainless Steel 304”. They described that the effects of cutting speed, feed rate and depth of cut on surface roughness and material removal rate during end milling of Stainless steel 304 were investigated utilizing Taguchi's experimental design method coalesced with Grey relational analysis. The conclusions can be made from performed experimental research that based on Grey Relational Grade analysis, the optimal process parameters for multi-objective optimization are as follows: Cutting speed at level 2 (75 m/min), feed rate at level 1 (0.15 mm/rev) and depth of cut at level 3 (1.5 mm) i.e. v2-f1-d3. Confirmatory test result was copacetic and has yielded reduction in surface roughness by 24.86 % and increment in material removal rate by 23.99 %. Thus we can observe amendment in performance characteristic. It has been established that Taguchi predicated Grey Relational Analysis is an efficacious multi-objective optimization implement. On the basis of the above Literature Review paper, we finds the several research gap in the following segment of End Milling. So selected of workpiece material as an Aluminium 6061 Alloy and Carbide End Mill with different angle Cutting insert. IV. EXPERIMENTAL SETUP AND DATA COLLECTION Workpiece Material

Fig. 3.1: Aluminium 6061 alloy Table - 3.1 Physical Properties of Al 6061 Density 2.7 g/cm³ Melting Point Approx. 580°C Modulus of Elasticity 70-80 GPa Poisson's Ratio 0.33

Optimization of CNC End Milling Process Parameters for Aluminium 6061 Alloy using Carbide Tool Material by Design of Experiments (IJIRST/ Volume 3 / Issue 11/ 016)

Table - 3.2 Chemical Composition of Al 6061 Component Amount (wt %) Aluminium 97.610 Magnesium 0.890 Silicon 0.570 Iron 0.352 Copper 0.219 Zinc 0.093 Titanium 0.019 Manganese 0.037 Chromium 0.114 Other 0.05

Advantages of Al 6061 Alloy  Light in weight and resistance to oxidation  Corrosion less  Low density material  Good Surface finish  Medium to High Strength  Widely Availability  Good corrosion resistance to sea water  Can be anodized  Good weldability and brazability Application of Al 6061 Alloy  The demand for high strength and low weight material in aerospace is found to be increasing in fabrication of structures and equipment's of Aircraft.  Aluminium alloys possess the characteristics of light weight and high strength.  Marine fittings  Transport, Bicycle frames  Camera lenses  Drive shafts  Electrical fittings and connectors  Brake components  Valves, and Couplings Selected Process & Response Parameters are Process Parameters Cutting Speed (Vc) m/min Feed Rate (Fd) mm/rev Depth of Cut (Ap) mm

Table - 3.3 Response Parameters Surface roughness (μm) Material Removal Rate (mm³/min)

Cutting Inserts and Cutters Specifications Table - 3.3 for 10° Carbide Insert Table - 3.4 Cutter 210º Code:Insert 210º Code:(R210-020A16L-09M050) (R210-090408-NL1025) Specifications Specifications R=Style(Right Hand Rotating) R=Style (Right/ Left Hand) 210=Main Code (Coro mill) 210=Main Code (Coro mill) 020=Cutter Diameter (mm) 09=Insert Size (mm) A=Types of coupling, Cylindrical (mm) 04=Insert Thickness (mm) 16=Coupling Size (mm) 08=Corner Radius (mm) L=Extra Long N=Main ISO Application Area For Aluminium 12=Insert Size (Ia) L=Operation (Light Cutting) M=Close Pitch 1025=Wiper (W) 050=Length (mm)

Optimization of CNC End Milling Process Parameters for Aluminium 6061 Alloy using Carbide Tool Material by Design of Experiments (IJIRST/ Volume 3 / Issue 11/ 016)

V. RESULT AND DISCUSSION Design of Experiment     

In industry, designed experiments can be used to systematically investigate the process or product variables that influence product quality. After you identify the process conditions and product components that influence product quality, you can direct improvement efforts to enhance a product's manufacturability, reliability, quality, and field performance. Designed experiments are often carried out in four phases: planning, screening (also called process characterization), optimization, and verification. DOE is considered as one of the most comprehensive approach in product or process developments. It is a statistical approach that attempts to provide a predictive knowledge of a complex, multi-variable process with few trials. Taguchi Method Steps:1) Identified the main function and its side effects 2) Identified the noise factors, testing condition and quality characteristics 3) Identified the objective function to be optimized 4) Identified the control factors and their levels 5) Select a suitable Orthogonal Array and Construct the matrix 6) Conduct the matrix experiment 7) Examine the data, predict the optimum control factor levels and its performance 8) Conduct the verification experiment 9) Selected levels of DOE

Serial No 1 2 3 4 5 6 7 8 9

Table – 4.1 Factor level of DOE Factors Level Factors Value Cutting Speed(rpm) 3 120, 235, 380 Feed Rate(mm/rev) 3 0.08, 0.14, 0.20 Depth of Cut(mm) 3 1, 3 , 6 Table – 4.2 DOE Serial No Cutting Speed (rpm) Feed Rate (mm/rev) Depth of Cut (mm) 1 120 0.08 1 2 120 0.14 3 3 120 0.20 6 4 235 0.08 3 5 235 0.14 6 6 235 0.20 1 7 380 0.08 6 8 380 0.14 1 9 380 0.20 3 Table – 4.3 Measured Surface Roughness and MRR Cutting Speed (rpm) Feed Rate (mm/rev) Depth of Cut (mm) SR (μm) MRR (mm³/min) 120 0.08 1 1.35 36.68 120 0.14 3 2.40 192.60 120 0.20 6 3.10 550.31 235 0.08 3 2.45 215.53 235 0.14 6 3.15 754.39 235 0.20 1 1.40 179.61 380 0.08 6 3.20 697.06 380 0.14 1 1.45 203.31 380 0.20 3 2.70 871.33

Taguchi Orthogonal Array Design In this experimental work, we have taken L9 orthogonal array design i.e. L9 (3^3) means that Factors are 3 & Runs are 9, and Columns of L9 (3^4) Array. Analysis: SR, MRR versus Cutting Speed, Feed Rate, Depth of Cut Response Table for Means Table – 4.4 Nominal is best (10×Log10 (Ybar^2/s^2)) Level Cutting Speed Feed Rate Depth of Cut

Optimization of CNC End Milling Process Parameters for Aluminium 6061 Alloy using Carbide Tool Material by Design of Experiments (IJIRST/ Volume 3 / Issue 11/ 016)

1 2 3 Delta Rank

131.07 192.76 296.51 165.44 2

159.38 192.88 268.07 108.70 3

70.63 214.50 335.20 264.57 1

Fig. 4.1: Means Plot for 10° Insert

Response Table for Signal to Noise Ratios Table - 4.5 Nominal is best (10×Log10 (Ybar^2/s^2)) Level Cutting Speed Feed Rate Depth of Cut 1 -2.692 -2.705 -2.711 2 -2.875 -2.873 -2.854 3 -2.924 -2.915 -2.927 Delta 0.232 0.210 0.216 Rank 1 3 2

Fig. 4.2: S/N Ratio for 10° Insert

Optimization of CNC End Milling Process Parameters for Aluminium 6061 Alloy using Carbide Tool Material by Design of Experiments (IJIRST/ Volume 3 / Issue 11/ 016)

   

The study proposes an integrated optimization approach using Taguchi method. In this research work, the average value of surface roughness and S/N ratio were calculated and were found to be within the range. Taguchi parameter design can provide a systematic procedure that can effectively and efficiently identify the optimum surface roughness in the process control of individual end milling machines. It also allows industry to reduce process or product variability and minimize product defects by using a relatively small number of experimental runs and costs to achieve superior-quality products. This research only demonstrates how to use Taguchi parameter design for optimizing machining performance with minimum cost. This approach can be recommended for continuous quality improvement and off-line quality of any production process. As speed increases surface roughness decreases and feed increases surface roughness also increases. For achieving good surface finish on the Al 6061 alloy work piece, higher cutting speed, lower feed and lower depth of cut are preferred. And Cutting Insert 210° consider as a high cutting speed. VI. CONCLUSION

In this study, the effects of cutting speed, feed and depth of cut on surface roughness and material removal rate during end milling of Aluminium 6061 alloy were investigated using Taguchi's experimental design method by Design of Experiment. From the experimental analysis, we can see that the Surface Roughness and Material Removal Rate (MRR) are mainly affected by the process Parameters of Cutting Speed, Feed Rate and Depth of Cut. Response table of S/N Ratio concludes that Cutting speed has great impact on SR and MRR, and Means Plot and Standard Deviations Plot mainly affect the Depth of Cut. REFERENCES [1]

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