NIRAJAN PUDASAINI 2VX12MPD15 A Project on: PROF. R R MALAGI PROJECT GUIDE

Contents Abstract Vertical milling About SG iron Input parameters Response variables Objective Literature review Problem statement About DOE RSM Box- Behnken design Design matrix Sequence of operation Manufacture of workpiece Machining Measurement of SR Observation table Results Conclusion Future works

Abstract  This report presents an approach to predicting the surface roughness and material removal rate in milling of spheroidal graphite iron using tungsten carbide insert tool and its optimization by coupling the prediction model with response surface methodology.  In this work, experiments are carried out as per the Box- Behnken design and an L13 orthogonal array is used to study the influence of various combinations of process parameters on SR and MRR.  ANOVA test is conducted to determine the significance of each process parameter. Two sets of L13 OA are used each for tool orientation of 45 and 90 degrees.  This work may be useful in selecting optimum values of various process parameters that would maximize the MRR and minimize the SR in machining.

Vertical Milling Machining Centers classified as: Vertical Machining centers Horizontal Machining centers Universal Machining centers VMC has spindle on vertical axis relative to work table Used for flat works that require tool access from top For e.g.: Mould and die cavities, large aircraft components Figure: A Vertical Milling Machine

Spheroidal Graphite Iron Also called ductile iron Characterized by graphite occurred in microscopic spheroids Various grades, differed due to matrix (microstructure of metal around the graphite) IndiaIS 1865 SG37 0/17 SG40 0/12 SG5 00/7 SG6 00/3 SG7 00/2 SG8 00/2 — ISOISO

Cutting parameters

Response variables 1. Surface roughness It is a measure of the level of unevenness of the part's surface. Measurement procedure Surface inspection by comparison method Direct instrument method Parameters Ra = arithmetic mean of departures of profile from mean line Rq, Ry, Rz, Sm are other parameters

2. Material removal rate It is the volume of material removed divided by the machining time. MRR can be expressed as the ratio of the difference between the weight of the work piece before and after machining to the machining time. MRR= (Wb-Wa)/t Where Wb = Weight of work piece before machining. Wa = Weight of work piece after machining. t = Machining time

Objective The objective of this study is to find out the optimum levels for the process parameters so that the surface roughness value will be minimum and rate of material removal will be maximum in a vertical machining center and to check the optimality by developing empirical models.

Literature review Pratyusha J et al. made a study for finding out optimum parameters for milling process using Taguchi methods. L9 array was used, parameters studied were speed, feed and depth of cut. They found that Taguchi method provides a systematic and efficient methodology for searching optimal milling parameters. R. Suresh et al. made an attempt to analyse the influence of cutting speed, feed rate, DOC and machining time on machinability characteristics like SR and tool wear using RSM. The found that combination of low feed rate, low depth of cut and low machining time with high cutting speed is beneficial for minimizing the machining force and surface roughness

Balinder singh et al. carried out experiments for optimization of input parameters in the CNC milling on EN 24 steel. Taguchi technique used SR and MRR were response variables, speed, feed and DOC were control parameters L27 array was used generated from MINITAB V15 Confirmation runs was used to verify the experiment Other research on VMC using Taguchi technique were done by Piyush Pandey et al., Avinash A. Thakre, Reddy Sreenivalsu and so on. Milon D. Selvam et al., R. Jalili Saffar et al. used GA method.

Ahmad Hamdan et al. carried out experiments for high speed machining of stainless steel using L9 array and Taguchi method. Results showed a reduction of 25.5% in cutting forces and 41.3% in SR improvement. Norfadzlan Yusup et al. made a comparison of five year researches from 2007 to 2007 that used evolutionary techniques to optimize machining process parameters. They found that SR is mostly studied with GA. A Kacal and M Gulesin studied optimal cutting condition in finish turning of of ductile iron using Taguchi method. ANOVA was used to identify significant factors affecting SR. They found that feed rate is most significant.

Problem statement In machining operation, the quality of surface finish and the rate of material removal are important requirements. The choice of optimized cutting parameters is very important for controlling the required surface quality and obtaining the maximum MRR. In this study, the optimum machining parameters, for vertical milling of SG iron, are to be determined to increase MRR and reduce the SR.

DOE technique Statistical design of experiments refers to the process of planning the experiments so that appropriate data that can be analysed by statistical methods will be collected, resulting in valid and objective conclusions.

Guidelines for designing an experiment 1. Recognition of and statement of the problem 2. Choice of factors, levels and ranges* 3. Selection of response variable* 4. Choice of experimental design 5. Performing the experiment 6. Statistical Analysis of the data 7. Conclusions and recommendations *In practice, step 2 and 3 are often done simultaneously or in reverse order

Response surface methodology It is a collection of mathematical and statistical techniques useful for the modeling and analysis of problems in which a response of interest is influenced by several variables and the objective is to optimize this response. In Statistics, RSM explores relationship between several explanatory variables and one or more response variables. Idea is to use a sequence of designed experiments to obtain an optimal solution. Estimate first-degree polynomial by factorial experiments Explains which explanatory variables have an impact on response variable of interest. By Box-behnken method, 2 nd degree polynomial model is estimated. This second degree polynomial can be used to optimize.

Box-Behnken design A useful method for developing second-order response surface models Based on the construction of balanced incomplete block designs and requires at least three levels for each factor. Requires only three levels to run an experiment. It is a special 3-level design because it does not contain any points at the vertices of the experiment region.

Geometric representation Number of trails and corresponding level

Design matrix Input parameters with levels Factors/LevelsLevel -1 (low)Level 0 (medium)Level 1 (high) Speed (rpm) Feed (mm/rev) Depth of Cut (mm)

Design matrix for 45 degree tool orientation Std Order RunOrd erPtTypeBlocks Feed (mm/rev) Speed (rpm) Depth of cut (mm)

Design matrix for 90 degree tool orientation Std Order RunOrd erPtTypeBlocks Feed (mm/rev) Speed (rpm) Depth of cut (mm)

Sequence of operation 1. Manufacture of workpieces (26 pieces) from casting 2. Measurement of initial weight 3. Machining in the Vertical Machining Center (MCV-1000) 4. Measurement of Surface Roughness 5. Measurement of final weight 6. Calculation of MRR and Surface Roughness for each trials 7. Analysis using MINITAB V16 8. Optimization of the responses

Manufacture of workpieces Using a sand casting method. 26 sets of workpieces were manufactured Made up of SG Iron

Machining Performed at a vertical machining center, Shradha enterprises, Udyambag, Belgaum The machine used is TAKUMI MCV-1000 model Takumi MCV- 1000

Machining procedure Clamping of workpiece Milling cutter Machining with coolant

Machining outputs Run Order (45 degree) Initial weight (kg) Final weight (kg) Time (min) Run Order (90 degree) Initial weight (kg) Final weight (kg) Time (min)

Measurement of Surface roughness Surface roughness measurement is done using the Surtronic 3+ device available at metrology laboratory of Gogte Institute of Technolgy, Belgaum.

Observation table Observation table for 45 degree tool orientation Std. Order Run Order Pt. TypeBlocks Feed (mm/rev) Speed (rpm) DOC (mm) MRR (kg/min) SR (microns)

Observation table for 90 degree tool orientation Std. Orde r Run Order Pt. TypeBlocks Feed (mm/rev) Speed (rpm) DOC (mm) MRR (kg/min) SR (microns)

Results and Discussions To find out which factors among the speed, feed and DOC is significant in increasing MRR and reducing the SR and at what level Response surface analysis using MINITAB v16 ANOVA to check adequacy of model Confidence interval = 85 % Only terms whose p < 0.15 is used to develop empirical model Analysis done using coded units, so, empirical equation generated are expressed in coded units

Analysis of response for 45 degree tool orientation Regression analysis for MRR TermsCoeff.SE Coeff.T testP value Constant Feed * Speed Depth of cut * Feed * Feed Speed *Speed * DOC * DOC Feed * speed Feed * depth of cut Speed * DOC *

Analysis of Variance for MRR SourceDFSeq SSAdj SSAdj MSFP Feed * Speed Depth of cut * Feed*Feed Speed*Speed * DOC*DOC Feed*Speed Feed*DOC Speed*DOC * Residual error Total

Empirical model for MRR MRR = * feed * DOC * speed* speed * speed * DOC

Main effect plot for MRR at 45 degree insert orientation

Interaction effect

Optimum machining parameters Feed (mm/rev) Speed (rpm) Depth of cut (mm) 500 (level 1) 1000 (level 1) 1.5 (Level 1)

Regression analysis for surface roughness for insert at 45 degree TermsCoeff.SE Coeff.T testP value Constant Feed * Speed * Depth of cut Feed * Feed Speed *Speed DOC * DOC Feed * speed * Feed * depth of cut Speed * DOC

Analysis of Variance for SR SourceDFSeq SSAdj SSAdj MSFP Feed Speed Depth of cut Feed*Feed Speed*Speed DOC*DOC Feed*Speed Feed*DOC Speed*DOC Residual error Total

Empirical model for SR SR = 5.7 – * feed – * speed *feed* speed

Main effect plot for SR at 45 degree insert orientation

Interaction effect

Optimum machining parameters Feed (mm/rev) Speed (rpm) Depth of cut (mm) 500 (level 1) 1000 (level 1) 1.5 (Level 1)

Analysis of response for 90 degree tool orientation Regression analysis for MRR TermsCoeff.SE Coeff.T testP value Constant * Feed * Speed Depth of cut * Feed * Feed Speed *Speed DOC * DOC Feed * speed Feed * depth of cut Speed * DOC *

Analysis of Variance for MRR SourceDFSeq SSAdj SSAdj MSFP Feed Speed Depth of cut Feed*Feed Speed*Speed DOC*DOC Feed*Speed Feed*DOC Speed*DOC Residual error Total

Empirical model for MRR MRR = * feed * DOC *speed * DOC

Main effect plot for MRR at 90 degree insert orientation

Interaction effect

Optimum machining parameters Feed (mm/rev) Speed (rpm) Depth of cut (mm) 500 (level 1) 1000 (level 1) 1.5 (Level 1)

Regression analysis for surface roughness TermsCoeff.SE Coeff.T testP value Constant Feed * Speed * Depth of cut Feed * Feed Speed *Speed DOC * DOC Feed * speed * Feed * depth of cut Speed * DOC *

Analysis of Variance for SR SourceDFSeq SSAdj SSAdj MSFP Feed Speed Depth of cut Feed*Feed Speed*Speed DOC*DOC Feed*Speed Feed*DOC Speed*DOC Residual error Total

Empirical model for SR SR = 5.51 – * feed – *speed * feed* speed * Speed * DOC

Main effect plot for SR at 90 degree insert orientation

Interaction effect

Optimum machining parameters Feed (mm/rev) Speed (rpm) Depth of cut (mm) 500 (level 1) 1000 (level 1) 0.5 (Level -1)

Conclusion ANOVA result showed that for both condition of tool orientation, feed and depth of cut have significant impact on material removal rate. Interaction of speed and DOC also have significant impact on it. For surface roughness, feed and speed has main effect and interaction of feed and speed also has significant impact for 45 degree tool orientation and for 90 degree orientation along with above factors a combination of speed and DOC is also significant.

Future works Results obtained can be used as a model for the selection machining parameters while machining in a VMC in order to obtain optimum MRR and SR. Also possible to study the effect of various other parameters like number of passes, tool diameter, austempering temperature of spheroidal graphite iron and so on. Another possibility is the use of collected data and results in order to find the signal to noise ratio by using the Taguchi method.

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