Blow Hole Defect Analysis in Die Casting

based on Industrial Engineering concepts to analyse and minimize the die casting defect

1. BLOW HOLE DEFECT ANALYSIS IN DIE CASTING Presented by, R.Ramesh Assistant professor , Mechanical Engineering, Muthayammal Engineering College, Rasipuram .

2. INTRODUCTION : Die Casting : Die casting  is a metal casting process that is characterized by forcing molten metal under high pressure into a mold cavity Gravity die casting is used to manufacture complex metal components where there is a need for high structural integrity. In this process, liquid metal is fed from below into the die used to form the component under a positive pressure.

3. Casting defects analysis is the process of finding the root cause of occurrence of defects in the rejection of casting and taking necessary steps to reduce the defects and to improve the casting Yield. Various optimization methods to the die casting process parameters: The Gradient search method, the Finite element method (FEM) and taguchi method. Techniques like cause-effect diagrams, design of experiments (DoE), Six Sigma, casting simulation and artificial neural networks (ANN) are used by various researchers for analysis of casting defects.

4. LITERATURE REVIEW AUTHOR TITLE SUMMARY NAME OF THE JOURNAL Rajesh Rajkolhe , J. G. Khan Defects, Causes and Their Remedies in Casting Process: A Review This study aims to finding different defects in casting, analysis of defect and providing their remedies with their causes. In this paper an attempt has been made to list different types of casting defects and their root causes of occurrence. This paper also aims to provide correct guideline to quality control department to find casting defects and will help them to analyze defects which are not desired. International Journal of Research in Advent Technology(2014) Atul Bhujugade, Vijay Sabnis Minimization of Casting Defects Using Casting Simulation Technique and Casting Defects Analysis Using Design of Experiment To perform the casting simulation technique analysis for shrinkage porosity defect and DOE used for sand casting Defect analysis. Taguchi based L9 orthogonal array for the experimental purpose and Analysis using Minitab software for analysis of variance Casting simulation technique analysis is performed. International Journal for Research in Applied Science & Engineering Technology (IJRASET)(2015)

5. Satish Kumar, Arun Kumar Gupta, Pankaj Chandna Optimization of Process Parameters of Pressure Die Casting using Taguchi Methodology Pressure die casting is usually applied for casting of aluminum alloys. The optimization of controllable process parameters such as solidification time, molten temperature, filling time, injection Pressure and plunger velocity. Moreover, by selection of optimum process parameters the pressure die casting defects such as porosity, insufficient spread of molten material, flash etc. are also minimized. International Journal of Mechanical Engineering (2012) Kulkarni Sanjay Kumar J K Sawale , Sampath Rao Study of effect of process parameter setting on porosity levels of aluminium pressure die casting process using Taguchi Methodology in this work that die casting parameters which are related with machine such as first phase speed, second phase speed, first phase length and injection pressure all have significant influence on porosity level. The quality assessment of the die casting part was based on porosity measurement. The experiment have been performed as per the combination of levels of different process parameters suggested by L9 orthogonal array and conformation experiments have been performed to validate the optimum levels of different parameters IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE) (2013)

6. Rasik A Upadhye Dr. Ishwar P Keswani Optimization of Sand Casting Process Parameter Using Taguchi Method in Foundry This paper demonstrates a robust method for formulating a strategy to find optimum factors of process and interactions with a small number of experiments. The process parameters considered are moisture, sand particle size, green compression strength, mould hardness, permeability, pouring temperature, pouring time and pressure test. The results indicated that the selected process parameters significantly affect the casting defects in the foundry. The improvement expected in reduction of casting defects is found to be 37.66 percent International Journal of Engineering Research & Technology (IJERT) , (2012).

7. PROBLEM FORMULATION : During the process of die casting, there is always a chance where defects will occur. Minor defects can be adjusted easily but high reject rates could lead to significant changes at a high cost. Therefore it is essential for die casters to have knowledge on the types of defects and be able to identify the exact root cause, hence eliminate the losses. In order to determine the quality of a casting, you must be able to identify the major defects are analyzed. Main type of defects are Surface defects: Cold flow, Cold shut, Non-fill , Poor-fill , Laps , Flow lines, Mis -run Internal defects: Inclusions, sludge, Porosity, blowholes Dimensional defects: Die temperature, Die Condition, Force of injection, Ejection

8. PROBLEM DESCRIPTION From the literature review, I have choose the problem of blow hole defect reduction. Blow hole problem is major problem of die casting Industry. So in order to analyse and identifies the defect root cause and eliminate the major problem. Due to some environmental factor suddenly affect the casting quality. So, in order to minimize the die casting defects by using design of experiments.

9. OBJECTIVE To minimize the die casting defect by using design of experiment method

10. METHODOLOGY Collecting casting defects from die casting Identify the major defects Analyzing the casting defect Identifying the cause of defect Minimize the defect by optimization techniques Results and conclusions

11. DETAILS ON THE ANALYSIS: In my project work, I am going to optimize the process parameters by using analytical methods i.e.-Statistical modeling and DoE for historical data to be analyzed, from Efficient Engineering Products, Coimbatore. Die casting is a complex process, which is controlled by many process parameters such as die related parameters and machine related parameters. Which has direct impact on casting quality, improper setting is mainly focused on same issue. The main issue is blow hole and porosity , which is caused by improper setting of process parameters. In this paper an industrial component having blow hole problem has been taken. This study proposes the application of taguchi methodology in identifying the optimum process parameters in order to improve the casting quality and reduce the defects.

12. From cause and effect diagaram , the major causes are prioritized and analyse controllable factor are taken like metal temperature, die temperature, die holding time. This factor can be affect the die casting defect. So, proper process parameter setting to be carried out the analysis.

13. TAGUCHI DESIGN PROCEDURE Taguchi’s design procedure involves the following steps. Determine suitable working levels of the design Select proper orthogonal array Run experiment Analyse the data Identify optimal parameter Conformation run Determine the result of parameter design

14. Factor and Levels Number of factors = 3 Number of Levels = 3 S.No FACTOR SYMBOL RANGE LEVEL 1 LEVEL 2 LEVEL 3 1 Metal melting temperature A 650°C - 700°C 650°C 675°C 700°C 2 Die temperature B 250°C- 300°C 250°C 275°C 300°C 3 Die holding time C 65 sec to 85 sec 65sec 75sec 85sec

15. Response A cause and effect diagram can be used for identifying the parameters that affects the response. In this study, casting density has been chosen as a response , since if the casting density is higher, lower the internal defects such as blow holes and porosity. Density of casting Where, ρ –density of casting (gm/cm 3 ) w1- Weight of the casting in air (gram) W2 –Weight of the casting in water (gram) ρ water - density of water (1 gm/cm 3 )

16. Degree of freedom The degree of freedom is a very important value because it determines the minimum number of treatment conditions .DOF is calculated using the following formula. DOF = (Number of Level-1) for each factor+ (Number of levels-1)(Number of levels-1)for each Interaction + one for the average. Dof = (3-1)3+1 = 7 Then the OA 9 table was selected.

17. Experiment Number Levels A B C 1 1 1 1 2 1 2 2 3 1 3 3 4 2 1 2 5 2 2 3 6 2 3 1 7 3 1 3 8 3 2 1 9 3 3 2

18. EXPERIMENTAL PROCEDURE: The experimental setup consists of die casting cell comprising of Gravity die casting machine, an electric holding furnace. The die casting machine is manually operated by the use of ladler for pouring the molten metal from the melting cum holding furnace. The various settings required for this experiment can be set manually and it is monitored. Before starting the experiment, the dies were preheated to a temperature of 250°C to 300°C using a gas burner. The die temperature was measured by using infrared gun. A total of 9 different combinations shown in the above Table were tried and parts were cast for each combination, which totals of 9 casting formed .

19. Test Run Design Trail No: Metal Temperature (A) in °C Die Temperature (B) in °C Die holding time (C) in Seconds 1 650 250 65 2 650 275 75 3 650 300 85 4 675 250 75 5 675 275 85 6 675 300 65 7 700 250 85 8 700 275 65 9 700 300 75

20. Experimental Results: The density of the casting was measured by Archimedes principle and the results are shown below. Minitab 16 software has been used to carry out Taguchi optimization. The response value is taken as density of the sample casting that were cast under each trail condition. Then S/N ratio were computed based upon the formula S/N ratio for larger the better characteristics.   S/N ratio = -10 log 10 (1/r Σ (1/y i 2 ))   where, r -number of observations, yi -Response value for each trail. The experimental results are shown in table 3.

21. MODEL S/N RATIO CALCULATION S/N ratio for treatment condition 1 S/N ratio = -10log10 (Σ (1/2.333 2 )/1) = 7.35829 Similarly the S/N ratio for all treatment condition were calculated and tabulated.

22. MAIN EFFECT CALCULATION The main effect factor A at Level 1 & 2 Effect of factor A1 = (2.333+ 2.702 +2.627)/3 = 2.554   Effect of factor A2 = (2.672+ 2.649 +2.743)/3 = 2.688 Similarly the main effect for all other factors at all levels main effect calculation are carried out and is shown in table

23. DENSITY CALCULATION: Weight of the casting air = 1500 g Weight of the casting in water = 950 g Density of water = 1 g/cm 3 ρ = ( 1500/(1500-950))*1 = 2.727 g/cm 3

24. Test Response Value Trail A B C Density (g/cm 3 ) S/N ratio 1 1 1 1 2.333 7.35829 2 1 2 2 2.702 8.63371 3 1 3 3 2.627 8.38920 4 2 1 2 2.672 8.53673 5 2 2 3 2.649 8.46164 6 2 3 1 2.743 8.76452 7 3 1 3 2.654 8.47802 8 3 2 1 2.690 8.59505 9 3 3 2 2.727 8.71370

25. Response Table for Signal to Noise Ratios Larger is better Level A B C 1 8.127 8.124 8.239 2 8.588 8.563 8.628 3 8.596 8.622 8.443 Delta 0.469 0.498 0.389 Rank 2 1 3 Response Table for Means Level A B C 1 2.554 2.553 2.589 2 2.688 2.680 2.700 3 2.690 2.699 2.643 Delta 0.136 0.146 0.112 Rank 2 1 3

26. From the response graph the optimized results are A3, B3, and C2. Higher the Density values to reduce the internal defect of blow hole. So, the optimized process parameters are Metal Temperature: 700°C Die Temperature: 300°C Die holding time: 75sec These are values to conduct the experiment.

27. ANOVA table for Means and S/N Ratios:   Analysis of Variance for SN ratios Source DF Seq SS Adj SS Adj MS F P A 2 0.4317 0.4317 0.2158 1.34 0.428 B 2 0.4444 0.4444 0.2222 1.38 0.421 C 2 0.2269 0.2269 0.1134 0.70 0.587 Residual Error 2 0.3226 0.3226 0.1613 Total 8 1.4256 Analysis of Variance for Means Source DF Seq SS Adj SS Adj MS F P A 2 0.03655 0.03655 0.018274 1.36 0.424 B 2 0.03788 0.03788 0.018939 1.41 0.416 C 2 0.01871 0.01871 0.009353 0.69 0.590 Residual Error 2 0.02696 0.02696 0.013478 Total 8 0.12009

28. From the ANOVA table for SN ratio, F value is most significant factor of Die temperature and next factor is Metal Temperature. Die holding time are also significant.

29. Prediction of optimum response: To predict the mean at the optimum condition and then compare them against a confirmation experiment. For mean response the overall average of casting density (ρ) is 2.644. The predicted optimal response (μ) is: µ predicted = Estimate of the process mean at optimum condition   = ρ.+(A3-ρ.)+(B3-ρ.)+(C2-ρ.) Where, µ = Predicted Value, ρ. = Mean responses of L9 taguchi method, A3 = Mean Metal melting temperature at higher level, B3 = Mean Die temperature at higher level, C2 = Mean Die holding time at middle level. So, the prediction equation the value is = 2.644+ (2.690-2.644)+(2.699-2.644)+(2.700-2.644) = 2.644 +0.046+0.055+0.056 = 2.801 g/cm 3

30. Percentage of Predicted increase: = (1- (Average of response/predicted response))*100 = (1-(2.644/2.801))*100 = 5.60% of Predicted increase.   The optimum casting density of the experiment is 2.758 g/cm 3 .   = (1-(Average of response/Optimum response))*100 = (1-(2.644/2.758))*100 = 4.13% Then to calculate the optimum value is 4.13% increased.  

31. CONCLUSION The optimization procedure has been made to study the effect of die casting process parameters on casting density. Generally when the casting density is higher, internal defects such as blow holes and porosity is eliminated. So, the basic idea is to provide a decision tool for setting optimum parameters so that the defects occurring in the casting is reduced. Taguchi method was applied for optimizing the die casting process parameters, and the results obtained using this method was useful in eliminating the blow holes problem in housing assembly product. The optimum values are Metal temperature 700°C, Die temperature 300°C, Die holding time 75seconds . After that the predicted optimum response of Die casting product is nearly equal to the Taguchi optimum response. The future work is to analysis different confirmation test like Radiography test, simulation, PROCAST software, and SOLID CAST software to analyzed and reduced the Die casting defects.

32. REFERENCES: Atul A. Bhujugade , Vijay B. Sabnis , Minimization of Casting Defects Using Casting Simulation Technique and Casting Defects Analysis Using Design of Experiment , International Journal for Research in Applied Science & EngineeringTechnology (IJRASET)(2015). Satish Kumar, Arun Kumar Gupta, Pankaj Chandna , Optimization of Process Parameters of Pressure Die Casting using Taguchi Methodology , International Journal of MechanicalEngineering Vol:6, No:8, 2012. Rajesh Rajkolhe , J. G. Khan Defects, Causes and Their Remedies in Casting Process: A Review, International Journal of Research in Advent Technology(2014). Kulkarni Sanjay Kumar, J K Sawale , Sampath Rao , Study of effect of process parameter setting on porosity levels of aluminium pressure die casting process using Taguchi Methodology, IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE), PP 12-17, 2013. Rasik A Upadhye ,Dr. Ishwar P Keswani , Optimization of Sand Casting Process Parameter Using Taguchi Method in Foundry , International Journal of Engineering Research & Technology (IJERT) ,2012. Mr. Siddalingswami . S. Hiremath , Dr. S. R. Dulange , Advanced Tehniques in Casting Defects And Rejection Analysis: A Study in an Industry, International Journal of Innovations in Engineering Research and Technology [IJIERT], : ISSN2394-3696VOLUME 2, ISSUE 9, SEP.-2015.

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