Improving Sand Casting for Small Scale Foundry
CHAPTER ONE
Objectives
- To establishthe optimum settings of molding sand related parameters for improving selected cast component using Taguchi Method.
- To analyze filling and solidification related defects using casting simulation technique (Click2Cast).
- To measure the effectiveness of the proposed techniques through confirmation test.
CHAPTER TWO
LITERATURE REVIEW
This section describes the previous works and achieved results of various researchers on casting process using different techniques. Taguchi model is suitable for robust design such as designing processes or products for minimizing variation of components or environmental conditions or variation around a target value. Taguchi’s method was used to determine the optimal process parameters for the green sand casting process in an automobile foundry industry, India. The authors observed that process parameters (i.e., moisture content, green strength, pouring temperature, mold hardness vertical and mold hardness horizontal) significantly affect the casting defects. A process window approach was applied for minimizing the error of casting cast iron flywheel. The researcher showed that casting quality involves with a large number of process variables in his gear blank casting process.
Taguchi’s method was also used for optimizing the mechanical properties of the Vacuum V- casting process. The effect of transient heat transfer, foam degradation and liquid metal flow in casting process was studied through a simple mathematical model. The author determined the optimal process parameters using the Taguchi’s method for the green sand casting process. The selected process factors are moisture content, green compression strength, permeability, and mold hardness which significantly affect the casting defects of spheroidal graphite cast iron rigid coupling castings. Control factors are the independent variables of any experiment that have various effects on the response or output variables at different levels of input variables. They can be subdivided into qualitative control factors and quantitative control factors. Noise factors are the uncontrollable variables that influence the output variables. This factors may or may not be known. For preventing the noise factors from interfering in the experimental results special attention should be given. Recently researchers applied Taguchi approach for optimizing green casting process parameters for enhancing the quality of mild steel. In Aluminum re-melting process a robust design technique was applied for finding out the optimal settings of design parameters for increasing performance, reducing quality and cost. Taguchi approach is applied in other casting processes. In die casting for Aluminum alloy the authors analyzed different process parameters and achieved optimal levels of die casting parameters for improving casting yield. Some other techniques such as artificial neural network was applied for identifying the complex relationship in hot-deformation process. Through this network, it reduced the number of experiments required to characterize a material’s behavior and also the problems associated with empirical, semi-empirical models which involve with the evaluation of a great number of constants.
In modern manufacturing defect free casting production is a great challenge for reducing the percentage of scrap. The formation of various casting defects is not only related with sand casting process parameters but also highly related to nature of fluid flow during the mold filling stage and type of solidification. Any improper designing of gating and feeding system results in mold erosion, air entrapment, nonuniform solidification, shrinkage porosities, lower casting yield (%) etc. Therefore, it is necessary to take special care in designing gating and feeding system to obtain defect free casting. The main function of gating system is to carry clean molten metal from ladle to the casting cavity ensuring complete filling.
For a given casting geometry an optimized gating design satisfying the entire requirement is obtained by experimentation through trial and error methods. But this technique takes a long time to get the desired dimensions of the gating channels and also increases cost to the company. This problem can be solved easily by using simulation that represents the actual mold filling and solidification process, so that we can predict the results in advance before producing actual casting. Research work published on optimization of gating system recommends maximizing the casting yield, minimizing the in-gate velocity of molten metal, ensuring directional solidification, optimizing the in-gate and riser location.
CHAPTER THREE
Materials and methods
Materials
The materials used in this research are green sand as molding material and wooden pattern of flywheels. The equipment used included molding box, rammer, runner, riser, shovel, furnace, and crucible, draw screw and vent wire. For casting, aluminum alloy is collected from local market. But in simulation AlSi7Mg aluminum alloy is considered and its chemical composition is shown in the Table 1.
CHAPTER FOUR
EXPERIMENTAL RESULTS & DISCUSSIONS
Process parameter optimization results
Taguchi method analysis results
At first sand casting is performed according to L18 orthogonal array of Taguchi approach. Table 3 represents the experimental mean values of casting defects which are visually inspected. Rejection rate is determined from the ratio rejected metal due to casting defects to the amount of metal poured.
CHAPTER FIVE
Conclusion
The feasibility of using statistical techniques for reducing casting defects have been proved successfully. It can be concluded that simulation helps to visualize filling and solidification phenomena with no wastage of time, energy, labor and money. Hence casting simulation enables to provide ‘correct at the first time’ through preventing potential problems related to flow of metals or during the time of freezing compatible with both product requirements and foundry capability. This technique also reduces lead time, increases productivity and minimizes the percentage of rejections. A high percentage of casting defects in job shop has a great impact on three parameters like method design, process capabilities and low compatibility between part requirements. For achieving the desired quality at the least cost without shop-floor trials these three must be optimized in an integrated manner. “These facts can make all the small and medium foundries in Bangladesh to implement simulation activity in casting as the need of hour.”
Future Scope of thesis
In future other process parameters such as grain size of sand, dry compression strength, hardness number of molding sand may be varied for finding out the effect of this parameters on rejection rate. This gating and feeding system may be analyzed for other cast material.
REFERENCES
- Johnson,E., 1989, “Design of Experiments: Taguchi in the foundry”. AFS Trans.,Vol.82, pp. 415-418.
- Kumar P., Gaindhar J., L., 1995, “Off-line quality control for V-process castings”.Quality Reliable Engineering International, 11, pp. 175-181.
- Rao, K., Prasad Y.,K.,D.,V., 1995, “Neural network approach to flow stress evaluation in hot deformation”. Journal of Materials Processing Technology, Vol. 53, pp.552-566.
- J. Ross, 1995, “Taguchi techniques for quality engineering”, McGraw-Hill, NewYork.
- Kumar Sushil, Satsangi P., S., and Prajapati D., R., 2011, “Optimization of green sandcasting process parameters of a foundry by using Taguchi’s method”. International Journal of Advanced Manufacturing Technology, 55, pp.23-54.
- Kumaravadivel,and Natarajan U., 2013, “Optimization of sand-casting process variables—a process window approach”. International Journal of Advanced Manufacturing Technology, Vol.66, pp.695-709.
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- Enright,P., Prince B., 1983, “Offline quality control parameter estimation and experimental design with the Taguchi method”. AFS Transactions, pp. 393–400.
- Guharaja S., Noorul Haq A., Karuppannan K.,M., 2006, “Optimization of green sandcasting process parameters by using Taguchi’s method”. International Journal of Advanced Manufacturing Technology, 30, pp.1040–1048.
