Minimizing Shrinkage Porosity by Optimizing Some Parameters of Al Alloy (6061T6) Using DOE

Response surface methodology is an efficient and powerful tool which is widely applied for casting optimization. In this research aluminum alloy wheel hub casting is done by using BOXBEHNKEN design, three level of each parameter were taken. Solid modeling of casting and gating system is done by CAD. Simulation of Aluminium Alloy (6061 T6) casting were perform in PRO-cast (2009.1) the simulation result indicates that selected parameters significantly affect the quality of casting. ANOVA is employed to examine the relationship between the factors. Input parameter namely flow rate, pouring temperature and runner size were taken to reduce the volume of shrinkage porosity. Experimental Design consist 15 experimental trials and output data obtained from simulation will be optimized through minitab-18. Result indicates that selected independent variables are significantly influence the response. ANOVA gives the optimized value of selected factors which reduces the porosity volume up to 30cm³. Keywords: Sand casting, Shrinkage porosity, Simulation, DOE, Response surface method.

Visualizing Exact and Approximated 3D Empirical Attainment Functions

Most real-world engineering optimization problems are inherently multiobjective, for example, searching for trade-off solutions of high quality and low cost. As no single optimal solution exists for such problems, multiobjective optimizers provide sets of optimal (or near-optimal) trade-off solutions to choose from. The empirical attainment function (EAF) is a powerful tool that can be used to analyze and compare the performance of these optimizers. While the visualization of EAFs is rather straightforward in two objectives, the three-objective case presents a great challenge as we need to visualize a large number of 3D cuboids. This paper addresses the visualization of exact as well as approximated 3D EAF values and differences in these values provided by two competing multiobjective optimizers. We show that the exact EAFs can be visualized using slicing and maximum intensity projection (MIP), while the approximated EAFs can be visualized using slicing, MIP, and direct volume rendering. In addition, the paper demonstrates the use of the proposed visualization techniques on a steel casting optimization problem.

Optimal Riser Design by STL Arbitrarily Slitting Method and Genetic Algorithm

It is essential to consider the proper position and volume of riser during the design of feeding system. In this paper, we are presenting a new method of designing optimal riser in the steel casting processes. A technique of slitting arbitrarily 3D entity model by STL (Stereolithography) was used to obtain accurate values of the partial modulus of casting, and then a mathematical model of the process of the riser design was optimized by a genetic algorithm (GA); with the help of the CAE system, which has an ability to calculate automatically and verify the validity of the optimized results, we will pursue the goal of obtaining the desired riser with optimal size and distribution but without causing any defect in casting. Thus, by combining the numerical optimization with the traditional riser design, our method proposed here will be more practical and reliable. In the end, we give an example to demonstrate the feasibility of our optimized approach in the riser design. Keywords：Riser design, Genetic Algorithm, casting optimization, STL