Simulation and Optimization of Warpage of Fiber Reinforced using Human Behavior Based Optimization

Warpage is one of the major defects in injection molding and this affects the quality of the materials. Some techniques are used to minimize the warpage by the changing the parameter settings. The optimization techniques were applied to the parameter to find the optimized value. The popular method in optimizing the parameter is Genetic Algorithm (GA) and this has the limitation of big stochastic components. The main objective of this research is to propose the Human Behavior Based Optimization (HBBO) in the warpage. This method doesn’t have large stochastic and has a fast convergence rate. The orthogonal Array is used to measure the warpage for the different parameter settings. The fiber reinforced component is used to measure the performance of the proposed method. The Back Propagation Neural Network is used to find the relationship between the warpage and other factors. Then optimization technique is applied to find the parameter value. The experimental result of the proposed HBBO method in Warpage optimization is compared with other existing method in warpage optimization. The HBBO method has the warpage of 0.0858 and the GA method has the warpage of 0.0953.