Sink-mark minimization in injection molding through response surface regression modeling and genetic algorithm

Multi-objective optimization of injection molding process parameters for short cycle time and warpage reduction using conformal cooling channel

“…The approach combining the approximation technique such as the NN, a radial basis function (RBF) network, and the Kriging and the optimization technique such as the GA and the sequential quadratic programming (SQP) is widely used for process parameter optimization in Refs. [8][9][10][11][12].…”

Method for modifying convective heat transfer coefficients used in the thermal simulation of a feed drive system based on the response surface methodology

“…The CCD method for four factors (i.e., h b , h s , h n , and h g ) consists of the following three portions: (1) The factorial portion (i.e., 2 4 cube points whose factor levels are coded as À 1 and 1); (2) the axial portion (i.e., 2 � 4 axial points with α ¼ 2 (distance between center and axial points); and (3) n 0 center points. In this study, n 0 ¼ 1 was used [37].…”

Process parameter optimization of plastic injection molding: a review

“…Many such hybrid techniques have been investigated to injection molding optimization also, like GA with the Taguchi method for minimizing warpage of molded components [98], GA with ANN for optimizing the initial process settings [81], genetic neural fuzzy system with 2-stage hybrid learning algorithm to predict product weight [63,64], GA with BPNN to achieve the optimal quality in terms of shear stress [101] and to minimize volumetric shrinkage [100], the Taguchi method combined with ANN and GA to achieve the minimal single response output in terms of warpage in a bus ceiling lamp base [55] and to save energy by multi-objective optimization of process parameters [70], the Taguchi method combined with BPNN and GA to determine the set of data in multiple-input single-output (MISO) by optimizing product weight [17] and to achieve multi response outputs [20], the Taguchi method and response surface method combined with BPNN and GA for predicting mechanical properties by estimating an optimal set of process parameters [110], the Taguchi method with Moldflow ® for finding the efficient frontier for a thin digital camera cover in a MIMO environment [24], Moldflow ® and orthogonal experiment method integrated with BPNN and GA to determine the optimal set of process parameters for optimizing warpage and clamp force [122], the variable complexity method combined with BPNN and GA to mice manufacturing for optimizing multiple objectives [26], GA with response surface methodology to achieve the optimal single response in terms of warpage in thin shell plastic parts [54] and to minimize sink depth in thermoplastic components [75], simulated annealing with ANN to predict part warpage in runner system by optimizing the runner dimensions [121], GA with a gradient-based method to find the optimum process parameters [59], PSO with ANN to optimize process parameters [103], BPNN with the Taguchi method and Davidson-Fletcher-Powell method to determine multiple input process parameters in order to achieve the desired product weight as the single output [18,19], the Latin hypercube sampling method combined with the Kriging method and multi-objective PSO to achieve a better Pareto frontier by reducing simulation cost [21], the response surface method integrated with Moldflow ® and Lingo software to optimize process parameters with corresponding output of warpage and shrinkage [22], GA with the mode-pursuing sampling method for achieving minimum warpage [30], ANN and artificial bee colony algorithm to determine the set of process parameters by minimizing warpage of molded components [42], the Taguchi method ...…”