This article combined Taguchi method and analysis of variance with the culture-based quantum-behaved particle swarm optimization to determine the optimal models of gating system for aluminium (Al) A356 sand casting part. First, the Taguchi method and analysis of variance were, respectively, applied to establish an L 27 ( 3 8 ) orthogonal array and determine significant process parameters, including riser diameter, pouring temperature, pouring speed, riser position and gating diameter. Subsequently, a response surface methodology was used to construct a second-order regression model, including filling time, solidification time and oxide ratio. Finally, the culture-based quantum-behaved particle swarm optimization was used to determine the multi-objective Pareto optimal solutions and identify corresponding process conditions. The results showed that the proposed method, compared with initial casting model, enabled reducing the filling time, solidification time and oxide ratio by 68.14%, 50.56% and 20.20%, respectively. A confirmation experiment was verified to be able to effectively reduce the defect of casting and improve the casting quality.
KeywordsTaguchi method, analysis of variance, response surface methodology, Al A356, culture algorithm, quantum-behaved particle swarm optimization Date
This study presents the Taguchi method and the Particle Swarm Optimization (PSO) technique which uses mutation (MPSO) and dynamic inertia weight to determine the best ranges of process parameters (extrusion velocity, eccentricity ratio, billet temperature and friction coefficient at the die interface) for a multi-hole extrusion process. A L 18 (2 1 ×3 7 ) array, signal-to-noise (S/N) ratios and analysis of variance (ANOVA) at 99% confidence level were used to indicate the optimum levels and the effect of the process parameters with consideration of mandrel eccentricity angle and exit tube bending angle. As per the Taguchi-based MPSO algorithm using DEFORM TM 3D Finite Element Analysis (FEA) software, the minimum mandrel eccentricity and exit tube bending angles were respectively calculated to be 0.03°, which are significantly less than those based on Genetic Algorithm (GA) and the Taguchi method, respectively. This indicates that the Taguchi-based MPSO algorithm can effectively and remarkably reduce the warp angles of Ti-6Al-4V extruded products and the billet temperature is the most influencing parameter. The results of this study can be extended to multi-hole extrusion beyond four holes and employed as a predictive tool to forecast the optimal parameters of the multi-hole extrusion process.
This paper combines an artificial neural network (ANN) with a traditional genetic algorithm (GA) method, called hybrid genetic algorithm (HGA), to analyze the warpage of multi-cavity plastic injection molding parts. Simulation results indicate that the minimum and the maximum warpage of the hybrid genetic algorithm (HGA) method were lower than that of the traditional GA method and CAE simulation. These results reveal that, when HGA is applied to multi-cavity plastic warpage analysis, the optimal process conditions are significantly better than those using the traditional GA method or CAE simulation.
This work proposes a CRC-aided K-best sphere decoding scheme to improve the performance of lattice codes. The generator of the lattice is designed as to be an upper triangular, which is naturally suited for sphere decoding. When the K is sufficiently large, the naïve K-best sphere decoding can approach the lower bound of block error rate (BLER) of maximum likelihood (ML). Therefore, the proposed scheme can outperforms the naïve K-best sphere decoding with the assistance of CRC code.
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