Calibrating polypropylene particle model parameters with upscaling and repose surface method

The discrete element method (DEM) is a computational technique extensively utilized for simulating particles on a large scale, specifically focusing on granular materials. Nonetheless, its implementation requires a substantial amount of computational power and accurate material properties. Consequently, this study delves into an alternative approach referred to as volume-based scaled-up modeling, aiming to simulate polypropylene particles using DEM while mitigating the computational burden and regenerating new material properties. This novel method aims to reduce the CPU time required for the simulation process and represent both the macro mechanical behavior and micro material properties of polypropylene particles. To accomplish this, the dimensions of the polypropylene particles in the DEM simulation were magnified by a factor of two compared to the original size of the prolate spheroid particles. In order to determine the virtual micro material properties of the polypropylene particles, a calibration method incorporating the design of experiments (DOE) and repose surface methodology was employed. The predicted bulk angle of repose (AOR) derived from the upscaled DEM parameters exhibited a remarkably close agreement with the empirical AOR test, demonstrating a small relative error of merely 1.69 %. Moreover, the CPU time required for the upscaled particle model proved to be less than 71 % of that necessary for the actual-scale model of polypropylene particles. These compelling results confirm the effectiveness of enlarging the particle volume used to calibrate micro-material properties in the Discrete Element Method (DEM) through the DOE technique. This approach proves to be a reliable and efficient metho

Optimizing Design Parameters for Maximizing Mass Discharge Rates in Silos for Soybeans Using Dem Simulations

The purpose of this study is to utilize the discrete element method (DEM) in combination with the design of experiment (DOE) approach to determine the appropriate key parameters for optimizing the discharge silo. The researchers employed a full factorial design of the experiment and response surface methodology to establish the mass discharge rate (MDR) of soybeans from the silo. By employing an optimal exact methodology, the study identified suitable values for the discharge angle and outlet width of the hopper that would result in the maximum mass discharge rate. The findings demonstrate the effectiveness of the silo discharge design and provide valuable guidance for designing silos and hoppers

Optimized plastic injection molding process and minimized the warpage and volume shrinkage by response surface methodology with genetic algorithm and firefly algorithm techniques

228-238This goal of this paper is an optimization approach to generate suitable process setting of multi responses of the minimization of warpage and volume shrinkage in the plastic injection molding (PIM). Central composite design (CCD) was employed to handle the orthogonal array for experimental test runs and using the response surface methodology (RSM) to construct response surface equation model. Then the optimization methods of firefly algorithm (FA) that have never been applied to minimize warpage and volume shrinkage in the plastic injection molding (PIM) and genetic algorithm (GA) were employed to optimal parameter conditions with fitness function generated from RSM. Simulation software Moldex 3D and plastic injection machine were used as the experimental tests to show the comparison of the optimal performance of both metaheuristic algorithms. The results showed that the firefly algorithm created the suitable process parameters to meet the minimization of warpage and volume shrinkage better than the popular genetic algorithm for this study.  It can be concluded that FA is very proper to approach the good performance in PIM