Genetic Algorithm Based Automatic Input Parameter Calibration Method for the Discrete Element Modeling of Vibration Feeders

Abstract

Using the genetic algorithm, this paper developed an automatic method for calibrating the used four input parameters (Young's modulus, Poisson's ratio, coefficient of restitution, and friction coefficient) in the discrete element modeling (DEM) of vibration feeders. Two objective functions were used: (1) the difference in the results between the DEM simulation and the experiment and (2) the computation time of the DEM simulation. We conducted preliminary experiments to obtain an average experimental response of the vibration feeding and found that single-item-clogging usually arises near the feeder tray outlet. The proposed method managed to find the required set of parameters for minimizing the two objective functions by multi-objective optimization using the genetic algorithm. Overall, we confirmed that the obtained parameter values were physically reasonable in comparison with those of previous studies.

Optimum Design Using Genetic Algorithm and Discrete-element Method: Application to Vibration Feeder Design for E-waste Recycling Automation

“…(1) locking multiple WEEE items and (2) jam ming by single WEEE item. We focused on the former type clogging in this paper; however, the latter type of clogging has been often observed in the experiments of this study and our previous study 20 . The formation of jamming type clogging is difficult to preventeven by the asymmetrical tray.…”

“…According to this preliminary study of mesh simplification, when the number of meshes becomes smaller than 10 -4 compared with the original data, the angle of repose calculated by the DEM varies considerably 19 . The DEM parameters used were the same as our previous study 20 .…”

“…The DEM parameter values were determined on the basis of compari son between the vibration feeder experiment us ing the tapered tray shown in Fig. 2 and its DEM simulation 20 .…”