Asset Details
Do you wish to reserve the book?
Multi-objective optimization design of injection molding process parameters based on the improved efficient global optimization algorithm and non-dominated sorting-based genetic algorithm
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Multi-objective optimization design of injection molding process parameters based on the improved efficient global optimization algorithm and non-dominated sorting-based genetic algorithm
Do you wish to request the book?
Multi-objective optimization design of injection molding process parameters based on the improved efficient global optimization algorithm and non-dominated sorting-based genetic algorithm
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Multi-objective optimization design of injection molding process parameters based on the improved efficient global optimization algorithm and non-dominated sorting-based genetic algorithm
2015
Overview
This paper develops a framework that tackles the Pareto optimum of injection process parameters for multi-objective optimization of the quality of plastic part. The processing parameters such as injection time, melt temperature, packing time, packing pressure, cooling temperature, and cooling time are studied as model variables. The quality of plastic part is measured by warp, volumetric shrinkage, and sink marks, which is to be minimized. The two-stage optimization system is proposed in this study. In the first stage, an improved efficient global optimization (IEGO) algorithm is adopted to approximate the nonlinear relationship between processing parameters and the measures of the part quality. In the second stage, non-dominated sorting-based genetic algorithm II (NSGA-II) is used to find a much better spread of design solutions and better convergence near the true Pareto optimal front. A cover of liquid crystal display part is optimized to show the method. The results show that the Pareto fronts obtained by NSGA-II are distributed uniformly, and this algorithm has good convergence and robustness. The pair-wise Pareto frontiers show that there is a significant trade-off between warpage and volumetric shrinkage, and there is no significant trade-off between sink marks and volumetric shrinkage and between sink marks and warpage.
Publisher
Springer London,Springer Nature B.V
We currently cannot retrieve any items related to this title. Kindly check back at a later time.