A GPU Based Parallel Genetic Algorithm for the Orientation Optimization Problem in 3D Printing
Zhishuai Li, Gang Xiong, Xipeng Zhang, Zhen Shen, Can Luo, Xiuqin Shang, Xisong Dong, Gui-Bin Bian
Abstract
The choice of model orientation is a very important issue in Additive Manufacturing (AM). In this paper, the model orientation problem is formulated as a multi-objective optimization problem, aiming at minimizing the building time, the surface quality, and the supporting area. Then we convert the problem into a single-objective optimization in the linear-weighted way. After that, the Genetic Algorithm (GA) is used to solve the optimization problem and the process of GA is parallelized and implemented on GPU. Experimental results show that when dealing with complex models in AM, compared with CPU only implementation, the GPU based GA can speed up the process by about 50 times, which helps to significantly reduce the optimization time and ensure the quality of solutions. The GPU based parallel methods we proposed can help to reduce the execution time and improve the efficiency greatly, making the processes more efficient.
BibTeX
@inproceedings{icra2019_agpubasedparalle,
title = {A GPU Based Parallel Genetic Algorithm for the Orientation Optimization Problem in 3D Printing},
author = {Zhishuai Li and Gang Xiong and Xipeng Zhang and Zhen Shen and Can Luo and Xiuqin Shang and Xisong Dong and Gui-Bin Bian and Xiao Wang and Fei-Yue Wang},
booktitle = {ICRA 2019},
year = {2019}
}