A GPU Based Parallel Genetic Algorithm for the Orientation Optimization Problem in 3D Printing

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.

Authors

• Zhishuai Li
• Gang Xiong 0001
• Xipeng Zhang
• Zhen Shen 0004
• Can Luo
• Xiuqin Shang
• Xisong Dong
• Gui-Bin Bian
• Xiao Wang 0002
• Fei-Yue Wang 0001

Keywords

• Optimization
• Silicon
• Solid modeling
• Graphics processing units
• Genetic algorithms
• Three-dimensional printing
• Optimization Problem
• 3D Printing
• Graphics Processing Unit
• Parallel Genetic Algorithm
• Parallelization
• Multi-objective Optimization
• Additive Manufacturing
• Surface Quality
• Multi-objective Optimization Problem
• Maximum And Minimum
• Computation Time
• Fitness Function
• Feasible Solution
• Optimization Variables
• Binary Code
• Stereolithography
• Parallel Algorithm
• Open Reduction
• Height Model
• Orientation Model
• Triangular Facets
• Calculation Of Indicators
• Parallel Optimization
• Single-objective Optimization Problem
• Triangular Prism
• Printing Time
• Vertex Coordinates
• Digital Light Processing
• Orientation Optimization
• GPU
• parallel computing
• genetic algorithm