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.

[1]  Yong-Hee Kim,et al.  Fast GPU Implementation for the Solution of Tridiagonal Matrix Systems , 2005 .

[2]  Pio G. Iovenitti,et al.  Part Build Orientations Based on Volumetric Error in Fused Deposition Modelling , 2000 .

[3]  Kai Wang,et al.  A GPU-Based Parallel Genetic Algorithm for Generating Daily Activity Plans , 2012, IEEE Transactions on Intelligent Transportation Systems.

[4]  Wang Ling.  Hybrid quantum genetic algorithms and performance analysis , 2005 .

[5]  Syed H. Masood,et al.  A volumetric approach to part-build orientations in rapid prototyping , 2001 .

[6]  Kai Wang,et al.  Agent-based traffic simulation and traffic signal timing optimization with GPU , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[7]  Zhu Wen-xing.  The control of the urban main road traffic flows based on multi-objective optimization , 2004 .

[8]  Kwan H. Lee,et al.  Determination of the optimal build direction for different rapid prototyping processes using multi-criterion decision making , 2006 .

[9]  Jonas Tölke,et al.  Implementation of a Lattice Boltzmann kernel using the Compute Unified Device Architecture developed by nVIDIA , 2009, Comput. Vis. Sci..

[10]  Zhen Shen,et al.  GPU Based Genetic Algorithms for the Dynamic Sub-area Division Problem of the Transportation System , 2014 .

[11]  Syed H. Masood,et al.  A generic algorithm for a best part orientation system for complex parts in rapid prototyping , 2003 .

[12]  Yu Fan-hua.  Grey particle swarm algorithm for multi-objective optimization problems , 2006 .

[13]  Duc Truong Pham,et al.  Part Orientation in Stereolithography , 1999 .

[14]  G. Reklaitis,et al.  A simulation based optimization approach to supply chain management under demand uncertainty , 2004, Comput. Chem. Eng..

[15]  Wolfgang Banzhaf,et al.  Deployment of CPU and GPU-based genetic programming on heterogeneous devices , 2009, GECCO '09.

[16]  Giorgio Franceschetti,et al.  Efficient hybrid stripmap/spotlight SAR raw signal simulation , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[17]  Kalyanmoy Deb,et al.  Parallelization of binary and real-coded genetic algorithms on GPU using CUDA , 2010, IEEE Congress on Evolutionary Computation.

[18]  Tetsu Narumi,et al.  Overheads in Accelerating Molecular Dynamics Simulations with GPUs , 2008, 2008 Ninth International Conference on Parallel and Distributed Computing, Applications and Technologies.

[19]  Alfredo Benso,et al.  GPU acceleration for statistical gene classification , 2010, 2010 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR).

[20]  Shuo-Yan Chou,et al.  Determining fabrication orientations for rapid prototyping with Stereolithography apparatus , 1997, Comput. Aided Des..

[21]  Jesse Karjalainen,et al.  Social manufacturing and business model innovation , 2016, 2016 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI).

[22]  P. M. Pandey,et al.  Optimal part deposition orientation in FDM by using a multicriteria genetic algorithm , 2004 .

[23]  Tien-Tsin Wong,et al.  Implementation of parallel genetic algorithms on graphics processing units , 2009 .

[24]  Jack J. Dongarra,et al.  Accelerating the reduction to upper Hessenberg, tridiagonal, and bidiagonal forms through hybrid GPU-based computing , 2010, Parallel Comput..

[25]  Shigeyoshi Tsutsui,et al.  Solving quadratic assignment problems by genetic algorithms with GPU computation: a case study , 2009, GECCO '09.