Ordinal Optimization for Optimal Orientation Problems in 3D Printing

Can Luo, Gang Xiong, Zhishuai Li, Zhen Shen, Li Wan, Meng Chu Zhou, Fei Yue Wang

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations


Compared with a traditional manufacturing process, 3D printing has advantages of performance and cost in personalized customization and has been applied in many fields. The problem of 3D model orientation optimization is a crucial one in practice. In this paper, based on the mathematical relationship between model orientation and printing time, surface quality, and supporting area, the model orientation problem is transformed into a multi-objective optimization problem with goal of minimizing printing time, surface quality, and supporting area. Ordinal Optimization (OO) is not only applicable to problems with random factors, but also to solve complex deterministic problems. The model orientation is a complex deterministic problem. We solve it with OO in this paper and use linear weighting to convert the multi-objective optimization problem into single-objective one. Finally, we compare the experimental results of solving 3D model orientation problems solved by OO and Genetic Algorithm (GA). The results show that OO requires less calculation time than GA while achieving comparable performance.

Original languageEnglish (US)
Pages (from-to)97-102
Number of pages6
Issue number5
StatePublished - 2020
Externally publishedYes
Event3rd IFAC Workshop on Cyber-Physical and Human Systems, CPHS 2020 - Beijing, China
Duration: Dec 3 2020Dec 5 2020

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering


  • 3D Printing
  • Digital Manufacturing
  • Genetic Algorithm
  • Intelligent Optimization
  • Machine Learning
  • Ordinal Optimization
  • Orientation Optimization


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