Building Orientation Determination Based on Multi-Objective Optimization for Additive Manufacturing

Hongyao Shen; Shanshan Guo; Jianzhong Fu; Zhiwei Lin

doi:10.1089/3dp.2019.0106

Building Orientation Determination Based on Multi-Objective Optimization for Additive Manufacturing

3D Print Addit Manuf. 2020 Aug 1;7(4):186-197. doi: 10.1089/3dp.2019.0106. Epub 2020 Aug 12.

Authors

Hongyao Shen^{1

2}, Shanshan Guo^{1

2}, Jianzhong Fu^{1

2}, Zhiwei Lin^{1

2}

Affiliations

¹ The State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical Engineering, Zhejiang University, Hangzhou, China.
² Key Laboratory of 3D Printing Process and Equipment of Zhejiang Province, College of Mechanical Engineering, Zhejiang University, Hangzhou, China.

Abstract

Due to the stratified construction of additive manufacturing (AM), the building orientation of an object greatly affects the manufacturing process and quality of the products. This article proposes an optimization algorithm for AM that comprehensively covers the three main aspects affected by the building direction: efficiency, surface quality, and internal properties. The goal of optimizing efficiency is to cut the manufacturing and postprocessing times by reducing the support volume. In terms of surface quality optimization, a new and comprehensive mathematical model is established. Considering not only the influence of the stepping effect on the whole model but also the supporting contact area, the optimization to the salient area was innovatively proposed. The salient area was defined by cone curvature, which has a significant influence on appearance. Another novel point is the quantitative optimization of the internal properties, which is based on the anisotropic characteristics of AM. The mechanical properties are taken into consideration in this article, and other anisotropic properties can be added to the optimization algorithm by using the same method. Afterward, a particle swarm optimization algorithm was adopted to synchronously optimize the targets just cited, according to the degree of importance of each factor for specific applications. The algorithm was implemented to optimize several different cases with different characteristics. Further, the results in the experiments manifested that the models printed in the optimized orientation performed better than the initial models, and the corresponding comprehensive evaluation scores were improved as well, with an optimization range of 70-90%. And the rate of optimization for support volume, surface roughness, salient area roughness, and maximum tensile strength, respectively, reached 20.9%, 57.3%, 59.5%, and 293.0%.

Keywords: additive manufacturing; building orientation; computer-aided process planning; multi-objective optimization; particle swarm optimization.