Multi objective optimization of FDM 3D printing parameters set via design of experiments and machine learning algorithms

通过实验设计和机器学习算法对FDM 3D打印参数集进行多目标优化

阅读:2

Abstract

The choice of the optimal printing setup for Fused Deposition Modeling (FDM) 3D-printing technology is challenging due to complex interactions between process parameters and mechanical properties. This especially affects engineering applications where the maximum performance is required. To address this challenge, this study explores the influence of main controllable printing parameters including layer thickness, extrusion temperature, printing speed and deposition patterns, on the mechanical properties of FDM-printed ABS specimens using the Design-of-Experiments (DoE) approach by a 34 full factorial design. Main-effects and Interaction-effects on tensile strength, elastic modulus, and strain at maximum stress are investigated via ANOVA analysis, providing interesting hints to evaluate at the design stage. Given the complexity of these effects, a deeper investigation is conducted with a quadratic regression model of the Response Surface Method and the Random Forest regressor, with the latter enhancing the predictive capability ( R2 ) on test data by more than 40% for all the mechanical properties. Eventually, a Genetic Algorithm (NSGA-II) is integrated to estimate the optimal parameter set for multiple responses. Overall results indicate that the deposition strategy is the parameter affecting the most the overall mechanical response, with "Lines" pattern providing the best balanced results in maximizing the elastic modulus and the tensile strength, respectively 1381 MPa and 33.3 MPa. Testing of a set of specimens printed with the found optimal parameters confirm the model's prediction.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。