A multi-objective optimization based on machine learning for dimension precision of wax pattern in turbine blade manufacturing

IF 4.2 2区 工程技术 Q2 ENGINEERING, MANUFACTURING Advances in Manufacturing Pub Date : 2024-05-17 DOI:10.1007/s40436-024-00492-2
Jing Dai, Song-Zhe Xu, Chao-Yue Chen, Tao Hu, San-San Shuai, Wei-Dong Xuan, Jiang Wang, Zhong-Ming Ren
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Abstract

Wax pattern fabrication in the investment casting of hollow turbine blades directly determines the dimension accuracy of subsequent casting, and therefore significantly affects the quality of final product. In this work, we develop a machine learning-based multi-objective optimization framework for improving dimension accuracy of wax pattern by optimizing its process parameters. We consider two optimization objectives on the dimension of wax pattern, i.e., the surface warpage and core offset. An active learning of Bayesian optimization is employed in data sampling to determine process parameters, and a validated numerical model of injection molding is used to compute objective results of dimension under varied process parameters. The collected dataset is then leveraged to train different machine learning models, and it turns out that the Gaussian process regression model performs best in prediction accuracy, which is then used as the surrogate model in the optimization framework. A genetic algorithm is employed to produce a non-dominated Pareto front using the surrogate model in searching, followed by an entropy weight method to select the most optimal solution from the Pareto front. The optimized set of process parameters is then compared to empirical parameters obtained from previous trial-and-error experiments, and it turns out that the maximum and average warpage results of the optimized solution decrease 26.0% and 20.2%, and the maximum and average errors of wall thickness compared to standard part decrease from 0.22 mm and 0.051 7 mm using empirical parameters to 0.10 mm and 0.035 6 mm using optimized parameters, respectively. This framework is demonstrated capable of addressing the challenge of dimension control arising in the wax pattern production, and it can be reliably deployed in varied types of turbine blades to significantly reduce the manufacturing cost of turbine blades.

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基于机器学习的多目标优化方法,用于提高涡轮叶片制造中蜡型的尺寸精度
空心涡轮叶片熔模铸造过程中的蜡型制作直接决定了后续铸件的尺寸精度,因此对最终产品的质量有重大影响。在这项工作中,我们开发了一个基于机器学习的多目标优化框架,通过优化工艺参数来提高蜡型的尺寸精度。我们考虑了蜡型尺寸的两个优化目标,即表面翘曲和型芯偏移。在数据采样中采用贝叶斯优化的主动学习方法来确定工艺参数,并使用经过验证的注塑成型数值模型来计算不同工艺参数下的尺寸目标结果。然后利用收集到的数据集来训练不同的机器学习模型,结果发现高斯过程回归模型在预测准确性方面表现最佳,并将其用作优化框架中的代用模型。在搜索过程中,采用遗传算法利用代用模型生成非主导帕累托前沿,然后采用熵权法从帕累托前沿中选择最优解。优化后的工艺参数集与之前试错实验获得的经验参数进行了比较,结果发现,优化方案的最大翘曲结果和平均翘曲结果分别降低了 26.0% 和 20.2%,与标准零件相比,壁厚的最大误差和平均误差分别从使用经验参数时的 0.22 mm 和 0.051 7 mm 降至使用优化参数时的 0.10 mm 和 0.035 6 mm。事实证明,该框架能够解决蜡型生产中出现的尺寸控制难题,并能可靠地应用于各种类型的涡轮叶片,从而显著降低涡轮叶片的制造成本。
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来源期刊
Advances in Manufacturing
Advances in Manufacturing Materials Science-Polymers and Plastics
CiteScore
9.10
自引率
3.80%
发文量
274
期刊介绍: As an innovative, fundamental and scientific journal, Advances in Manufacturing aims to describe the latest regional and global research results and forefront developments in advanced manufacturing field. As such, it serves as an international platform for academic exchange between experts, scholars and researchers in this field. All articles in Advances in Manufacturing are peer reviewed. Respected scholars from the fields of advanced manufacturing fields will be invited to write some comments. We also encourage and give priority to research papers that have made major breakthroughs or innovations in the fundamental theory. The targeted fields include: manufacturing automation, mechatronics and robotics, precision manufacturing and control, micro-nano-manufacturing, green manufacturing, design in manufacturing, metallic and nonmetallic materials in manufacturing, metallurgical process, etc. The forms of articles include (but not limited to): academic articles, research reports, and general reviews.
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