In order to conform to dimensional tolerances, an efficient numerical method, displacement iterative compensation method, based on finite element methodology （FEM） was presented for the wax pattern die profile desig...In order to conform to dimensional tolerances, an efficient numerical method, displacement iterative compensation method, based on finite element methodology （FEM） was presented for the wax pattern die profile design of turbine blades. Casting shrinkages at different positions of the blade which was considered nonlinear thermo-mechanical casting deformations were calculated. Based on the displacement iterative compensation method proposed, the optimized wax pattern die profile can be established. For a A356 alloy blade, substantial reduction in dimensional and shape tolerances was achieved with the developed die shape optimization system. Numerical simulation result obtained by the proposed method shows a good agreement with the result measured experimentally. After four times iterations, compared with the CAD model of turbine blade, the total form error decreases to 0.001 978 mm from the orevious 0.515 815 mm.展开更多
The aim of this study is to evaluate the maximum and minimum distances between the model and the cast crown of three techniques using Scanning Electron Microscopy (SEM). Three technique groups were used for this study...The aim of this study is to evaluate the maximum and minimum distances between the model and the cast crown of three techniques using Scanning Electron Microscopy (SEM). Three technique groups were used for this study: group A (control), traditional manual wax patterns;group B, dipping wax patterns;group C, resin patterns made with CAD/CAM. For each group, 10 samples were made using the same model, and then metal cast. Marginal accuracies of the samples were evaluated by performing gap measurements using SEM with a magnification of 1200× (minimum distance). The data were statistically analyzed using the one-way analysis of variance (ANOVA) at the 0.05 significance level. The average (standard deviation) of the minimum distance [μm] was 22.5 (12.1), 9.9 (4.3), and 14.7 (6.6), in groups A, B, and C, respectively. The average standard deviation of gap area [μm2] was 21667.2 (3476.4), 9906.4 (1512.1), and 16048.8 (8123). In the minimum distance comparison, groups A and B (p = 0.006) showed statistically significant results. In the gap area comparison, there was no statistical significance among groups A, B, and C (p = 0.174). The marginal adaptations of all three techniques were within a reported clinically acceptable range of margin.展开更多
As an important index affecting the aerodynamic performance and the structural strength of hollow turbine blades, the wall-thickness precision of the blade is mainly inherited from the positional relationship between ...As an important index affecting the aerodynamic performance and the structural strength of hollow turbine blades, the wall-thickness precision of the blade is mainly inherited from the positional relationship between the corresponding wax pattern and the internal ceramic core.However, due to locating errors, the actual position of ceramic core is always deviated from the ideal position, which makes it difficult to guarantee the wall-thickness precision of the wax pattern.To solve this problem, a wall-thickness compensation strategy is proposed in this paper. Firstly,based on the industrial computed tomography(ICT) technique and curve matching algorithms, a model reconstruction method is developed, with which the 3D model of a trial wax pattern can be easily constructed. After that, focusing on eliminating the wall-thickness errors of the trial wax pattern, an optimization method for the pose of the ceramic core in the wax pattern is proposed. Then, by mapping the optimal pose of the ceramic core to length adjustments of the locating rods, the wall-thickness errors of the wax pattern can be greatly reduced. A case study is also given to illustrate the effectiveness of the proposed compensation strategy.展开更多
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...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 0.0517 mm using empirical parameters to 0.10 mm and 0.0356 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.展开更多
基金Project (2008ZE53042) supported by National Aerospace Science Foundation of China
文摘In order to conform to dimensional tolerances, an efficient numerical method, displacement iterative compensation method, based on finite element methodology （FEM） was presented for the wax pattern die profile design of turbine blades. Casting shrinkages at different positions of the blade which was considered nonlinear thermo-mechanical casting deformations were calculated. Based on the displacement iterative compensation method proposed, the optimized wax pattern die profile can be established. For a A356 alloy blade, substantial reduction in dimensional and shape tolerances was achieved with the developed die shape optimization system. Numerical simulation result obtained by the proposed method shows a good agreement with the result measured experimentally. After four times iterations, compared with the CAD model of turbine blade, the total form error decreases to 0.001 978 mm from the orevious 0.515 815 mm.
文摘The aim of this study is to evaluate the maximum and minimum distances between the model and the cast crown of three techniques using Scanning Electron Microscopy (SEM). Three technique groups were used for this study: group A (control), traditional manual wax patterns;group B, dipping wax patterns;group C, resin patterns made with CAD/CAM. For each group, 10 samples were made using the same model, and then metal cast. Marginal accuracies of the samples were evaluated by performing gap measurements using SEM with a magnification of 1200× (minimum distance). The data were statistically analyzed using the one-way analysis of variance (ANOVA) at the 0.05 significance level. The average (standard deviation) of the minimum distance [μm] was 22.5 (12.1), 9.9 (4.3), and 14.7 (6.6), in groups A, B, and C, respectively. The average standard deviation of gap area [μm2] was 21667.2 (3476.4), 9906.4 (1512.1), and 16048.8 (8123). In the minimum distance comparison, groups A and B (p = 0.006) showed statistically significant results. In the gap area comparison, there was no statistical significance among groups A, B, and C (p = 0.174). The marginal adaptations of all three techniques were within a reported clinically acceptable range of margin.
基金co-supported by the National Natural Science Foundation of China (Nos. 51475374 and 51505387)the Fundamental Research Funds for the Central Universities (No. 3102015ZY087)
文摘As an important index affecting the aerodynamic performance and the structural strength of hollow turbine blades, the wall-thickness precision of the blade is mainly inherited from the positional relationship between the corresponding wax pattern and the internal ceramic core.However, due to locating errors, the actual position of ceramic core is always deviated from the ideal position, which makes it difficult to guarantee the wall-thickness precision of the wax pattern.To solve this problem, a wall-thickness compensation strategy is proposed in this paper. Firstly,based on the industrial computed tomography(ICT) technique and curve matching algorithms, a model reconstruction method is developed, with which the 3D model of a trial wax pattern can be easily constructed. After that, focusing on eliminating the wall-thickness errors of the trial wax pattern, an optimization method for the pose of the ceramic core in the wax pattern is proposed. Then, by mapping the optimal pose of the ceramic core to length adjustments of the locating rods, the wall-thickness errors of the wax pattern can be greatly reduced. A case study is also given to illustrate the effectiveness of the proposed compensation strategy.
基金funded by the National Key Research and Development Program of China(Grant No.2019YFA0705302)the National Science and Technology Major Project“Aeroengine and Gas Turbine”of China(Grant No.2017-VII-0008-0102).
文摘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 0.0517 mm using empirical parameters to 0.10 mm and 0.0356 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.
