Investigate on computer-aided fixture design and evaluation algorithm for near-net-shaped jet engine blade

Wu, Dongbo; Zhao, Bing; Wang, Hui; Zhang, Kaiyao; Yu, Jiguo

doi:10.1016/j.jmapro.2020.02.023

Cited by 20 publications

(9 citation statements)

References 14 publications

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“…The acceleration sensor (Dytran 3225M23) is used to measure response curve of bladefixture system excited by the force hammer (Dytran 5850B), the LMS data-acquisition system is used to collect the displacement response signal of the blade-fixture system from the acceleration sensor. Figure 5 is the natural frequency test platform of the second process procedure of CNC machining process of near-net-shaped blade, and fixture 1# is designed and manufactured through fixture material and structural layout optimization [28], and fixture 2# is used as a comparative experiment, and the blade is the same blade, and the only variable is the machining fixture.…”

Section: Conditions and Methodsmentioning

confidence: 99%

Adaptive CNC Machining Process Optimization of Near- net- shaped Blade based on Machining Error data Flow Control

Wang

et al. 2021

Preprint

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Adaptive CNC machining process is one of the efficient processing solution for near- net- shaped blade, this study proposes an adaptive computer numerical control (CNC) machining process optimization scheme based on multi-process machining errors data flow control. The geometric and mechanical models of the multi-process adaptive CNC machining process are firstly constructed. The multi-process machining error data flow and the process system stiffness of near- net- shaped blade are then experimentally explored. The machining error flow collaborative control of the near- net- shaped blade multi-process CNC machining is finally realized by the adaptive CNC machining process under the premise of sufficient stiffness of the blade- fixture system. The results show that the dynamic displacement response of the blade multi-process CNC machining process is controlled within 0.007mm. The optimized adaptive CNC machining process based on the multi-process geometric machining error data flow control and the sufficient stiffness of blade- fixture system can realize the multi-process machining error control and high-precision machining of near- net- shaped blade. The process chain of the optimized adaptive CNC machining process is reduced by 87% compared with the low melting point alloy pouring process and 50% compared with adaptive CNC machining process of the twice on-machine measurement on the blade body.

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Section: Conditions and Methodsmentioning

confidence: 99%

Adaptive CNC Machining Process Optimization of Near- net- shaped Blade based on Machining Error data Flow Control

Wang

et al. 2021

Preprint

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“…Based on the 3-2-1 locating principle, Selvakumar et al 27 carried out fixture layout optimization by coupling GA and ANN to reduce workpiece deformation and machining forces. Wu et al 28 developed a computer-aided fixture system for engine blades and presented an optimization algorithm combining GA and FEA to determine the optimal positioning points of the fixture. Yang et al 29 constructed an optimization model for the positioning layout of sheet metal parts based on the N -2-1 locating principle and used the cuckoo search algorithm and FEA to obtain the optimal positions of N locators to minimize the deformation of sheet metal parts during machining.…”

Section: Introductionmentioning

confidence: 99%

Fixture layout optimization for car dashboard based on N-X locating principle

Liu

Sun

2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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A fixture is a crucial component in the car dashboard manufacturing system and is used to position and restrain the car dashboard under external forces. However, the conventional fixture is only suitable for a specific type of car dashboard, which has poor adaptability. To this end, a new flexible fixture based on the N- X locating principle is proposed in this paper to adapt to different types of car dashboards. In the presented fixture, N clamping elements clamp the dashboard edge, while X movable supporting elements support the area close to the tool along the machining path. Furthermore, the fixture layout is optimized by combining a genetic algorithm (GA) and finite element analysis (FEA). In the optimization program, a multi-objective optimization function is established to suppress the deformation of the car dashboard. FEA is applied to calculate the machining deformation. The optimal locations of the fixture elements are determined by GA. A case is implemented to validate the proposed method and the results show that the maximum deformation and average deformation are reduced by 53.8% and 40%, respectively, compared with the empirical method.

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“…Higher computational time and lack of accuracy were the disadvantages of their proposed method. Wu et al [19] used genetic algorithm to extract the optimal position of the clamping points in blade fixture. As they predict the deformation of the blade during the milling process via FEM, the higher computational load and limited time of evaluation were their proposed method disadvantages.…”

Section: Introductionmentioning

confidence: 99%

“…Rezaei Aderiani et al [20] extract the optimal fixture layout for compliant sheet metal assemblies using GA and FEM. The higher computational load and limited times of evaluation were their model limitations; same as Wu's model [19]. Wu et al [21] used the hybrid model of FEM and GA to extract the optimal layout of the fixture in end-milling process of the fan blade.…”

Section: Introductionmentioning

confidence: 99%

Optimization of fixture locating layout design using comprehensive optimized machine learning

Qazani

Parvaz

Pedrammehr

2022

Int J Adv Manuf Technol

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Fixtures are commonly employed in production as work holding devices that keep the workpiece immobilized while machined. The workpiece’s deformation, which affects machining precision, is greatly influenced by the positioning of fixture elements around the workpiece. By positioning the locators and clamps appropriately, the workpiece’s deformation might be decreased. Therefore, it is required to model the fixture–workpiece system’s complicated behavioral relationship. In this study, long short-term memory (LSTM), multilayer perception (MLP), and adaptive neuro-fuzzy inference system (ANFIS) are three machine-learning approaches employed to model the connection between locator and clamp positions and maximum workpiece deformation throughout end milling. The hyperparameters of the developed ANFIS, MLP, and LSTM are chosen using the evolutionary algorithms, including genetic algorithm (GA), particle swarm optimization (PSO), butterfly optimization algorithm (BOA), grey wolf optimization (GWO), and wolf optimization algorithm (WOA). Among developed methods, MLP optimized using BOA (BOA-MLP) reached the highest accuracy among all developed models in predicting the response surface. The developed model had a lower computational load than the final element model in calculating the response surface during the machining process. At the final step, the prementioned five evolutionary algorithms were implemented in the developed BOA-MLP to extract the optimal parameters of the fixture to decrease the deflection of the workpiece throughout the machining. The proposed method was modeled in MATLAB. The outcomes showed that the mentioned model was efficient enough compared with the previous method, such as optimized response surface methodology in the point view of 0.0441 μm lower workpiece deflection.

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Investigate on computer-aided fixture design and evaluation algorithm for near-net-shaped jet engine blade

Cited by 20 publications

References 14 publications

Adaptive CNC Machining Process Optimization of Near- net- shaped Blade based on Machining Error data Flow Control

Adaptive CNC Machining Process Optimization of Near- net- shaped Blade based on Machining Error data Flow Control

Fixture layout optimization for car dashboard based on N-X locating principle

Optimization of fixture locating layout design using comprehensive optimized machine learning

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