Optimization of the number and positions of fixture locators for curved thin-walled parts by whale optimization algorithm

Abstract

Optimizing the fixture layout of the locating element is an important method to reduce the clamping deformation of thin-walled parts. A method for optimizing the fixture layout based on whale optimization algorithm is proposed in this paper, the number and positions of the fixtures for curved thin-walled parts are optimized. Firstly, the multi-point flexible locating tooling for curved thin-walled parts is developed based on the multi-point support technology. Then the strain energy is used to describe the deformation of the curved thin-walled parts in all directions, and an optimization model that takes the position of the locating element as a decision variable and minimum strain energy as the goal is established. Combined with the whale optimization algorithm and the parameterized finite element analysis, the optimal design of the number and positions of fixture locators for curved thin-walled parts are realized. Finally, the effectiveness of the proposed method is validated by the aircraft skin locating layout optimization, and a multi-point flexible locating and deformation measurement platform is constructed to verify the results of finite element calculations.

Evolutionary Design of Machining Fixture Layout for Thin-Walled Structure

“…From the aforementioned results, we can see that the maximum deformation is lower than 0.12 mm. According to the result of these researches [1,4,25,[40][41][42][43][44], we can learn that the maximum deformation of the workpiece with the aluminum materials is in the range of 0.02 to 2 mm when the eternal load is in the range of 200 N to 1500 N. For example, [1] uses sheet metal with a thickness of 3 mm and similar material properties to this article to research the fixture layout. Finally, its maximum deformation is in the range of 0.7 to 1.9 mm.…”

“…in-wall structures have been designed for a wide range of applications in the eld of aerospace engineering and automotive industry, due to the fact that thin-walled structures exhibit high performances such as light-weight, high speci c strength, and high structural e ciency. Although the thinwalled structures have those excellent performances, they also have some critical liminations such as low structural sti ness and large de ection during the manufacturing process [1][2][3]. Such limitations not only cause a big challenge in its fabrication but also in uence product quality.…”

Application of optimized convolutional neural network to fixture layout in automotive parts

“…Modeling the force of clamps and positional fixtures can be challenging. Some publications focus on developing higher fidelity models by using response surface methodology (RSM) [14], [18], surrogate modeling with ANN [18], [17] or strain energy analysis [19]. Other publications choose a more complex workpiece and use simple static analysis to retrieve displacements and forces.…”

Optimization of fixture locating layout design using comprehensive optimized machine learning

“…Yu and Wang [28] used machine-learning method to extract the model that can imitate the same process using RSM and mathematical optimization. Li et al [29] used whale optimization algorithm to reduce the clamping deformation of the curved thin-walled parts. Feng et al [30] proposed the recent machine learning-based prediction model of the workpiece deformation calculator using extreme gradient boosting (XGBoost) method.…”

“…Based on reviewed articles, the previous works can be divided into two groups. The first group of the work is established based on the combination of optimization method (evolutionary, global, mathematic) with FEM [10,11,13,16,[19][20][21][22][23][24][25][26][27]29]. These studies are accurate in calculation of the workpiece deformation during the machining operation.…”