Systematic Modeling of Workpiece-Fixture Geometric Default and Compliance for the Prediction of Workpiece Machining Error

Qin, Guohua; Zhang, Weihong; Wu, Zhuxi; Wan, Min

doi:10.1115/1.2336260

Cited by 40 publications

(21 citation statements)

References 13 publications

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“…, N. At each iteration k, the position x k i is updated by a velocity v k + 1 i that depends on three factors: (1) current velocity v k i , (2) the weight difference vector (p k i À x k i ) and (3) the weight difference vector (p k g À x k i ). The set X is updated for the next iteration using equation (8) x k + 1…”

Section: Updating the Particle Position And Velocitymentioning

confidence: 99%

Fixture layout design based on two-stage method for sheet metal components

Xing

Wang

2012

Proceedings of the Institution of Mechanical Engineers, Part B:

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The optimal fixture scheme can effectively reduce the deformation of sheet metal parts in the normal direction of their main plane. The fixture layout design requires a great deal of finite element analysis, which limits efficiency and quality of fixture design. This article proposes a two-stage method to decrease the number of finite element analyses for fixture layout design. First, three locating points are generated by using particle swarm optimization based on the rigid model. Second, the other points are optimized by establishing a response surface modeling based on radial basis function. Finally, through the case of rear bracket plate, this work elaborates two-stage fixture locating scheme design process. The results show that the two-stage method has good simulation effect and higher prediction accuracy.

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Section: Updating the Particle Position And Velocitymentioning

confidence: 99%

Fixture layout design based on two-stage method for sheet metal components

Xing

Wang

2012

Proceedings of the Institution of Mechanical Engineers, Part B:

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show abstract

“…These work only predicted the displacement of the workpiece, did not consider the effect of the displacement of the workpiece on the machining feature, consequently, cannot be directly used to analyze the machining accuracy of the machining feature. Recent work [21,22] built the relationship model between the machining error and the geometric default (including machining datum), local deformation and entire deformation of the workpiece-fixture, these models can be only utilized to predict the machining error due to the workpiecefixture system.…”

Section: Introductionmentioning

confidence: 99%

An error control approach to tool path adjustment conforming to the deformation of thin-walled workpiece

Wan

Hua

Wang

et al. 2011

International Journal of Machine Tools and Manufacture

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“…The inappropriate layout of fixture elements is the reason for the machining error, which ranges from 20% to 60 %. 3 Hence, many researchers focused to develop methodologies for designing and optimizing the layout for fixture elements.…”

Section: Introductionmentioning

confidence: 99%

An integrated finite element method, response surface methodology, and evolutionary techniques for modeling and optimization of machining fixture layout for 3D hollow workpiece geometry

Sundararaman

Padmanaban

Sabareeswaran

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part C:

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Machining fixtures play inevitable role in manufacturing to ensure the machining accuracy and workpiece quality. The layout of fixture elements, clamping forces, and machining forces significantly affect the workpiece elastic deformation during machining. The clamping and machining forces are necessary to immobilize and machine the workpiece, respectively. Finding the appropriate layout of fixture elements is the other possible way to reduce the workpiece deformation, which in turn improves the machining accuracy. The finite element method interfaced with evolutionary techniques is normally used for fixture layout optimization. In the finite element method, the workpiece is discretized into a number of small elements and fixture elements are placed only on the nodes. Hence, evolutionary techniques are capable of searching the optimal fixture layout from those discrete nodal points than from the entire area on the locating and clamping face. To overcome these limitations, in this research paper, response surface methodology is employed to establish a quadratic model between the position of fixture elements and maximum workpiece deformation. This enables the optimization techniques to search for the optimal solution in the continuous domain of the solution space. Then, the real-coded genetic algorithm based discrete optimization, continuous optimization based on binary-coded genetic algorithm and particle swarm optimization are employed to optimize the developed quadratic model and their performances are compared. The result clearly shows that the integration of finite element method, response surface methodology with particle swarm optimization is better than the integration with genetic algorithm to optimize the machining fixture layout and also reduces the computational complexity and time to a greater extent.

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Systematic Modeling of Workpiece-Fixture Geometric Default and Compliance for the Prediction of Workpiece Machining Error

Cited by 40 publications

References 13 publications

Fixture layout design based on two-stage method for sheet metal components

Fixture layout design based on two-stage method for sheet metal components

An error control approach to tool path adjustment conforming to the deformation of thin-walled workpiece

An integrated finite element method, response surface methodology, and evolutionary techniques for modeling and optimization of machining fixture layout for 3D hollow workpiece geometry

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