Machining fixture layout optimisation using genetic algorithm and artificial neural network

Selvakumar, S.; Arulshri, K. P.; Padmanaban, K.

doi:10.1504/ijmr.2013.053286

Cited by 21 publications

(11 citation statements)

References 28 publications

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“…Knowing the work piece deformations persuaded by loading in a fixture-work piece system is essential for ensuring worthy part production. [5] Appropriate methods for precisely calculating such deformations are significant for the design and implementation of fixtures. In this scenario, finite element modelling has been used widely by researchers and practitioners.…”

Section: Prediction Of Work Piece Deformationmentioning

confidence: 99%

Optimal Fixture Design for Drilling of Elastomer using DOE and FEM

Selvakumar¹,

Arunraja²,

Praveen³

2016

JAMES

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An elastomer is a polymer with the property of viscoelasticity generally having notably low Young's modulus and high yield strain compared with other materials. The elastomer which is used in our project is styrene-butadiene rubber. The drilling of elastomer work piece is not easy since it can easily deform. Hence the desired output cannot be obtained. By designing a proper fixture layout, the drilling of elastomer can be done accurately. The fixture design requires accurate positioning of locators and clamps to reduce the deformation of work piece during drilling. In this work six locators and three clamps are used. The primary design layout is done and then it is analyzed in ANSYS to find out the work piece deformation. The various possible design layouts are formed using L27 orthogonal array by MINITAB Software. Then all the 27 layouts are analyzed using ANSYS and the deformation values are found. The optimal layout is found using DOE -Taguchi method from MINITAB Software by giving the 27 layouts and the corresponding deformation values as inputs. From the obtained SN ratio graph, the optimal fixture layout is found for minimum deformation of the work piece.

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Section: Prediction Of Work Piece Deformationmentioning

confidence: 99%

Optimal Fixture Design for Drilling of Elastomer using DOE and FEM

Selvakumar¹,

Arunraja²,

Praveen³

2016

JAMES

View full text Add to dashboard Cite

show abstract

“…Besides, the solutions thus generated are highly sensitive to the quality of initial solution which represents input to the process of optimization. On the other hand, there have been some investigations which utilized genetic algorithms (GA) for optimization of fixture design solution [17][18][19][20][21]. In such cases the problem was rather simplified, disregarding the dynamic nature of forces and machining torques.…”

Section: Introductionmentioning

confidence: 99%

Computer-Aided Design and Optimization of Fixtures for Plastic Parts Machining

Vukelić¹,

Šimunović²,

Šarić³

et al. 2018

J. Prod. Eng.

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Reviewed and verified in this paper is a system which integrates functions of fixture design and optimization. The system was developed for machining of plastic parts. Due to specific characteristics of plastic parts, the system allows optimization of location of fixture elements for clamping and locating, using minimum deformation of plastic parts as the goal function. The paper present the basic steps of the methodology applied, reviews specific segments of the system, and illustrates its applicability on a study case featuring operations of drilling and milling of ABS parts.

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“…Selvakumar et al [ 10 ] used back propagation neural network to describe the function relationship of the position of the locators and clamps and the maximum workpiece deformation and combined ANN with DOE to optimize the machining fixture layout. Selvakumar et al [ 11 ] integrated GA with ANN to accomplish the optimal machining fixture layout. Lu and Zhao [ 12 ] built a back propagation neural network model to predict the deformation of the sheet metal workpiece under different fixture layouts and different fixture locator errors and applied genetic algorithm to the established ANN model to find the optimal position of the fourth fixture locator based on the “ N -2-1” locating principle.…”

Section: Introductionmentioning

confidence: 99%

Development of a Prediction Model Based on RBF Neural Network for Sheet Metal Fixture Locating Layout Design and Optimization

Wang

Yang

Kang

et al. 2016

Computational Intelligence and Neuroscience

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Fixture plays an important part in constraining excessive sheet metal part deformation at machining, assembly, and measuring stages during the whole manufacturing process. However, it is still a difficult and nontrivial task to design and optimize sheet metal fixture locating layout at present because there is always no direct and explicit expression describing sheet metal fixture locating layout and responding deformation. To that end, an RBF neural network prediction model is proposed in this paper to assist design and optimization of sheet metal fixture locating layout. The RBF neural network model is constructed by training data set selected by uniform sampling and finite element simulation analysis. Finally, a case study is conducted to verify the proposed method.

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Machining fixture layout optimisation using genetic algorithm and artificial neural network

Cited by 21 publications

References 28 publications

Optimal Fixture Design for Drilling of Elastomer using DOE and FEM

Optimal Fixture Design for Drilling of Elastomer using DOE and FEM

Computer-Aided Design and Optimization of Fixtures for Plastic Parts Machining

Development of a Prediction Model Based on RBF Neural Network for Sheet Metal Fixture Locating Layout Design and Optimization

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