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• W3033907584 abstract "Fixture layout depicts the arrangement of locators and clamps around the workpiece. The position of the locators and clamps is one of the major factors which cause the elastic deformation of the workpiece during machining. The identification of optimal fixture layout design is a feasible solution to ensure the end quality of the workpiece by minimizing the elastic deformation. In this study, the effect of fixture layout on the elastic deformation of the workpiece is analysed based on full factorial experiments using finite element method (FEM). The analytical results are adequate to develop empirical models using response surface methodology (RSM) and artificial neural network (ANN). The result shows that the ANN model has higher modelling capability than RSM. Further, the ANN model is integrated with real-coded genetic algorithm (GA) to find the optimal fixture layout with the consideration of stability constraint. The result of the presented technique is compared with the previously published results, and it shows the effectiveness of the proposed technique as a practical tool to solve fixture layout design problems." @default.
• W3033907584 created "2020-06-12" @default.
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• W3033907584 date "2020-01-01" @default.
• W3033907584 modified "2023-10-03" @default.
• W3033907584 title "Intelligent Fixture Layout Design for End Milling Process Using Artificial Neural Networks" @default.
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• W3033907584 doi "https://doi.org/10.1007/978-981-15-3631-1_20" @default.
• W3033907584 hasPublicationYear "2020" @default.
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