File Name: machining fixture locating and clamping position optimization using genetic algorithms .zip
Fixture layout design is concerned with immobilization of the workpiece engine mount bracket during machining such that the workpiece elastic deformation is reduced. The fixture holds the workpiece through the positioning of fixturing elements that causes the workpiece elastic deformation, in turn, leads to the form and dimensional errors and increased machining cost. The fixture layout has the major impact on the machining accuracy and is the function of the fixturing position. The position of the fixturing elements, key aspects, needed to be optimized to reduce the workpiece elastic deformation. The purpose of this study is to evaluate the optimized fixture layout for the machining of the engine mount bracket. In this research work, using the finite element method FEM , a model is developed in the MATLAB for the fixture-workpiece system so that the workpiece elastic deformation is determined. The artificial neural network ANN is used to develop an empirical model.
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Deformation of the workpiece may cause dimensional problems in machining. Supports and locators are used in order to reduce the error caused by elastic deformation of the workpiece. The optimization of support, locator and clamp locations is a critical problem to minimize the geometric error in workpiece machining.
Fixture plays a significant role in determining the sheet metal part SMP spatial position and restraining its excessive deformation in many manufacturing operations. However, it is still a difficult task to design and optimize SMP fixture locating layout at present because there exist multiple conflicting objectives and excessive computational cost of finite element analysis FEA during the optimization process. To this end, a new multiobjective optimization method for SMP fixture locating layout is proposed in this paper based on the support vector regression SVR surrogate model and the elitist nondominated sorting genetic algorithm NSGA-II. And the fixture locating layout is treated as design variables, while the overall deformation and maximum deformation of SMP under external forces are as the multiple objective functions. Second, two SVR prediction models corresponding to the multiple objectives are established by learning from the limited training samples and are integrated as the multiobjective optimization surrogate model. Finally, a multiobjective optimization for fixture locating layout of an aircraft fuselage skin case is conducted to illustrate and verify the proposed method.
Genetic algorithm based deformation control and clamping force optimisation of workpiece fixture system. To cite this article: K. Paulraj Genetic algorithm based deformation control and clamping force optimisation of workpiece fixture system, International Journal of Production Research, , , DOI: Siva Kumara and G. In re-engineering mass production industry, design and production of components are frequently changed according to the customer needs within a very short span of time. This leads to rising difficulty in maintaining the accuracy of every finished component. It also induces the long setup time of the cutting tools and machine tools affecting the production rate of the components.
An automated flexible fixture system for mass customisation. Illidge; G. The need for mass customisation is growing as customer demand for customised products increases. Mass customisation is the production of custom products at mass-produced rates. Current fixture technology does not possess the flexibility required for mass customisation. It was determined, through research, that if mass customisation is to be achieved, a fixture system is required that possesses the following attributes: it accommodates a large variety of part families and geometries; it has automated setups and setup changeovers; it has a semi-automated fixture design; and it provides adaptive feedback during machining processes. A fixture system, known as an automated flexible fixture system AFFS , was developed that met these requirements.
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The purpose of this paper is to propose a general model for locating and clamping workpieces of complex geometry with two skewed holes under multiple constraints. Numerous constraints related to application of the proposed model are discussed as prerequisite to design of fixture solution. Based on theoretical model, a fixture was designed and successfully tested in experimental investigation.
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.
In machining fixtures, minimizing workpiece deformation due to clamping and cutting forces is essential to maintain the machining accuracy. This can be achieved by selecting the optimal location of fixturing elements such as locators and clamps. Many researches in the past decades described more efficient algorithms for fixture layout optimization. In this paper, artificial neural networks ANN -based algorithm with design of experiments DOE is proposed to design an optimum fixture layout in order to reduce the maximum elastic deformation of the workpiece caused by the clamping and machining forces acting on the workpiece while machining. Finite element method FEM is used to find out the maximum deformation of the workpiece for various fixture layouts.
Fathianathan, M. April 2, September ; 7 3 : —
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