3D FREEFORM COMPONENT FIXTURE DESIGN OPTIMISATION USING A GENETIC ALGORITHM

1. 1. Pro-Engineer Create 3D component. .igs file format 2. ABAQUE Generate surface mesh point 3D FREEFORM COMPONENT FIXTURE DESIGN OPTIMISATION USING A GENETIC ALGORITHM .slp file format .txt mesh file If NO, modify mesh 3. Matlab Users select .slp file format as input information source for the component. Then load the mesh point from text file, which show all the possible clamping and locator point. Calculate best clamping position. Does the clamping and locator position fulfill all requirements? ABSTRACT This research details an application of genetic algorithms (GA) developed for the optimisation of fixture locator and clamper positioning for 3D freeform components. Based on the geometrical information of the workpiece, a genetic algorithm based approach is applied to determine the most statically stable fixture configuration from a large number of possible candidates. The preliminary implementation is introduced to demonstrate the ability of GA in automated fixture design. 3.1GA toolbox User input predefined GA parameter Conducts the GA search locators setting If YES Generate results in for form of a figure and text. Figure 1. Flow Chart of the System Operation Results Two cases were studied: (1) A rectangular box with a truncated corner (fig. 2) and (2) A turbine blade with locators and clampers on the aerofoil (fig. 3). There are general consistence of yielding solutions in similar configurations for 10 trials. The fixture optimisation using genetic algorithms has been proven to be effective at finding high quality solutions, which could be integrated into industry automated fixture design (AFD). It also demonstrated the robustness of GA. This will reduce the dependency on human designer expertise to produce high quality fixture designs even for complex components Application of GA The accuracy of positioning a workpiece on machine depends on the fixture that holds the workpiece. The process of application of GA in fixture design optimisation is illustrated in figure 1. The model of the component to be machined is created from or imported into Pro-Engineer. It is then converted into the render (.slp) and 2D/3D vector graphics format based on the Initial Graphics Exchange Specification (.igs) file format. The .igs file then loaded into ABAQUS to generate a surface mesh; the mesh points are extracted and saved in the form of a text file (.txt). By importing these data into Matlab environment, the optimal positions can be found by using GA. The applied genetic algorithm searches toward the optimal solution and illustrates a set of best possible locator and clamp configurations that allow engineers to make final decision. It significantly reduces design time and efforts. Figure 2. Example result of case 1 Key: Blue crosses = all possible locator and clamping position. Red squares = locators. Green crosses = possible clamping position. Red cross = best clamping position. Figure 3. Example result of case 2 K.Y. Yeung1, X. Chen21 University of Nottingham, Nottingham, NG7 2RD, UK2 University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK.