A design method for a lightweight unmanned sightseeing vehicle frame was proposed based on multi-condition and multi-objective optimization to improve the vehicle’s range and reduce its production cost. First, a finite element model of the frame is established, and its static and dynamic characteristics are analyzed. Then, the wall thickness of the middle beam of the frame was selected as the design variable, and its sensitivity was analyzed. Sample points were generated from the design variables using the Latin hypercube sampling method, and the corresponding response values of the sample points were calculated. A Kriging approximation model was established using the sample points and response values and replaced the actual model for optimization. Finally, a multi-condition and multi-objective optimization mathematical model of the frame was established with the minimum mass, maximum first-order natural frequency, and the minimum stresses under full load bending and torsion conditions as the objectives. The multi-objective genetic algorithm was used for the lightweight design by comparing the fuzzy matter element and analytic hierarchy process methods to select the optimal design and to verify the rationality of the final design scheme. The results show that this method results in an optimized frame meeting the strength requirements under various working conditions and reducing the frame mass by 5.4%.
An automatic all-around three-dimensional shape measurement system is introduced. Striped patterns from an optical modulator are projected on the surface of the object. By rotating the precision work-table, the deformed patterns were recorded by a CCD camera from different angles and stored in a computer, and the 3D coordinates of the surface of the object were calculated according to geometric relations. Thus from an arbitrary direction, 2D views and 3D solid models of the measured object may be constructed conveniently. After a series of image processing, the sizes of object may be measured. With this system, it is possible to capture the surface topography far away from the object. The error influence factors of the system and image processing are analyzed. Experiments show the measurement process and the effect of the measurement.
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