Development of a new model for the fixture design and clamping optimization

Workpiece deformation must be controlled in the manufacturing process and other engineering application. Fixture configuration (position), clamping force and temperature are main aspects that influence the degree and distribution of Workpiece deformation. This paper takes large optical glass as an example, develop a new multiple kernel learning method to discuss the optimal fixture design. The proposed method uses two layers regressions to group and order the data sources by the weights of the kernels and the factors of the layers. Since that, the influences of the clamps and the temperature can be evaluated by grouping them into different layers. Then, based on the proposed model, the optimal magnitude and positions of clamping forces can be obtained. The experiments show is effective for the optical element clamping optimization analysis.