Geometric Variation Prediction for Automotive Aluminum Welded Assemblies

Assembly error prediction is one of the key problems in quality control. The objective of this paper is to develop the statistical error analysis model for assembling, to derive measures of controlling the geometric variations in assembly with multiple assembly stations, and to provide a statistical tolerance prediction/distribution toolkit integrated with CAD system for responding quickly to market opportunities with reduced manufacturing costs and improved quality. First the homogeneous transformation is used to describe the location and orientation of assembly features, parts and other related surfaces. The desired location and orientation, and the related fixturing configuration (including locator position and orientation) are automatically extracted from CAD models. The location and orientation errors are represented with differential transformations. Then the statistical error prediction model is formulated and the related algorithms are integrated with the CAD system so that the complex geometric information can be directly accessed. In the prediction model, the manufacturing process (joining) error, induced by heat deformation in welding, is taken into account. Finally case studies are presented to verify the prediction algorithms. The proposed model has following characteristics: 1) variety error elements of both design and process aspects are taken into account; 2) assembly error prediction and control can be dealt with for multiple assembly stations and multiple fixtures in each station; and 3) the technical approach is integrated with CAD system.