Study And Application On Tolerance Allocation Of Compliant Autobody Panel Multi-Station Assembly

The autobody which is the carrier of the whole car parts is an important part of a vehicle. It's weight and manufacturing costs account for about 40%-60% of the whole car. Usually an autobody is composed of hundreds of complicated sheet metal parts with complex assembly process. So the assembly variation is inevitable. And how to effectively control and reduce the body assembly variation, is one of the main problems the body design and manufacture facing.The accuracy of autobody mainly is the size precision, geometric precision and assembly precision of body parts. Body parts mainly composed of thin stampings mostly have large flexibility. The assembly variation analysis and tolerance allocation have a great impact on the assembly quality. Existing assembly variation analysis and tolerance allocation researches are not suitable for flexible sheet metal parts. Therefore, the search for tolerance allocation methods of flexible sheet metal body parts is of great significance for controlling assembly variation.Based on the deviation analysis of flexible sheets in a single assembly station, by reviewing and developing the previous studies, a flexible multi-station assembly variation transfer model is established. Combined with the characteristics of flexible body assembly, the tolerance allocation of flexible body panels based on multi-objective optimization is taken. The main work is as follows:(1) Flexible sheet metal assembly variation modeling and analysisFlexible sheet metal parts assembly variation modeling and analysis is taken. The positioning and assembly processes of flexible sheet metal parts are studied. Studies the influence coefficient method which builds a linear relationship between the input and the output variation with the sensitivity coefficient matrixes. With the finite element method and the influence coefficient method, considering the parts deviation, fixture deviation and gun deviation, a flexible sheet metal assembly variation model is built. A simple sheet metal parts assembly deviation analysis is taken at last. (2) Flexible sheet metal multi-station assembly variation transfer modelingBased on the previous studies, with the finite element method, influence coefficient method and the state space method, a multi-station assembly variation transfer model is established. The model builds a linear relationship between the input and the output variation, considering the deviation of parts, fixture and guns. Through the analysis of multi-station assembly deviation, the deviation analysis of the measuring points at different stations is obtained. The integrated use of multi-station assembly variation transfer model and Monte Carlo method analysis can greatly reduce the computational load and working hours, comparing with direct simulation using the finite element method.(3) Study and application of cost-driven tolerance allocationThe multi-objective optimization model for tolerance allocation of the overall flexible parts multi-station assembly system is Established. Considering the product and process tolerances, using the traditional multi-objective optimization methods (the upper boundary method) and multi-level optimization strategy, multi-objective optimization function is solved. Cost-driven tolerance allocation is studied and applicated at the station level. At the case of with and without considering the deviation of tools respectively, the allocated tollerance of all parts of the station, sub-components and final assembly are compared.(4) Multi-objective optimization of tolerance allocation based on fixture selectionAn optimal design algorithm of fixture layout based on sensitivity index is established. A multi-objective optimization method of tolerance allocation based on fixture selection is proposed. Study and application of multi-objective optimization of product and process tolerance allocation is carried out on the system level, considering the assembly quality, cost and robustness of the system. Optimal selection of different fixture configuration is done by comparing the robustness. And the robustness, allocated tolerances and the minimum cost between different fixture configurations are compared.