A graph-based data structure for assembly dimensional variation control at a preliminary phase of product design

Dimensional quality control remains a challenge in assembly of complex products. Currently dimensional variation control research efforts include tolerance analysis and allocation, fixture layout design, assembly sequence planning, and others, without systematically viewing assembly as a proxy for a wide range of design decisions to produce final products with least cost, most productivity and best quality. A literature review in current assembly modelling methods shows that a unified data structure has yet to be developed at a preliminary design phase in order to design product and plan assembly process automatically. This paper serves to develop such a data structure, which captures heterogeneous product and assembly process information available at a preliminary design phase and unifies them in a data structure represented as a hierarchical graph. The graph-based data structure, on one side, facilitates automatic product design and assembly process planning at the preliminary design phase by utilising research results developed in graph theory, and on the other side, allows easier embedding the assembly model into computer aided design (CAD) software since they share the similar data structure. The presented graph-based data structure is illustrated by a sport utility vehicle (SUV) side frame assembly

Robust dimensional variation control of compliant assemblies through the application of sheet metal joining process sequencing

Imperfections are inherent in every manufactured part - when the hundreds of sheet metal components that form the automotive Body In White (BIW) are assembled together, significant deformation and variability are possible. Although early work by Takazawa (1980) showed that compliant components can absorb individual component variability when assembled, interactions between the components and successive operations complicates analysis of the assembly process and prediction of the assembled output. Therefore, improving vehicle dimensional quality requires more detailed knowledge of the assembly process and control of features critical to functionality and aesthetic appeal. In the automotive industry, these features include: uneven gaps and flushness between panels, high closure forces, and incorrect seal gaps leading to leaks and excessive noise. Despite significant research in the field of compliant assembly, there have not been sufficiently detailed studies regarding the joining sequence process. Further, existing works are based on a number of assumptions that limits the applicability of their results. This thesis addresses this gap by utilising the joining process sequence to control deformations and minimise dimensional variation during the assembly of complex non-ideal compliant components. In this work, a geometry class to represent complex compliant assemblies is presented; the interactions of process sequences and variations examined; the criteria for robust sequence selection established; and a method for the rapid identification of robust sequences is developed. In addressing the aim of this research a number of key findings were developed. A broad method of classifying the input variation of the components is presented. Using this basis, identifying when the joining process has a significant influence on final assembly dimensionality can be established. The pre-existing guidelines of fixed-to-free end were then further generalised for complex geometries, resulting in the approach of most-to-least rigid configuration, noting the importance of prior joining operations and the fixture boundary conditions. In determining the potential impact of the joining sequence, the need to consider the build-up of internal stresses while modelling the assembly process is highlighted. A novel method, which analyses the natural frequency shift of the structure between successive joins, is presented as a technique of calculating a robust joining sequence. This technique requires no knowledge of the part deformations, only the component geometry, fixture configuration and weld locations, and hence is more practical to industry. Experimental studies to validate the simulation-based work were then performed. Although sequence-based trends are identifiable in some of the extracted data patterns, the twist induced in the experimental structure was less significant when compared to the simulated results. This difference is a result of a number of factors which are then postulated and analysed. Further investigation of this effect would be beneficial to further validate the approach. To build on the work in this thesis, two notable directions in addition to further industrial validation have been identified. By assessing the functional impact of variation patterns in measurement data, functional variation sequence synthesis could be investigated; where the goal is to select a sequence that best controls these critical functional variations. Evolvable assembly systems that utilise the input work and holding forces to optimise the sequence operations and minimise potential spring-back of the component can also be considered. This strategy would negate the need for the additional measurement step for process feedback which currently hampers existing adaptive techniques. With between four and six thousand joins performed per vehicle, an estimated 300 billion joining operations are performed annually worldwide. With minimal knowledge available to industry regarding the impact of the joining process sequence, the results from this work have the potential to significantly improve quality in the automotive market