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A 3D rigid assembly modeling technique is developed for stream of variation analysis (SOVA) in multi-station processes. An assembly process is modeled as a spatial indexed state transition dynamic system. The model takes into account product and process factors such as: part-to-fixture, part-to-part, and inter-station interactions, which represent the influences coming from both tooling errors and part errors. The incorporation of the virtual fixture concept (Huang et al., Proc. of 2006 ASME MSEC) and inter-station interaction leads to the generic, unified SOVA model formulation. An automatic model generation technique is also developed for surmounting difficulties in modeling based on first principles. It enhances the applicability in modeling complex assemblies. The developed SOVA methodology outperforms the current simulation based techniques in computation efficiency, not only in forward analysis of complex assembly systems (tolerance analysis, sensitivity analysis), but it is also more powerful in backward analysis (tolerance synthesis and dimensional fault diagnosis). The model is validated using industrial case studies and series of simulations conducted using standardized industrial software (3DCS Analyst). [ABSTRACT FROM AUTHOR]

A stream-of-variation analysis (SOVA) model for three-dimensional (3D) rigid-body assemblies in a single station is developed. Both product and process information, such as part and fixture locating errors, are integrated in the model. The model represents a linear relationship of the variations between key product characteristics and key control characteristics. The generic modeling procedure and framework are provided, which involve: (1) an assembly graph (AG) to represent the kinematical constraints among parts and fixtures, (2) an unified method to transform all constraints (mating interface and fixture locators etc.) into a 3-2-1 locating scheme, and (3) a 3D rigid model for variation flow in a single-station process. The generality of the model is achieved by formulating all these constraints with an unified generalized fixture model. Thus, the model is able to accommodate various types of assemblies and provides a building block for complex multistation assembly model, in which the interstation interactions are taken into account. The model has been verified by using Monte Carlo simulation and a standardized industrial software. It provides the basis for variation control through tolerance design analysis, synthesis, and diagnosis in manufacturing systems. [ABSTRACT FROM AUTHOR]

In a number of manufacturing processes—tooling installation, calibration, and maintenance--guarantee the precision of fixtures and play important roles toward the overall quality of products. Recently, a new type of measurement equipment called a ‘laser tracker’ was developed and utilized for assembly fixture calibration to shorten calibration time and improve the accuracy of the currently used theodolite systems. Though calibration of the assembly fixture is critical for product quality, as such, calibration time creates a significant burden for productivity of multistation assembly processes. In order to shorten calibration lead time, the number of necessary setups, determined by visibility analysis, needs" to be minimized. This paper presents a screen space transformation-bated visibility analysis that allows minimizing the number of setups. The screen space transformation is applied to transform the visibility problem from three- to two-dimensional space, thus, efficiently solving the visibility problem. A case study illustrates the procedure and verifies the validity of the proposed methodology. This methodology can be applied not only for manufacturing processes, such us in-line fixture calibration, but also toward analysis and optimization of AGVs, robot navigation systems, and building security. [ABSTRACT FROM AUTHOR]

Presents two techniques, quasi random numbers based on the Number Theoretic Method and sequential algorithm based on the Number Theoretic net, to calculate yield and to perform tolerance allocation. Importance of tolerancing in product and manufacturing process design; Demonstration of optimal tolerance design; Efficient use of algorithm to obtain global optimum, with considerable reduction in amount of calculation.

Dimensional control has a significant impact on overall product quality and performance of large and complex multistation assembly systems. To date, the identification of process- related faults that cause large variations of key product characteristics (KPCs) remains one of the most critical research topics in dimensional control. This paper proposes a new approach for multiple fault diagnosis in a multistation assembly process by integrating multivariate statistical analysis with engineering models. The proposed method is based on the following steps: (i) modeling of fault patterns obtained using state space representation of process and product information that explicitly represents the relation- ship between process-related error sources denoted by key control characteristics (KCCs) and KPCs, and (ii) orthogonal diagonalization of measurement data using principal component analysis (PCA) to project measurement data onto the axes of an affine space formed by the predetermined fault patterns. Orthogonal diagonalization allows estimating the statistical significance of the root cause of the identified fault. A case study of fault diagnosis for a multistation assembly process illustrates and validates the proposed methodology. [ABSTRACT FROM AUTHOR]