Zhenyu Kong

Stream-of-Variation (SOVA) Modeling II: A Generic 3D Variation Model for Rigid Body Assembly in Multistation Assembly Processes

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).

Stream-of-Variation Modeling—Part I: A Generic Three-Dimensional Variation Model for Rigid-Body Assembly in Single Station Assembly Processes

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.

Multiple Fault Diagnosis Method in Multistation Assembly Processes Using Orthogonal Diagonalization Analysis

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 integrdting 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 relationship 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.

Stream-of-Variation Modeling I: A Generic 3D Variation Model for Rigid Body Assembly in Single Station Assembly Processes

A stream-of variation analysis (SOVA) model for 3D rigid body assemblies in 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 (KPCs) and Key Control Characteristics (KCCs). The generic modeling procedure and framework are provided, which involves: (1) an assembly graph (AG) to represent the kinematical constraints among parts and fixtures; (2) a 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. The generality of the model is achieved by formulating all these constraints with a unified generalized fixture model. Thus, the new model accommodates various types of assemblies. This model provides a building block for complex multi station assembly model, in which the inter-station interactions are taken into account. The model has been verified by using Monte Carlo (MC) simulation and a standardized industrial software. It provides the basis for variation control through tolerance design analysis, synthesis and diagnosis in manufacturing systems.Copyright

The analysis of feature-based measurement error in coordinate metrology

Coordinate measurement systems (CMSs) dominate the dimensional control and diagnostics of various manufacturing processes. However, CMSs have inherent errors caused by the lack of a tracing ability for some of the measured part features. This is important for product inspection and process variation reduction in a number of automated manufacturing systems, such as for example the automotive body assembly process. The lack of a feature tracing ability means that instead of measuring a given feature, the CMS may actually measure the area around the selected feature. In this paper, a principle for the part feature tracing ability and the resultant feature-based measurement error analysis are developed to estimate the aforementioned deficiencies in the CMSs. The impact of feature type and part(s) positional variation on the feature-based measurement error is explored. The proposed approach is applicable to both contact and non-contact CMSs including both mechanical and optical coordinate measuring machines An analysis of the error for different measurement algorithms is presented. We show that the developed feature-based measurement error can have a significant impact on the measurement accuracy and hence on process control and the diagnostic algorithms currently used in manufacturing. A feature-based error map and error compensation approach are also developed and presented. Simulations, experimental results and two industrial case studies illustrate the proposed method.

Fixture workspace synthesis for reconfigurable assembly using procrustes-based pairwise configuration optimization

In recent years, reconfigurability of assembly systems, which enables a family of products to be produced on a single production line, is becoming of paramount importance in addressing increasing diversification of the market. One of the key issues in reconfigurable assembly systems is to identify the fixture workspace for a family of parts based on the individual configuration of the fixture-locating layout for each part. Aimed at the problems existing in the current research in this area, this paper presents an approach that allocates the best superposition of locating layouts for multiple parts to be assembled on a single reconfigurable fixture. The presented methodology is based on (1) analytical Procrustes analysis, to rapidly eliminate unlikely sets of solutions, and then (2) pairwise optimization of fixture configurations for a given part family. The proposed method is able to provide more accurate solutions for various Engineering Requirement Functions (ERFs) used in industrial practice with much less computational complexity than the existing method presented in the literature. Comprehensive case studies illustrate the proposed methodology as well as its advantages over the existing method.

Multiple Fault Diagnosis Method in Multi-Station Assembly Processes Using State Space Model and Orthogonal Diagonalization Analysis

Dimensional control has a significant impact on the overall product quality and performance in large and complex multi-station assembly systems. From measurement data, the way to identify root causes for large variation of Key Product Characteristics (KPCs) is one of the most critical research topics in dimensional control. This paper proposes a new approach for multiple fault diagnosis in a multi-station assembly process by integrating multivariate statistical analysis with engineering model. Based on product/process information, by using the state space model, a set of fault patterns for multi-station assembly process are developed, which explicitly represent the relationship between the error sources and KPCs. The vectors of these patterns form an affine system. Afterwards, the Principal Component Analysis (PCA) is applied to conduct orthogonal diagonalization of the measurement data. Thus, the measurement data can be easily projected to the axes of the affine system. Whereby, the significance of each fault pattern shall be estimated accurately. Finally, a few case studies are also provided to validate the proposed methodology.Copyright