Wenzhen Huang

Mode-based Decomposition of Part Form Error by Discrete-Cosine-Transform with Implementation to Assembly and Stamping System with Compliant Parts

A discrete-cosine-transformation (DCT) based decomposition method is proposed for modeling part form error, which decomposes the error field into a series of independent error modes. Compression, which ensures a compact model, is achieved by correlation reduction or mode truncation based on good energy compaction property of DCT. The part error related to assembly process (rigid body modes) and part distortion during manufacturing (deformation modes) can be separated and identified. DCT has been proven to be equivalent to least square regression with 2D cosine-base for modeling of part variation pattern. Estimation of these parameters in the model has also been developed. The proposed method was applied to model and evaluate assembly and stamping errors at one of the US stamping plant.

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.

Fault Diagnosis Using an Enhanced Relevance Vector Machine (RVM) for Partially Diagnosable Multistation Assembly Processes

Dimensional integrity has a significant impact on the quality of the final products in multistation assembly processes. A large body of research work in fault diagnosis has been proposed to identify the root causes of the large dimensional variations on products. These methods are based on a linear relationship between the dimensional measurements of the products and the possible process errors, and assume that the number of measurements is greater than that of process errors. However, in practice, the number of measurements is often less than that of process errors due to economical considerations. This brings a substantial challenge to the fault diagnosis in multistation assembly processes since the problem becomes solving an underdetermined system. In order to tackle this challenge, a fault diagnosis methodology is proposed by integrating the state space model with the enhanced relevance vector machine (RVM) to identify the process faults through the sparse estimate of the variance change of the process errors. The results of case studies demonstrate that the proposed methodology can identify process faults successfully.

Statistical modal analysis for variation characterization and application in manufacturing quality control

A Statistical Modal Analysis (SMA) methodology is developed for geometric variation characterization, modeling, and applications in manufacturing quality monitoring and control. The SMA decomposes a variation (spatial) signal into modes, revealing the fingerprints engraved on the feature in manufacturing with a few truncated modes. A discrete cosine transformation approach is adopted for mode decomposition. Statistical methods are used for model estimation, mode truncation, and determining sample strategy. The emphasis is on implementation and application aspects, including quality monitoring, diagnosis, and process capability study in manufacturing. Case studies are conducted to demonstrate application examples in modeling, diagnosis, and process capability analysis.

High-dimensional process monitoring and change point detection using embedding distributions in reproducing kernel Hilbert space

High-dimensional process monitoring has become ubiquitous in many domains, which creates tremendous challenges for conventional process monitoring methods. This article proposes a novel Reproducing Kernel Hilbert Space (RKHS)-based control chart that can be applied to high-dimensional processes with sophisticated process distributions to detect a wide range of process changes beyond the ones that are detected by traditional statistical process control methods. Through extensive experiments on both simulated and real-world processes and various kinds of process change patterns, it is shown that the RKHS-based control chart leads to improved statistical stability, fault detection power, and robustness to non-normality as compared with existing methods such as T2 and MEWMA control charts.

Process Capability Analysis With GD&T Specifications

Geometric dimensioning & tolerancing (GD&T) and process capability indices (PCIs) play critical roles in quality assurance. Conventional PCIs, when used together with GD&T, strongly rely on certain assumptions (e.g. normality and regularity of specification region). GD&T requirements often involve interrelated tolerances, creating irregular tolerance regions. Violation of these assumptions misleads the results (18) and interpretation in applications. A non-conformity (NC) index is developed based on nonparametric distribution model and numerical assessment techniques. Kernel is used for probability density (pdf) estimation and Monte Carlo integration algorithm is adopted for NC analysis, i.e. integration of a pdf over a specification region. The method is validated by case study.Copyright