Jianjun Shi

State Space Modeling of Sheet Metal Assembly for Dimensional Control

In this paper, a state space modeling approach is developed for the dimensional control of sheet metal assembly processes. In this study, a 3-2-1 scheme is assumed for the sheet metal assembly. Several key concepts, such as tooling locating error, part accumulative error, and re-orientation error, are defined. The inherent relationships among these error components are developed. Those relationships finally lead to a state space model which describes the variation propagation throughout the assembly process. An observation equation is also developed to represent the relationship between the observation vector (the in-line OCMM measurement information) and the state vector (the part accumulative error). Potential usage of the developed model is discussed in the paper.

State space modeling of dimensional variation propagation in multistage machining process using differential motion vectors

In this paper, a state space model is developed to describe the dimensional variation propagation of multistage machining processes. A complicated machining system usually contains multiple stages. When the workpiece passes through multiple stages, machining errors at each stage will be accumulated and transformed onto the workpiece. Differential motion vector, a concept from the robotics field, is used in this model as the state vector to represent the geometric deviation of the workpiece. The deviation accumulation and transformation are quantitatively described by the state transition in the state space model. A systematic procedure that builds the model is presented and an experimental validation is also conducted. The validation result is satisfactory. This model has great potential to be applied to fault diagnosis and process design evaluation for complicated machining processes.

Fixture failure diagnosis for autobody assembly using pattern recognition

In this paper, a fault diagnostic method is proposed for autobody assembly fixtures. This method uses measurement data to detect and isolate dimensional faults of part caused by fixture. The proposed method includes a predetermined variation pattern model and a fault mapping procedure. The variation pattern model is based on CAD information about the fixture geometry and location of the measurement points. This fault mapping procedure combines Principal Component Analysis with pattern recognition approach. Simulations and one case study illustrate the proposed method.

Fault Diagnosis of Multistage Manufacturing Processes by Using State Space Approach

This paper presents a methodology for diagnostics of fixture failures in multistage manufacturing processes (MMP). The diagnostic methodology is based on the state-space model of the MMP process, which includes part fixturing layout geometry and sensor location. The state space model of the MMP characterizes the propagation of fixture fault variation along the production stream, and is used to generate a set of predetermined fault variation patterns. Fixture faults are then isolated by using mapping procedure that combines the Principal Component Analysis (PCA) with pattern recognition approach. The fault diagnosability conditions for three levels: (a) within single station, (b) between stations, and (c) for the overall process, are developed. The presented analysis integrates the state space model of the process and matrix perturbation theory to estimate the upper bound for isolationability of fault pattern vectors caused by correlated and uncorrelated noises. A case study illustrates the proposed method.

Feature-preserving data compression of stamping tonnage information using wavelets

Tonnage information is referred to as stamping force measurement in a complete forming cycle. Tonnage data contains rich information and features of stamping process failures. Due to its nonstationary nature and lack of physical engineering models, tonnage information cannot be effectively compressed using conventional data-compression techniques. This article presents a statistical method for “feature-preserving” data compression of tonnage information using wavelets. The technique provides more effcient data-compression results while maintaining key information and features for process monitoring and diagnosis. Detailed criteria, algorithms, and procedures are presented. A real case study is provided to illustrate the developed concepts and algorithms.

Quality control and improvement for multistage systems: A survey

A multistage system refers to a system consisting of multiple components, stations or stages required to finish the final product or service. Multistage systems are very common in practice and include a variety of modern manufacturing and service systems. In most cases, the quality of the final product or service produced by a multistage system is determined by complex interactions among multiple stages—the quality characteristics at one stage are not only influenced by local variations at that stage, but also by variations propagated from upstream stages. Multistage systems present significant challenges, yet also opportunities for quality engineering research. The purpose of this paper is to provide a brief survey of emerging methodologies for tackling various issues in quality control and improvement for multistage systems including modeling, analysis, monitoring, diagnosis, control, inspection and design optimization.

Active Balancing and Vibration Control of Rotating Machinery: A Survey

Vibration suppression of rotating machinery is an important engineering problem. In this paper, a review of the research work performed in real-time active balanc- ing and active vibration control for rotating machinery, as well as the research work on dynamic modeling and analy- sis techniques of rotor systems, is presented. The basic methodology and a brief assessment of major difficulties and future research needs are also provided.

The GLRT for statistical process control of autocorrelated processes

This paper presents an on-line Statistical Process Control (SPC) technique, based on a Generalized Likelihood Ratio Test (GLRT), for detecting and estimating mean shifts in autocorrelated processes that follow a normally distributed Autoregressive Integrated Moving Average (ARIMA) model. The GLRT is applied to the uncorrelated residuals of the appropriate time-series model. The performance of the GLRT is compared to two other commonly applied residual-based tests ‐ a Shewhart individuals chart and a CUSUM test. A wide range of ARIMA models are considered, with the conclusion that the best residual-based test to use depends on the particular ARIMA model used to describe the autocorrelation. For many models, the GLRT performance is far superior to either a CUSUM or Shewhart test, while for others the diAerence is negligible or the CUSUM test performs slightly better. Simple, intuitive guidelines are provided for determining which residual-based test to use. Additional advantages of the GLRT are that it directly provides estimates of the magnitude and time of occurrence of the mean shift, and can be used to distinguish diAerent types of faults, e.g., a sustained mean shift versus a temporary spike.

Diagnosis of Multiple Fixture Faults in Panel Assembly

This paper presents a modeling procedure and diagnostic algorithm for fixture related faults in panel assembly, From geometric information about the panel and fixture, a fixture fault model can be constructed off-line. Combining the fault model with inline panel dimensional measurements, the algorithm is capable of detecting and classifying multiple fixture faults. The algorithm, which relies heavily on the fault model, is based on least squares estimation. Consequently, the test is of relatively simple form and is easily implemented and analyzed, Experimental results of applying the algorithm to an autobody assemble process are provided.

Diagnosability Study of Multistage Manufacturing Processes Based on Linear Mixed-Effects Models

Automatic in-process data collection techniques have been widely used in complicated manufacturing processes in recent years. The huge amounts of product measurement data have created great opportunities for process monitoring and diagnosis. Given such product quality measurements, this article examines the diagnosability of the process faults in a multistage manufacturing process using a linear mixed-effects model. Fault diagnosability is defined in a general way that does not depend on specific diagnosis algorithms. The concept of a minimal diagnosable class is proposed to expose the “aliasing” structure among process faults in a partially diagnosable system. The algorithms and procedures needed to obtain the minimal diagnosable class and to evaluate the system-level diagnosability are presented. The methodology, which can be used for any general linear input–output system, is illustrated using a panel assembly process and an engine head machining process.