Traditional Statistical Process Control Methods Research Articles

Joint Multifractal and Lacunarity Analysis of Image Profiles for Manufacturing Quality Control

The modern manufacturing industry faces increasing demands to customize products according to personal needs, thereby leading to the proliferation of complex designs. To cope with design complexity, manufacturing systems are increasingly equipped with advanced sensing and imaging capabilities. However, traditional statistical process control methods are not concerned with the stream of in-process imaging data. Also, very little has been done to investigate nonlinearity, irregularity, and inhomogeneity in the image stream collected from manufacturing processes. This paper presents the joint multifractal and lacunarity analysis to characterize irregular and inhomogeneous patterns of image profiles, as well as detect the hidden dynamics in the manufacturing process. Experimental studies show that the proposed method not only effectively characterizes surface finishes for quality control of ultraprecision machining but also provides an effective model to link process parameters with fractal characteristics of in-process images acquired from additive manufacturing. This, in turn, will allow a swift response to processes changes and consequently reduce the number of defective products. The proposed multifractal method shows strong potentials to be applied for process monitoring and control in a variety of domains such as ultraprecision machining and additive manufacturing.

Wafer quality monitoring using spatial Dirichlet process based mixed-effect profile modeling scheme

The objective of this work is to develop a profile-based statistical process control scheme for efficaciously monitoring wafer thickness profiles with non-normality in an industrial wafer slicing process. This is an important research area because the geometric quality of semiconductor wafers in a slicing process has a direct impact on the functional integrity of semiconductor parts and the efficiency of the production. Since non-normality in profiles indicates the existence of inter-cluster variations (i.e., the profiles cannot be represented by a single mean profile with normally-distributed random noise), it deteriorates the effectiveness of many traditional statistical process control methods with normality assumption. To realize the objective of this work, a mixed-effect profile monitoring (MEPM) scheme is proposed. The MEPM scheme adaptively groups profile data into clusters and models the inter-cluster variations, consequently, enabling a robust statistical process control scheme for detecting deviant profile data. Capturing the clustering information of the profile data leads to a deep understanding and an accurate modeling of the spatial data. It is a significant improvement over the current practice of monitoring the geometric product quality by summary quality features (such as total thickness variation) or by profiles neglecting inter-cluster variations. In this paper, the MEPM scheme is tested for detecting the out-of-control wafers from a wafer slicing process. Based on wafer thickness profiles, the MEPM scheme outperforms other benchmark methods and identifies the deviant wafers with low average type II error (missed detection rate) as 0.039. This profile monitoring scheme is extensible to geometrical quality assurance for products in other processes.

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.

Study of Statistical Process Control for Multi Specification and Small Batch Production Based on Group Technology

For the multi specification and small batch productions, because of small batch and short manufacture cycle of product, it is difficulty to get enough sample data used for setup control chart to Statistical Process Control (SPC) of manufacture process quality by traditional SPC method. A new part code system based on OPTIZ code system is proposed by coding parts, manufacturing processes and qualities according to Group Technology (GT) based on similarity theory, so characteristics of parts, manufacturing processes and qualities are transformed to code, analyzed and judged by similarity theory to enlarge sample data, to realize SPC by control chart method. A SPC program for multi specification and small batch production based on group technology is developed by Visual Basic software and verified by actual example.

Designing a new model of distributed quality control for sub-assemble products based on the intelligent web information system

The quality control of sub-assemble products (SAP) in a distributed manufacturing shop (DMS) becomes crucial and complicated when the production of SAPs involves a variety of production technology. In this case, traditional statistical process control methods are not sufficient to control such manufacturing system. Here, we design an intelligent web information system, where quality data are collected from DMS and stored in the central database. The processes of manufacturing SAPs in DMS are then controlled by clustering homogenous SAPs using the quality control of SAP in DMS (QCSD) and process smoothness factor based SAP predefined clustering (PSFSPC) algorithms, respectively. A prototype system called an intelligent web information system quality control (IWIS-QC) has been developed to trace the quality profiles of SAPs. Finally, a case study has been presented to illustrate and validate the proposed approach.

A Sequential Bayesian Control Model for Influenza-Like Illnesses and Early Detection of Intentional Outbreaks

ABSTRACT Data pertaining to influenza and influenza-like illnesses (ILI) are being used in the United States and around the globe to assess evidence of influenza activity, whether it is the natural course of the flu or intentional release of a biological agent with flu-like symptoms. These data, used in surveillance, are unstable, serially correlated, and non-stationary. An important goal is to detect, as soon as possible, either emergence of an epidemic or release of biological agent whose symptoms may be initially classified as ILI. Statistical methodologies for analyzing these data are currently short of being able to capture all their important structural details and are generally deficient. Tools from statistical process control (SPC) are, on the face of it, ideally suited for the task since they address a problem of detecting sudden shift against a background of random variability. However, traditional SPC methods are generally deficient in assuming exact knowledge of the background prevalence and in relying on over simplified models. The objective of this article is to use a Bayesian model to find statistical and data-driven evidence for a surveillance problem based on the U.S. Sentinel ILI data. Bayesian statistical methodologies applied to SPC are very well suited for this setting of partial but imperfect information on the parameters describing these data. This article provides a control algorithm capable to act in detect-to-warn fashion on near-real-time data, to increase the ability to detect unusual surges in the prevalence of ILIs. We defined a model that uses sequential update methods to chart the discrepancy between the observed and predicted incidence of ILI.

The funnel experiment: The Markov‐based SPC approach

AbstractThe classical funnel experiment was used by Deming to promote the idea of statistical process control (SPC). The popular example illustrates that the implementation of simple feedback rules to stationary processes violates the independence assumption and prevents the implementation of conventional SPC. However, Deming did not indicate how to implement SPC in the presence of such feedback rules. This pedagogical gap is addressed here by introducing a simple feedback rule to the funnel example that results in a nonlinear process to which the traditional SPC methods cannot be applied. The proposed method of Markov‐based SPC, which is a simplified version of the context‐based SPC method, is shown to monitor the modified process well. Copyright © 2007 John Wiley & Sons, Ltd.

How Unusual Are the “Unusual Events” Detected by Control Chart Techniques in Healthcare Settings?

Statistical process control (SPC) charts have become widely implemented tools for quality monitoring and assurance in healthcare settings across the United States. SPC methods have been successfully used in industrial settings to track the quality of products manufactured by machines and to detect deviations from acceptable Levels of product quality. However, problems may arise when SPC methods are used to evaluate human behavior. Specifically, when human behavior is tracked over time, the data stream generated usually exhibits periodicity and gradualism with respect to behavioral changes over time. These tendencies can be quantified and are recognized in the statistical field as autocorrelation. When autocorrelation is present, conventional SPC methods too often identify events as "unusuaL" when they really should be understood as products of random fluctuation. This article discusses the concept of autocorrelation and demonstrates the negative impact of autocorrelation on traditional SPC methods, with a specific focus on the use of SPC charts to detect unusual events.

Methods for performance monitoring and diagnosis of multivariable model-based control systems

Methods for performance monitoring and diagnosis of multivariable closed loop systems have been proposed aiming at application to model predictive control systems for industrial processes. For performance monitoring, the well-established traditional statistical process control method is empolyed. To meet the underlying premise that the observed variable is univariate and statistically independent, a temporal and spatial decorrelation procedure for process variables has been suggested. For diagnosis of control performance deterioration, a method to estimate the model-error and disturbance signal has been devised. This method enables us to identify the cause of performance deterioration among the controller, process, and disturbance. The proposed methods were evaluated through numerical examples.

SPC in an automated manufacturing environment

Implementation of statistical process control (SPC) in an automated environment requires a number of issues to be addressed. Changes in sample data distribution and statistical properties such as independence will affect the use and interpretation of traditional SPC procedures; changed monitoring and adjustment techniques will influence the subsequent decision-making; at the same time, automation could facilitate implementation of SPC with other control techniques. In this paper, the potential of combining SPC with engineering process control methods is discussed. Comparative analysis between traditional SPC methods and SPC combined with feedback control is presented. Actual and simulated data are used to illustrate the procedure which, because of its self-tuning ability, could greatly reduce system readjustment while most of the advantages of traditional SPC are preserved at the same time.

Diagnosing for signal in multiple correlated processes

Recently, considerable attention has been given to the effect of data correlation on statistical process control (SPC). Use of traditional SPC methods when observations are correlated often leads to misleading conclusions for diagnosis of processes. A good solution to the problem of diagnosis for multiple correlated processes should be that it not only can be used in monitoring the processes, but also is able to diagnose the behavior of each process. In terms of the above meaning required,this problem has been opened for many years. In this paper,an attempt to solve the problem is made. A procedure is systemly reported by regression residual for multiple correlated processes when an singnal of out-of-control occurs and an example of implementatim of the procedure is given.

Statistical process control and the Taguchi method: a comparative evaluation

Most manufacturing processes do not produce completely identical characteristics for successive units of the same product. There are many different sources of manufacturing variability. Product design has a significant impact on manufacturing variability. The design flows of a particular product cannot be predicted, making it difficult to apply effectively traditional statistical process control (SPC) methods. In this article, a comparison of traditional SPC methods and Taguchi methods (TM) of quality management is presented. We show how the Taguchi method complements many of the features of SPC. Finally, we argue for adoption of TM for implementation into a total quality program.

Synthetic neural networks for process control

Synthetic neural networks offer great promise for process control. A performance comparison is drawn between traditional statistical process control methods and neural networks. Specifically, a series of simulation experiments in which back propagation networks are contrasted with control charts is described. The basis for comparison is average run length (both predicted and observed) and accuracy. The Monte Carlo simulations are derived from plausible production process data. Neural networks were found to perform reasonably well under most conditions.