Process control of product quality

Statistical process control (SPC) and automatic process control (APC) are two important methods that have been used for improving product quality and process productivity. SPC is used for process monitoring, while APC is used for process adjustment. SPC reduces process variability by detecting and eliminating special causes of process variation, while APC reduces variability by adjusting the process to keep the product variability on target. Both the methods were initially thought to be in conflict with each other, but in recent years, many researchers have shown their interest in integrating SPC and APC techniques to reduce total variability of the process. They have found that the techniques used in these two methods are complementary rather than contradictory. A considerable amount of work has appeared in the literature about methods that combine SPC and APC techniques for the same process. In this paper, we will cover preliminaries on SPC and APC and outline the major techniques applied by both. Literature survey related to the issues facing their integration will be outlined. We will also summarise different strategies followed to achieve their integration. Possible future directions of research will be highlighted and our concluding remarks will be indicated.

Characterization of wheat-based biscuit cooking process by statistical process control techniques

Evaluating environmental performance using statistical process control techniques

Statistical concepts and methods have played a critical role in speeding the pace of industrial development over the last century. In return, industrial applications have provided statisticians with incredible opportunities for methodological research. The richness and variety of these applications have had a major influence on the development of statistics as a discipline; consider, for example, the extensive research in statistical process control (SPC) and changepoint detection, dating back to the pioneering work of Shewhart in the 1920s, and developments in automatic process control, design of experiments, sequential analysis, reliability, and so on. Recent efforts by manufacturers to adopt sustained quality and productivity improvement programs have generated a renewed interest in and appreciation for statistics in industry. In fact, fundamental statistical concepts such as understanding and managing variation form the backbone of popular quality management paradigms and practices. Many of the traditional SPC concepts and techniques grew in response to the manufacturing environments prevalent several decades ago. Current advanced manufacturing and high-technology industries, however, operate under a much more complex and diverse set of conditions. These changes have important implications for research directions in industrial statistics, not only in terms of identifying new problems and developing new methods, but also in reevaluating the paradigms that inspired earlier approaches. In this vignette we use applications from automotive and semiconductor manufacturing to illustrate various issues and to discuss future research needs and directions. The discussion is limited to a few selected topics and is inevitably slanted toward our own experiences.

As suggested by Box and Kramer [1] and others, it is possible to reduce both the special cause and common cause variations by applying Statistical Process Control (SPC) methods to monitor the output of an Automatic Process Control (APC) controlled process. In this paper, we develop an economic model for SPC monitoring of APC controlled processes. We also develop an economic loss-based criterion, the Average Quality Cost (AQC), to evaluate the performance of SPC charting methods. The AQC and the traditional average run length of three common SPC charts are investigated and compared.

Semiconductor lithography manufacturing presents a major challenge for the application of classical Statistical Process Control (SPC) methodologies due to the complex nature of this process. For example, difficulties can occur due to inadequate data sampling, nonnormal error distributions, equipment or process instability and nonstationary random errors. Incorrect use of classical SPC techniques can result in the incorrect interpretation of process stability which can have a drastic impact on productivity. Photolithography provides additional SPC challenges due to the inherent multivariable nature of the output variables that are being controlled. This paper examines appropriate SPC and monitoring techniques for stepper control of overlay performance using in-process measurement and analysis equipment to address these issues. The average run length of three charting techniques is compared to quantify the ability of each technique to detect process mean shifts. Shewart, Exponentially Weighted Moving-Average (EWMA) and Cumulative-Sum (CUSUM) charts are analyzed for a baseline process and mean shifts of 0.42, 0.85 and 1.25 standard deviations. These results illustrate the superior performance of a CUSUM chart over Shewart and EWMA charts. In addition, the Shewart chart with Western Electric rules produced false mean shift alarms for the baseline case. The EWMA is also observed to be sensitive to the selection of weighting factors. The effectiveness of plotting individual wafers is compared with plotting lot means. The plotting of individual wafers outperforms lot means in the determination of baseline shifts because of the larger population size of the individual charts.

Since solely using statistical process control (SPC) and engineering process control (EPC) cannot optimally control the manufacturing process, lots of studies have been devoted to the integrated use of SPC and EPC. The majority of these studies have reported that the integrated approach has better performance than that by using only SPC or EPC. Almost all these studies have assumed that the assignable causes of process disturbance can be identified and removed by SPC techniques. However, these techniques are typically time-consuming and thus make the search hard to implement in practice. In this paper, the EPC and neural network scheme were integrated in identifying the assignable causes of the underlying disturbance. For finding the appropriate setup of the networks' parameters, such as the number of hidden nodes and the suitable input variables, the all-possible-regression selection procedure is applied. For comparison, two SPC charts, Shewhart and cumulative sum (Cusum) charts were also developed for the same data sets. As the results reveal, the proposed approaches outperform the other methods and the shift of disturbance can be identified successfully.

An integrated approach to process control

It is not at all surprising that many persons avoid involvement in statistical process control. The statistical procedures developed in industry to monitor production processes are somewhat different from those employed in biostatistics and social science applications. Many find it difficult to adjust to these procedures and to apply statistical process control techniques to situations in health care settings. Not everyone involved in a QI process, however, needs to become versed in statistical process control techniques. At the Indiana University Medical Center several persons from different hospital departments have volunteered to form a cadre of internal statistical process control consultants. The group has come to be known by some as the statistical process control "think tank." No doubt a small group of individuals who are interested in statistics, quality control, and computers can be found in most hospitals. While it is necessary that the large majority of managers and supervisors in any health care organization are knowledgeable about process management and control charts at the concept level of understanding, it takes a relatively few to understand and use such control charts. As internal consultants, these few persons can provide technical assistance when such aid is needed.

Since the 1930s statistical methods applied to industrial process control have received much attention. Many techniques of statistical process control, such as control chart and process capability analysis, require knowledge of process standard deviation. Therefore, estimation of the process standard deviation plays an important role in statistical process control. In this article, we reviewed four widely used estimators of standard deviation and derived the efficiency of each estimator. Then, these four estimators of standard deviation were compared based on their efficiencies. From the comparative studies, we suggest that if the sample size is greater than or equal to 6, the second, third and fourth estimators may be used to estimate process standard deviation. However, if the sample size is less than or equal to 5, then the third and fourth estimators should be preferred.

The aim of this research is to expand a framework that integrates two important concepts: statistical process control (SPC) and engineering process control (EPC). Most of the literature researches on integrated SPC/EPC systems are focused into continuous process mainly with algorithmic SPC. The integrated SPC/EPC systems in batch process control have not received the same degree of attention. This paper is a first of its kind in integrated SPC/EPC systems that applied in batch process. The proposed SPC/EPC integration is performed continually in active SPC for the batch making advance and in run-to-run (RTR) control action between batches. As a validation step, the proposed approach is applied to an industrial batch alkyd polymerisation reactor. Through this case study application, process engineers at the company are now able to use a valuable decision making tool when the production process is affected by certain disruptions, with obvious consequences on product quality, productivity and competitiveness.

Assessing the evidence from the use of SPC in monitoring, predicting & improving software quality

Signal processing of vibration signals from rotating machinery has been an active research area for recent years. Especially, discrete wavelet transform (DWT) is considered as a powerful tool for feature extraction in detecting fault on rotating machinery. However, the number of retained DWT features can be still too large to be used for standard multivariate statistical process control (SPC) techniques although DWT significantly reduces the dimensionality of the data. Even though many feature-based SPC methods have been introduced to tackle this deficiency, most of methods require a parametric distributional assumption that restricts their feasibility to specific problem of process control and thus limits their applications. This study introduced new feature extraction technique to alleviate the high dimensionality problem of implementing multivariate SPC when the quality characteristic is a vibration signal from bearing system. A set of multiscale wavelet scalogram features was generated to reduce the dimensionality of data, and is combined with the bootstrapping technique as nonparametric density estimation to set up an upper control limit of control chart. Our example and numerical simulation of a bearing system demonstrated that the proposed method has satisfactory fault-discriminating ability without any distributional assumption.

Statistical process control (SPC) is widely used as a tool to control and improve process quality. In general, SPC has not found widespread application in automatic test equipment (ATE) field repair systems. The difficulty in generating test parameter metrics that can be used to efficiently monitor the repair process is a factor that has limited application of SPC to the field repair process. United States Air Force (USAF) Organizational Level (O-Level) and Intermediate Level (l-Level) repair facilities are field facilities and as such, they provide the first opportunity to flag a repair process problem. This paper will demonstrate an approach whereby SPC techniques can be applied to a field repair facility with a specific example provided by an automatic test system used for l-Level support.

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.