SPC in an automated manufacturing environment

The implementation and use of statistical process control in a chemicals and plastics firm are examined. The essential external factors are focused upon, which are associated with the practical implementation of statistical process control. The important components of a Statistical Process Control process optimisation study are discussed in the context of their achievements at a manufacturing facility. Benefits achieved from a successful implementation of statistical process control are also discussed.

The 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.

A systematic literature review is a rigorous method of assessing and combining research results compared to the usual literature review, which consists of annotated bibliographies, a systematic review of the existing literature regarding specific research questions on a topic of interest. Statistical Process Control (SPC) is a well-established technique in a context that has recently been used in software production. Software production is not like manufacturing because it requires more human resources and machines, resulting in an item's one-time production. It is therefore essential to assess what SPC works in a software context. Thus, this consideration has motivated us to define and conduct an evaluation review to assess whether SPC is used effectively and genuinely Quality Control Cycle (QCC) in Indonesia's various industries. According to a systematic literature review process, an established protocol was revised and refined by the authors. This journal collection of journals has been used in various industries in Indonesia. We report our preliminary considerations and results in a systematic review on the SPC and find 30 journals collected as references for readers in making research journals related to SPC. Our products and first impressions are favorable. This journal collection can also make it easier for researchers to get the desired journal references.

Distributed statistical process control in work cells

Process control of product quality

The development of a PC-based functional test platform that incorporates Statistical Process Control (SPC) and a failure analysis database is described. With Commercial Off-The-Shelf(COTS) tools available it is possible in a low volume high-mix environment to easily obtain basic statistical information on each test, develop an easily accessible database of information on failures, causes of failures and troubleshooting activities and increase test efficiency with minimal test development costs. A common test platform has been developed that can be configured to test a wide variety of sub-assemblies from digital controller cards to radio frequency (RF) modules. The platform consists of a PC with a multi-function data acquisition card and a General Purpose Interface Bus (GPIB) card for control of test instruments. Applications are linked with Dynamic Data Exchange (DDE) and the PCs are connected to the company's Local Area Network (LAN) so that test data can be accessed from throughout the plant. A key point is the implementation of SPC with this test platform. Statistics include process capability indices C/sub p/ and C/sub pk/ and failure rate. Test data is analyzed and used to make process improvements. Examples are presented. Since the entire system has been designed and built around established COTS software and hardware, additional capabilities can be easily added in the future. Some planned enhancements are described.

To meet the ever-increasing demand of the rapidly-growing semiconductor manufacturing industry it is critical to have a comprehensive methodology integrating techniques for process optimization real-time monitoring and adaptive process control. To this end we have accomplished an integrated knowledge-based approach combining latest expert system technology machine learning method and traditional statistical process control (SPC) techniques. This knowledge-based approach is advantageous in that it makes it possible for the task of process optimization and adaptive control to be performed consistently and predictably. Furthermore this approach can be used to construct high-level and qualitative description of processes and thus make the process behavior easy to monitor predict and control. Two software packages RIST (Rule Induction and Statistical Testing) and KARSM (Knowledge Acquisition from Response Surface Methodology) have been developed and incorporated with two commercially available packages G2 (real-time expert system) and ULTRAMAX (a tool for sequential process optimization).© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Traditional statistical process control (SPC) techniques of control charting are not applicable in many process industries because data from these facilities are autocorrelated. Therefore the reduction in process variability obtained through the use of SPC techniques has not been realized in process industries. Techniques are needed to serve the same functions as SPC control charts, which are to identify shifts in correlated parameters. Neural networks are a potential tool for identifying shifts in correlated process parameters, as data independence is not an assumption of neural network theory. In this research, a back-propagation neural network is utilized to identify shifts in process parameter values from AR (1) time series models with varying values of the autocorrelation coefficient φ. To find the appropriate number of input nodes for use in a neural network model, the all-possible-regression selection procedure is applied. In addition, time series residual control charts are also developed for the data sets for comparison. As the results reveal, networks were successful at separating data that were shifted one, two and three standard deviations from non-shifted data for generated process data. The SPC control charts were not able to identify the same process shifts. In the other words, the neural networks can be used to identify shifts in process parameters. Therefore, it is allowing improved control in manufacturing processes that generate correlated process data.

Data mining model-based control charts for multivariate and autocorrelated processes

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.

A production process composed of tens or even hundreds of subprocesses is a common phenomenon in industry. Each of the subprocesses contributes to various aspects of product quality. Ideally, a control chart can be set up on every subprocess to guarantee the quality of the final product. This is not practical however, because of limited human and economic resources, and the management has to decide which subprocesses are to be given higher priorities. In this paper, for the purpose of prioritizing processes in complicated production systems for implementing statistical process control (SPC) schemes, preliminary selection based on statistical and technical criticality of processes is discussed. An analytic hierarchy process approach based on pair-wise comparisons between several factors in deciding the relative criticality of the processes in a hierarchy structure is then studied. The approach can be used in management decision making in planning for SPC implementation. A case study is presented to illustrate the methodology.

This book is great because it integrates process adjustment techniques based on Engineering Process Control (EPC) methods with traditional Statistical Process Control (SPC) very well, adequately ba...

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

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.

High-dimensional and high-frequency data streams (HHDS) have become widely available with the prevalence of data-acquisition technologies. Because HHDS have the characteristics of high dimensionality and temporal correlation, traditional statistical process control (SPC) techniques cannot be used directly to address the problem of monitoring HHDS. Most extant monitoring methods commonly assume that the process observations are independent or can be described by some parametric models and face the challenge posed by high-dimensional data. Little research has been conducted on the robust monitoring of HHDS that can simultaneously accommodate high dimensions and high frequencies. Therefore, in this paper, we propose a novel nonparametric approach for monitoring HHDS, based on data decorrelation, dimension reduction, and SPC. Specifically, process observations are first sequentially decorrelated and standardized using Cholesky decomposition, and then the k -nearest neighbors classification algorithm is applied to transform uncorrelated high-dimensional data into one-dimensional data. Finally, the traditional cumulative-sum procedure is used for online monitoring, based on an empirical log-likelihood ratio test. Numerical studies have shown that our monitoring scheme is sufficiently reliable and efficient for the online monitoring of HHDS. An illustrative example about the Dow Jones 30 industrial stock prices is presented to further validate the proposed approach.