Fuzzy Logic and SPC

Functional data characterize the quality or reliability performance of many manufacturing processes. As can be seen in the literature, such data are informative in process monitoring and control for nanomachining, for ultra-thin semiconductor fabrication, and for antenna, steel-stamping, or chemical manufacturing processes. Many functional data in manufacturing applications show complicated transient patterns such as peaks representing important process characteristics. Wavelet transforms are popular in the computing and engineering fields for handling these types of complicated functional data. This article develops a wavelet-based statistical process control (SPC) procedure for detecting ‘out-of-control’ events that signal process abnormalities. Simulation-based evaluations of average run length indicate that our new procedure performs better than extensions from well-known methods in the literature. More importantly, unlike recent SPC research on linear profile data for monitoring global changes of data patterns, our methods focus on local changes in data segments. In contrast to most of the SPC procedures developed for detecting a known type of process change, our idea of updating the selected parameters adaptively can handle many types of process changes whether known or unknown. Finally, due to the data-reduction efficiency of wavelet thresholding, our procedure can deal effectively with large data sets.

Co-organized by the Intermountain, Long Island, Silicon Valley and Upper Midwest Chapters and the Students & Young Professionals Committee Technical Presentations: 1. Professor Ron Lasky, PhD, PE, Dartmouth College and Senior Technologist, Indium Corporation Title: Statistical Process Control 101 Abstract:. The presentation will cover statistical process control (SPC). SPC is an important tool in controlling SMT assembly processes, especially stencil printing. It will start with a discussion of the 6 Ms of variation and then delineate common cause and special cause variation. The difference between precision and accuracy will then be clarified. After discussing the precision tolerance ratio, the technique to perform a Gage R&R (Repeatability and Reproducibility) analysis will be presented. Process control charts, and their development will then be discussed including the Shewhart Rules. Several sets of SPC Xbar-R and C Chart data will then be plotted and analyzed with commercial statistical software. The webinar will conclude with a discussion and application of the process capability indices Cp and Cpx. SPC 101 Topics Covered Include: The 6 Ms of variation Special Cause and Common Variation Measuring as a Process Precision and Accuracy Precision/Tolerance Ratio Gage R&R (Repeatability & Reproducibility) Analysis Control Charts- Shewhart Rules Xbar R Charts Stencil Printing Example C Charts Pareto Charts Cp and Cpk with Examples Commercial statistical software will be used throughout 2. Chrys Shea, President, Shea Engineering Services Title: The Use of SPC in Relation to Stencil Printing Abstract: The presentation will review the typical variables monitored under SPC on typical SMT assembly lines, with special focus on the solder paste printing process. The print process is arguably the most important to monitor because it is the most easily affected by both Special and Common causes of variation. The presentation will review a fishbone diagram of print process factors as well as a discussion of troubleshooting print problems and specific defect modes. Biographies: Ron Lasky Ron Lasky is currently an Instructional Professor at Dartmouth College, a Senior Technologist at Indium Corporation and a Consultant with ITM. Prior to this, he had over thirty years experience in electronic and optoelectronic packaging and assembly at IBM, Universal Instruments and Cookson Electronics. He has authored or edited 7 books on science, electronics, and optoelectronics and numerous technical papers and authored articles for Scientific American. He holds a number of patent disclosures and is the developer of several new concepts in SMT processing software relating to cost estimating, line balancing and process optimization. He is the co-creator of engineering certification exams in electronic assembly and the Director of Dartmouth"™s Lean Six Sigma Program. He was awarded the SMTA Founder's Award in 2003 and Member of Technical Distinction Award in 2021. He has four degrees, including a Ph.D. from Cornell University in Materials Science. Ron is a New York State licensed professional engineer and a Lean Six Sigma Master Black Belt. Chrys Shea is the President of Shea Engineering Services, a consulting firm that produces technical studies and communications tools for the electronics manufacturing industry. Chrys is an SMTA "Member of Technical Distinction," an SMTA "Distinguished Speaker" and four-time winner of the SMTAI "Rich Freiberger Best of Conference" award for her research on solder paste printing. She earned a Bachelor's Degree in Mechanical Engineering and a Master's Degree in Manufacturing Engineering. Chrys' background is in SMT process engineering. She is a member of the SMTA International Technical Committee and the IPC 7526A Stencil Cleaning Handbook Committee. She held various production engineering, new product introduction, research and management positions before launching her own business in 2008. Chrys Shea Files Available to Download: Recorded Presentation (On-Demand) Presentation Slides Part 1 (PDF) Presentation Slides Part 2 (PDF) Additional Resources (PDF)

Introduction of statistical process control in the setting of a small blood centre was tested, both on the regular red blood cell production and specifically to test if a difference was seen in the quality of the platelets produced, when a change was made from a relatively large inexperienced occasional component manufacturing staff to an experienced regular manufacturing staff. Production of blood products is a semi-automated process in which the manual steps may be difficult to control. This study was performed in an ongoing effort to improve the control and optimize the quality of the blood components produced and gives an example of how to meet EU legislative requirements in a small-scale production centre. Data included quality control measurements in 363 units of red blood cells, 79 units of platelets produced by an occasional staff with 11 technologists and 79 units of platelets produced by an experienced staff with four technologists. We applied statistical process control to examine if time series of quality control values were in statistical control. Leucocyte count in red blood cells was out of statistical control. Platelet concentration and volume of the platelets produced by the occasional staff were out of control, which was not the case with the experienced staff. Introduction of control charts to a small blood centre has elucidated the difficulties in controlling the blood production and shown the advantage of using experienced regular component manufacturing staff.

This paper reports on the findings of a questionnaire survey into the use of statistical process control (SPC) in a sample of United Kingdom automotive suppliers. All the 76 respondents had attended the Ford‐approved three‐day course on SPC held at the University of Manchester Institute of Science and Technology (UMIST). The survey investigated the following broad issues of SPC: motivation, implementation, application, suppliers and customers, and benefits. A number of findings surprised the authors; these included the number of organizations who had not appointed a SPC facilitator, organizations experiencing difficulties in deciding what type of control charts to use, control charts being ‘filled‐in’ by people not directly responsible for control of a process, charts being displayed away from work areas and the frankness of some suppliers in declaring that the vast majority of their manufacturing processes are not in a state of statistical control. If there is an overall message arising from the findings it is that the introduction and application of SPC should not be overcomplicated.

6579 Background: Collecting electronic patient reported outcomes (ePROs) reduces the burden of cancer and prevents acute care hospitalizations. However, broad implementation in the routine care setting remains challenging, partly because the optimal implementation strategies – actions that are most likely to encourage adoption and use – are not known. Since 2019, the SIMPRO Consortium has deployed an ePRO-based symptom management program (eSyM) across 6 community-based cancer centers. Methods: We used statistical process control (SPC) charts to identify variation due to special versus common causes (random variation) for one key performance indicator (KPI) – the weekly ePROs response rate. All 6 SIMPRO sites contributed data for this analysis: 3 for chemotherapy patients and 3 for surgery patients. Sites had to have at least 18 months of KPI data; information from the first 4 months after eSyM rollout were excluded to account for program ramp-up. The KPI was calculated as the number of patients who submitted an ePRO questionnaire divided by the number eligible to submit a questionnaire each week. The upper and lower confidence limits were set at +/- 2 sigma to allow for 95% of observations to fall within the limits. Three rules were applied to identify special cause variation: 1) astronomical points (outside sigma limits), 2) shifts (8 or more points above or below a center line), and 3) trends (6 or more points consecutively ascending or descending). Implementation strategies that mapped to special cause variations were selected as those most likely to impact the KPI. Results: Across the 6 sites that deployed eSyM, the mean KPI performance ranged from 19.7%-35.7%. SPC charts detected multiple special cause variations in the KPI at all 6 sites, though the patterns varied by site. All sites demonstrated positive and negative astronomical variations as well as positive and negative shifts. Three sites demonstrated negative trends; no sites demonstrated positive trends. Direct patient outreach was the implementation strategy most frequently associated with special cause variation, followed by changes in promoting the ePRO program to clinical staff. Positive changes were associated with strategy initiation; negative changes were associated with strategy discontinuation. Conclusions: Applying SPC charts to a KPI from the eSyM deployment highlighted the effect of personal communication with patients and providers, suggesting that these implementation strategies could offer value to other sites deploying eSyM. The absence of positive trends early in eSyM program deployment underscores the need to identify better implementation strategies and allocate more resources to foster program adoption and sustainability. Applying SPCs to other KPIs, such as symptom severity and healthcare utilization, could improve eSyM deployment and impact.

Software process monitoring a complex activity. Recently many authors have been suggesting the use of Statistical Process Control (SPC) for software process monitoring while others have pointed out potential pitfalls in using this approach. SPC often used as is without the appropriate customizations or extensions to software, given the peculiarities and differences of software processes compared to manufacturing ones. As so the starting point to understand how and whether SPC can be used in software to understand its contribution in monitoring processes. This work puts together experience collected by the authors in using SPC in industrial contexts, points out the main issues concerning software process monitoring and highlights how the technique addresses them. The main contribution of the paper to formalize and put a set of guidelines together in a disciplined process for guiding practitioners in correctly using SPC during process monitoring.

The early stages of industrial SPC (statistical process control) programs are typically characterized by trendcharts at critical process steps. As processes are brought under control, these trendcharts are replaced by control charts. But premature replacement of trendcharts by control charts, before processes are under control, may undermine the credibility of nascent SPC programs, causing a barrage of special cause flags triggered by limits inappropriately calculated on unstable processes. The author describes an operational definition of the term under control as needed to decided which trendcharts can be implemented on control charts. Simple rules, which derive from Monte Carlo simulations of special cause flags from W.A. Shewhart (1931) and L.S. Nelson (1984), provide constant and low probabilities of inappropriate process rejection and maintain the high efficiency of time series analysis intrinsic to traditional special cause flags. The rules depend heavily on sample size; it is shown that any attempt to ignore the effects of sample size will invite wildly uncontrolled risks of rejection good processes. A table is provided to facilitate application of these setup tests at a constant and low risk of rejecting good processes. >

In this paper, we propose the novel use of Statistical Process Control (SPC) as a tool for identifying anomalies in the image acquisition process of a digital camera, for the purpose of camera identification. Control charts are used to illustrate the overall level of control associated with several devices (models include Apple iPhone 3G and 3GS, Nokia N97, and Leica D-Lux4), which are in turn reviewed in accordance with the Western Electric Rules for identifying assignable causes for the observed variation. X-Moving Range and Exponentially Weighted Moving Average (EWMA) control charts are used to highlight the variation for a subset of the devices. By implementing such a statistical model, the forensic investigator is much better positioned to understand the behaviour of a particular device, and is ultimately able to identify the most unstable feature of the cameras image acquisition process, thereby establishing a suitable fingerprint for matching images to their source.

The magnificent seven statistical process control (SPC) tools are commonly used to monitor the variation in the process. Control charts are the most efficient and real‐time monitoring tool from the SPC toolkit. A control chart visually differentiates the effect of special or inherent cause variations and indicates the process as out‐of‐control when such a special cause exists. For single occasion problems, simple random sampling (SRS) is used as a sampling design. When the quality of a product is assessed at regular intervals, then modified successive sampling (MSS) is chosen as the preferred sampling mechanism. Hence, this paper intends to propose EWMA‐MSS(S) and CUSUM‐MSS(S) charts under MSS schemes to monitor moderate to small shifts in the location parameter of a process. We presented the performance evaluation of EWMA‐MSS(S) and CUSUM‐MSS(S) charts in terms of run length metrics and compared them with the Shewhart‐MSS(S) control chart. The outcomes show that the EWMA‐MSS(S) perform amazingly well as compared to the Shewhart‐MSS(S) and CUSUM‐MSS(S) control charts. Further, illustration of the proposed charts on the QSAR aquatic toxicity dataset is also used to demonstrate the application of the proposed charts.

Questions about the validity of measurements made in an E/sup 3/ laboratory using software to control the instrumentation are a continuing concern for EMC engineers. In previous work, it has been shown that the use of statistical process control (SPC) methods to gage and track the variability of system measurements can provide confidence in the repeatability of the system hardware setup, and in the data it produces. Once the hardware system stability has been demonstrated, it is possible to also utilize SPC historical information to assist in confirming the validity of the data gathered using the system's control software.

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.

Sig sigma is a methodology where companies strive to reproduce results ending up having a 99.9996% chance their product will be void of defects. In order for companies to reach six sigma, statistical process control (SPC) needs to be introduced. SPC has many different tools associated with it, control charts being one of them. Control charts play a vital role in managing how a process is behaving. Control charts allow users to identify special causes, or shifts, and can therefore change the process to keep producing good products, free of defects. There are many factories and manufacturing facilities having implemented some sort of statistical process control. St. Jude Medical implemented control charts to monitor different tools on the manufacturing line. How the data is entered and stored poses a difficult situation for the person monitoring the processes. The program used to keep the control charts is not user friendly and difficult to use. Another program can be produced to provide a greater level of efficiency. The goals of this project are to stress how important control charts are in the manufacturing world, what problems are currently seen for operators and supervisors, and how a new and improved program can help fix the current situation. This paper goes into the reasons for the change as well has what has been improved.

–Statistical process control (SPC) charts are critically important for quality control and management in manufacturing industries, environmental monitoring, disease surveillance, and many other applications. Conventional SPC charts are designed for cases when process observations are independent at different observation times. In practice, however, serial data correlation almost always exists in sequential data. It has been well demonstrated in the literature that control charts designed for independent data are unstable for monitoring serially correlated data. Thus, it is important to develop control charts specifically for monitoring serially correlated data. To this end, there is some existing discussion in the SPC literature. Most existing methods are based on parametric time series modeling and residual monitoring, where the data are often assumed to be normally distributed. In applications, however, the assumed parametric time series model with a given order and the normality assumption are often invalid, resulting in unstable process monitoring. Although there is some nice discussion on robust design of such residual monitoring control charts, the suggested designs can only handle certain special cases well. In this article, we try to make another effort by proposing a novel control chart that makes use of the restarting mechanism of a CUSUM chart and the related spring length concept. Our proposed chart uses observations within the spring length of the current time point and ignores all history data that are beyond the spring length. It does not require any parametric time series model and/or a parametric process distribution. It only requires the assumption that process observation at a given time point is associated with nearby observations and independent of observations that are far away in observation times, which should be reasonable for many applications. Numerical studies show that it performs well in different cases.

Increasing customer demands and the need to stay competitive create challenges in high-volume manufacturing companies competing in global markets. Increasing pressures to produce high-quality products with fewer resources can cause degradation in procedural compliance, particularly when compliance conflicts with the ability to meet production schedules. Reaction to statistical process control (SPC) out-of-control conditions is one such procedure. SPC procedures often require that processes he shut down until the cause of an out-of-control condition is identified and removed. Poor integration of SPC procedures into existing business systems makes compliance with this type of procedure stressful for many levels of the organization. Using focus group data, sample surveys, and focused interviewing, a model is derived that describes the SPC system changes that can occur. The interrelationships between these organizational functions, their respective knowledge levels, and what motivates them is critical to understanding why attempts to integrate SPC into business systems can fail. This article proposes a sociotechnical model describing the de-evolution of SPC in organizations where careful consideration is not applied to the integration of SPC into existing business systems. Advice to practitioners on the avoidance of SPC de-evolution is provided.

Introduction Statistical Process Control(SPC) entails developing ability to achieve neverending improvement of quality. Control here means more than meeting this year's budget. It means what Juran[l] refers to as the habit of improvement, or what Deming[2] calls philosophy of continuing improvement. The various techniques of SPC provide means of achieving improvement on a continuing basis. Benefits of improved quality include increased productivity, better competitive position, and increased employee involvement. Since measured quality of a product is always subject to a certain amount of variability due to chance, statistical signals make it possible to separate variation due to common (chance) causes from variation outside of this system. The reasons for these assignable, or special, causes of variation outside of normal pattern can be discovered and corrected. The emphasis is shifted from dealing with problems after they occur, to detection and prevention. As there are two types of variation, there are two ways of improving system or process. One way is to reduce variation owing to common causes and other is to reduce variation due to special causes. The statistics do not detect special cause variation, they allow a trained operator to do this. If not apprehended and corrected, this type of variation may recur. The other way of improving process is to reduce variation owing to common causes. This requires action on process itself, and this is management's responsibility. A process is operating in statistical control when only source of variation is due to common causes. Process capability is ability of a process to meet customer expectations (specifications). The basis for continuing improvement is to bring process into statistical control by detecting and eliminating special causes of variation, so that performance is predictable.