Comments on the mixture detection rule used in SPC control charts

When calculating independently the false alarm rate of the eight usual runs rules used in SPC control chart, it appears that the proposed rule designed to detect mixture patterns corresponds to a Type-I error strongly lower than the seven other rules. This discrepancy is underlined and the mixture rule is showed to be useless both for in-control and out-of-control processes. Thus a modification of the mixture detection rule is proposed and the impact of this new mixture rule is then illustrated and discussed using Monte Carlo calculations

Real-time quality assurance testing using photonic techniques: Application to iodine water system

A feasibility study of the use of inspection systems incorporating photonic sensors and multivariate analyses to provide an instrumentation system that in real-time assures quality and that the system in control has been conducted. A system is in control when the near future of the product quality is predictable. Off-line chemical analyses can be used for a chemical process when slow kinetics allows time to take a sample to the laboratory and the system provides a recovery mechanism that returns the system to statistical control without intervention of the operator. The objective for this study has been the implementation of do-it-right-the-first-time and just-in-time philosophies. The Environment Control and Life Support Systems (ECLSS) water reclamation system that adds iodine for biocidal control is an ideal candidate for the study and implementation of do-it-right-the-first-time technologies

Random Sequence Perception Amongst Finance and Accounting Personnel: Can We Measure Illusion Of Control, A Type I Error, or Illusion Of Chaos, A Type II Error?

The purpose of this dissertation was to determine if finance and accounting personnel could distinguish between random and non-random time-series strings and to determine what types of errors they would make. These individuals averaging 13 years of experience were unable to distinguish non-random patterns from random strings in an assessment composed of statistical process control (SPC) charts. Respondents scored no better than guessing which was also assessed with a series of true-false questions. Neither over-alternation (oscillation) nor under-alternation (trend) strategies were able to predict type I or type II error rates, i.e. illusion of control or illusion of chaos. Latent class analysis methods within partial least squares structural equation modeling (PLS-SEM) were successful in uncovering segments or groups of respondents with large explained variance and significant path models. Relationships between desirability of control, personal fear of invalidity, and error rates were more varied than expected. Yet, some segments tended to illusion of control while others to illusion of chaos. Similar effects were also observed when substituting a true-false guessing assessment for the SPC assessment with some loss of explained variance and weaker path coefficients. Respondents also provided their perceptions and thoughts of randomness for both SPC and true-false assessments

The viability of Weibull analysis of small samples in process manufacturing

This research deals with some Statistical Quality Control (SQC) methods, which are used in quality testing. It investigates the problem encountered with statistical process control (SPC) tools when small sample sizes are used. Small sample size testing is a new area of concern especially when using expensive (or large) products, which are produced in small batches (low volume production). Critical literature review and analysis of current technologies and methods in SPC with small samples testing failed to show a conformance with conventional SPC techniques, as the confidence limits for averages and standard deviation are too wide. Therefore, using such sizes will provide unsecured results with a lack in accuracy. The current research demonstrates such problems in manufacturing by using examples, in order to show the lack and the difficulties faced with conventional SPC tools (control charts). Weibull distribution has always shown a clear and acceptable prediction of failure and life behaviour with small sample size batches. Using such distribution enables the accuracy needed with small sample size to be obtained. With small sample control charts generate inaccurate confidence limits, which are low. On the contrary, Weibull theory suggests that using small samples enable achievement of accurate confidence limits. This research highlights these two aspects and explains their features in more depth. An outline of the overall problem and solution point out success of Weibull analysis when Weibull distribution is modified to overcome the problems encountered when small sample sizes are used. This work shows the viability of Weibull distribution to be used as a quality tool and construct new control charts, which will provide accurate result and detect nonconformance and variability with the use of small sample sizes. Therefore, the new proposed Weibull deduction control charts shows a successful replacement of the conventional control chart, and these new charts will compensate the errors in quality testing when using small size samples

Recognition of Process Disturbances for an SPC/EPC Stochastic System Using Support Vector Machine and Artificial Neural Network Approaches

Because of the excellent performance on monitoring and controlling an autocorrelated process, the integration of statistical process control (SPC) and engineering process control (EPC) has drawn considerable attention in recent years. Both theoretical and empirical findings have suggested that the integration of SPC and EPC can be an effective way to improve the quality of a process, especially when the underlying process is autocorrelated. However, because EPC compensates for the effects of underlying disturbances, the disturbance patterns are embedded and hard to be recognized. Effective recognition of disturbance patterns is a very important issue for process improvement since disturbance patterns would be associated with certain assignable causes which affect the process. In practical situations, after compensating by EPC, the underlying disturbance patterns could be of any mixture types which are totally different from the original patterns. This study proposes the integration of support vector machine (SVM) and artificial neural network (ANN) approaches to recognize the disturbance patterns of the underlying disturbances. Experimental results revealed that the proposed schemes are able to effectively recognize various disturbance patterns of an SPC/EPC system

Sensor Fusion and Process Monitoring for Ultrasonic Welding of Lithium-ion Batteries.

Ultrasonic metal welding is used for joining lithium-ion batteries of electric vehicles. The quality of the joints is essential to the performance of the entire battery pack. Hence, the ultrasonic welding process that creates the joints must be equipped with online sensing and real-time process monitoring systems. This would help ensure the process to be operated under the normal condition and quickly address quality-related issues. For this purpose, this dissertation develops methods in process monitoring and fault diagnosis using online sensing signals for ultrasonic metal welding. The first part of this dissertation develops a monitoring algorithm that targets near-zero misdetection by integrating univariate control charts and a multivariate control chart using the Mahalanobis distance. The proposed algorithm is capable of monitoring non-normal multivariate observations with adjustable control limits to achieve a near-zero misdetection rate while keeping a low false alarm rate. The proposed algorithm proves to be effective in achieving near-zero misdetection in process monitoring in ultrasonic welding processes. The second part of the dissertation develops a wavelet-based profile monitoring method that is capable of making decisions within a welding cycle and guiding real-time process adjustments. The proposed within-cycle monitoring technique integrates real-time monitoring and within-cycle control opportunity for defect prevention. The optimal decision point for achieving the most benefit in defect prevention is determined through the formulation of an optimization problem. The effectiveness of the proposed method is validated and demonstrated by simulations and case studies. The third part of this dissertation develops a method for effective monitoring and diagnosis of multi-sensor heterogeneous profile data based on multilinear discriminant analysis. The proposed method operates directly on the multi-stream profiles and then extracts uncorrelated discriminative features through tensor-to-vector projection, and thus preserving the interrelationship of different sensors. The extracted features are then fed into classifiers to detect faulty operations and recognize fault types. The research presented in this dissertation can be applied to general discrete cyclic manufacturing processes that have online sensing and control capabilities. The results of this dissertation are also applicable or expandable to mission-critical applications when improving product quality and preventing defects are of high interests.PhDIndustrial and Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113405/1/graceguo_1.pd

Control chart patterns recognition with constrained data

Recognition and classification of non-random patterns of manufacturing process data can provide clues to the possible causes that contributed to the product defects. Early detection of abnormal process patterns, particularly in highly precise and rapid automated manufacturing is necessary to avoid wastage and catastrophic failures. Towards this end, various control chart patterns recognition (CCPR) methods have been proposed by researchers. Most of the existing control chart patterns recognizers assumed that data is fully available and complete. However, in reality, process data streams may be constrained due to missing, imbalanced or inadequate data acquisition and measurement problems, erroneous entries and technical failure during data acquisition process. The aim of this study is to investigate and develop an effective recognition scheme capable of handling constrained control chart patterns. Various scenarios of data constraints involving missing rates, missing mechanisms, dataset size and imbalance rate were investigated. The proposed scheme comprises the following key components: (i) characterization of input data stream, (ii) imputation and feature extraction, and (iii) alternative recognition schemes. The proposed scheme was developed and tested to recognize the constrained patterns, namely, random, increasing/decreasing trend, upward/downward shift and cyclic patterns. The effect of design parameters on the recognition performance was examined. The Exponentially-Weighted Moving Average (EWMA) imputation, oversampling and Fuzzy Information Decomposition (FID) were investigated. This research revealed that some constraints in the dataset can eventually change the distribution and violate the normality assumption. The performance of alternative designs was compared by mean square error, percentage of correct recognition, confusion matrix, average run length (ARL), t-test, sensitivity, specificity and G-mean. The results demonstrated that the scheme with an ANNfuzzy recognizer trained using FID-treated constrained patterns significantly reduce false alarms and has better discriminative ability. The proposed scheme was verified and validated through comparative studies with published works. This research can be further extended by investigating an adaptive fuzzy router to assign incoming input data stream to an appropriate scheme that matches complexity in the constrained data streams, amongst others

An exploratory study on statistical process control in the UK food industry

Statistical Process Control (SPC) is an effective technique improving process performance in manufacturing companies; however, the literature shows its implementation in the food industry is still less evident. This research aims to assess the SPC implementation in the UK food industry and subsequently develops an SPC implementation roadmap (SPCIR) and SPC Readiness Self-assessment Tool for food companies to assess their readiness level to adopt SPC. Survey and multiple-case studies were conducted to identify the widespread of SPC, challenges of implementing SPC, Critical Success Factors and the reasons for not implementing SPC in this industry. A five-phase SPCIR was refined through the action research, while five SPC readiness factors were identified through the Delphi study. This study adds value to the current knowledge by extending organisational readiness theories through the identification of SPC readiness factors and expands the organisational learning theory by uncovering type of learning created within SPC implementation. This study is relevant, practical, and useful to both practitioners and academics by providing a holistic implementation roadmap to guide the managers to implement SPC not only at the organisational level but also at the project level. This study offers an itinerary of organisational readiness that enables the managers to confirm the organisational preparedness for the adoption of SPC. The small sample size may limit the generalisability of the findings. But this exploratory study provides critical information to the managers in this sector to develop a strategic plan for a successful SPC implementation