Ibrahim Masood

Synergistic-ANN Recognizers for Monitoring and Diagnosis of Multivariate Process Shift Patterns

An intelligent control chart pattern recognition system is essential for efficient monitoring and diagnosis process variation in automated manufacturing environment. Artificial neural networks (ANN) have been applied for automated recognition of control chart patterns since the last 20 years. In early study, the development of control chart patterns recognizers was mainly based on generalized-ANN model. There has been an increasing trend among researchers to move beyond generalized recognizer particularly for addressing complex recognition tasks. However, the existing works mainly focus on univariate process cases. This paper aims to investigate an effective synergistic-ANN model for on-line monitoring and diagnosis multivariate process patterns. The recognition performances of a generalized-ANN and the parallel distributed ANN recognizers for learning dynamic patterns of multivariate process patterns were discussed.

Strategies for Integrating Quality, Environmental, Safety and Health Management Systems

This paper discusses the strategies for integrating Quality, Environmental, Safety and Health Management Systems based on survey and case studies results. Questionnaires were distributed to 87 companies that certified with both ISO9001 and ISO14001. Meanwhile, three case studies were conducted at the manufacturing companies that have integrated several management systems. There are two ways of integrating the management systems which are: (1) consecutive implementation of management systems followed by integration or (2) integrate the management systems simultaneously from the beginning. Based on survey and case studies, it was found that many organisations started with implementing individual management system first, and then followed by integrating the management systems. Almost all the survey respondents agreed that the sequence should start with establishing Quality Management System first, and then integrate with Environmental Management System and followed by Occupational Health and Safety Management System.

A Framework for Multivariate Process Monitoring and Diagnosis

Monitoring and diagnosis of mean shifts in manufacturing processes become more challenging when involving two or more correlated variables. Unfortunately, most of the existing statistical process control frameworks are only effective in shift detection but suffers high false alarm, that is, imbalanced performance monitoring. The problem becomes more complicated when dealing with small shift particularly in identifying the causable variables. In this research, a framework to address balanced monitoring and accurate diagnosis was investigated. Design considerations involved extensive simulation experiments to select input representation based on raw data and statistical features, recognizer design structure based on synergistic model, and monitoring-diagnosis approach based on two stages technique. The study focuses on correlated process mean shifts for cross correlation function, ρ = 0.1 ~ 0.9 and mean shift, μ = ± 0.75 ~ 3.00 standard deviations. The proposed design, that is, an Integrated Multivariate Exponentially Weighted Moving Average with Artificial Neural Network gave superior performance, namely, average run length, ARL1 = 3.18 ~ 16.75 (for out-of-control process), ARL0 = 452.13 (for in-control process) and recognition accuracy, RA = 89.5 ~ 98.5%. This research has provided a new perspective in realizing balanced monitoring and accurate diagnosis of multivariate correlated process mean shifts.

Multivariate process monitoring and diagnosis: A case study

In manufacturing industries, monitoring and diagnosis of multivariate process out-of-control condition become more challenging. Process monitoring refers to the identification of process status either it is running within a statistically in-control or out-of-control condition, whereas process diagnosis refers to the identification of the source variables of out-of-control process. In order to achieve these requirements, the application of an appropriate statistical process control framework is necessary for rapidly and accurately identifying the signs and source out-of-contol condition with minimum false alarm. In this research, a framework namely, an Integrated Multivariate Exponentially Weighted Moving Average with Artificial Neural Network was investigated in monitoring-diagnosis of multivariate process mean shifts in manufacturing audio video device component. Based on two-stages monitoring-diagnosis technique, the proposed framework has resulted in efficient performance.

Design of an Artificial Neural Network Pattern Recognition Scheme Using Full Factorial Experiment

Automated recognition of process variation patterns using an artificial neural network (ANN) model classifier is a useful technique for multivariate quality control. Proper design of the classifier is critical for achieving effective recognition performance (RP). The existing classifiers were mainly designed empirically. In this research, full factorial design of experiment was utilized for investigating the effect of four design parameters, i.e., recognition window size, training data amount, training data quality and hidden neuron amount. The pattern recognition study focuses on bivariate correlated process mean shifts for cross correlation function, ρ = 0.1 ~ 0.9 and mean shifts, μ = ± 0.75 ~ 3.00 standard deviations. Raw data was used as input representation for a generalized model ANN classifier. The findings suggested that: (i) the best performance for each pattern could be achieved by setting different design parameters through specific classifiers, which (ii) gave superior result (average RP = 98.85%) compared to an empirical design (average RP = 96.5%). This research has provided a new perspective in designing ANN pattern recognition scheme in the field of statistical process control.

Statistical features-ANN recognizer for bivariate process mean shift pattern recognition

Artificial neural network (ANN)-based recognizers have been developed for monitoring and diagnosis bivariate process mean shift in multivariate statistical process control (MSPC). They have better average run lengths (ARLs) performance in monitoring process mean shifts and gave an useful diagnosis information compared to the traditional MSPC schemes such as T2, multivariate cumulative sum (MCUSUM) and multivariate exponentially weighted moving average (MEWMA). The existing recognizers are raw databased, whereby raw data input representation were applied into ANN. This approach required in a large network size, more computational effort and training time consuming. In this paper, the statistical features input representation was investigated, whereby the raw data were transformed into exponentially weighted moving average, multiplication of mean with standard deviation and multiplication of mean with mean-square value. The statistical features-ANN recognizer resulted in smaller network size, fast training time, better ARLs for monitoring process mean shifts and comparable recognition accuracy for diagnosing the source variable(s) compared to the raw data-ANN recognizer.

Risk prediction model: Statistical and artificial neural network approach

Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician’s decision making, individual’s behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies th...

Recognition of unnatural variation patterns in metal-stamping process using artificial neural network and statistical features

Unnatural process variation (UPV) is vital in quality problem of a metalstamping process. It is a major contributor to a poor quality product. The sources of UPV usually found from special causes. Recently, there is still debated among researchers in finding an effective technique for on-line monitoring-diagnosis the sources of UPV. Control charts pattern recognition (CCPR) is the most investigated technique. The existing CCPR schemes were mainly developed using raw data-based artificial neural network (ANN) recognizer, whereby the process samples were mainly generated artificially using mathematical equations. This is because the real process samples were commonly confidential or not economically available. In this research, the statistical features - ANN recognizer was utilized as the control chart pattern recognizer, whereby process sample was taken directly from an actual manufacturing process. Based on dynamic data training, the proposed recognizer has resulted in better monitoring-diagnosis performance (Normal = 100%, Unnatural = 100%) compared to the raw data- ANN (Normal = 66.67%, Unnatural = 26.97%).

Quality control in hard disc drive manufacturing using pattern recognition technique

Computerized monitoring-diagnosis is an efficient technique to identify the source of unnatural variation (UV) in manufacturing process. In this study, a pattern recognition scheme (PRS) for monitoring-diagnosis the UVs was developed based on control chart pattern recognition technique. This PRS integrates the multivariate exponentially weighted moving average (MEWMA) control chart and artificial neural network (ANN) recognizer to perform two-stage monitoring-diagnosis. The first stage monitoring was performed using the MEWMA statistics, whereas the second stage monitoring-diagnosis was performed using an ANN. The PRS was designed based on bivariate process mean shifts between 0.75σ and 3.00σ, with cross correlation between ρ=0.1 and 0.9. The performance of the proposed PRS has been validated in quality control of hard disk drive component manufacturing. The validation proved that it is efficient in rapidly detecting UV and accurately classify the source of UV patterns. In a nutshell, the PRS will aid in realizing automated decision making system in manufacturing industry.