Muneeb Akram

MINHAJ UNIVERSITY, Computer Science, Undergraduate

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University of British Columbia

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Portland State University

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University of Zagreb

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University of Haifa

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Kansas State University

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University of Cologne

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UNL - New University of Lisbon

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University of Macedonia

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Nepal College of Information Technology

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Graduate Center of the City University of New York

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Papers by Muneeb Akram

System and Method for Process Monitoring and Control

A Robust-Gain-Scheduled methodology for process parameter design and control with application to a carbonated beverage filling process

2011 IEEE International Conference on Industrial Engineering and Engineering Management, 2011

Proper selection of control parameters is critically important since it has a large impact on the... more Proper selection of control parameters is critically important since it has a large impact on the end product and enables keeping the operation run within the specified limits. It also helps to maximize profitability, to ensure quality and safety. In this paper, we will develop a Robust Gain-Scheduled (RGS) methodology that combines between the uses of: robust design to determine

Joint determination of optimum process mean, production run length and specification limits for a deteriorating process

International Journal of Quality Engineering and Technology, 2011

Most of the recent work found in literature solved the problem of determining the optimum values ... more Most of the recent work found in literature solved the problem of determining the optimum values of process parameters by considering one or jointly two parameters using separate models under different assumptions. The objective of this work is to develop a trine model that can be used for joint determination of three process parameters, namely: optimum process mean, production run length and specification limits, under mixed quality loss function for processes that are subject to deterioration over time. This paper will summarise the recent related literature and outlining the technical information required for this work. In this work, the problem will be tracked in two ways: by minimising the total loss and by maximising the net profit. For achieving that, we developed different models that can be used to determine optimum values for process parameters; the analysis leads to the development of the trine model. Numerical examples parallel to each model are presented to illustrate its use in determining the desired optimum parameter value. Sensitivity analysis for different process parameters are also presented to study their effects on the net profit in the view of satisfying the manufacturing requirements.

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Quality monitoring and process adjustment by integrating SPC and APC: a review

International Journal of Industrial and Systems Engineering, 2012

Statistical process control (SPC) and automatic process control (APC) are two important methods t... more 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.

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A fuzzy integrated SPC/APC scheme for optimised levels of process quality, performance and robustness

International Journal of Experimental Design and Process Optimisation, 2011

Statistical process control (SPC) and automatic process control (APC) are two independent methods... more Statistical process control (SPC) and automatic process control (APC) are two independent methods for quality improvement and process adjustment that have been developed in isolation from each other and applied within different industries. Both methods were initially considered to be in conflict with each other, until their advocates realised the fact that the techniques being applied were complementary rather than being contradictory. Since then, some work suggesting integration between SPC and APC techniques has appeared in the literature. Most integration strategies found in the literature applied SPC techniques for monitoring and APC techniques for process regulation, while others derived SPC controllers and applied their use alone, which does not imply complete integrated schemes. Our objective in this work is to develop an integrated scheme that combines the utilisation of SPC and APC techniques for process monitoring, as well as its adjustment under fuzzy logic interaction. We envision that driving any process under the proposed scheme will optimise its level of quality and performance, as well as robustness. To illustrate the effectiveness of the proposed scheme, we will conduct an optimisation case study on a pH control process.

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