Mahmoud Awad

Multiresponse optimization using multivariate process capability index

Robust design is an efficient method for product and process improvement which combines experimentation with optimization to create a system that is less sensitive to uncontrollable variation. In this article, a simple and integrated modeling methodology for robust design is proposed. This methodology achieves the robustness objective function and input variables constraints simultaneously. The objective function is written in terms of the multivariate process capability vector (MCpm) of several competing features of the system under study. The proposed methodology is applicable to general functions of the system performance with random variables. The effectiveness of the methodology is verified using two real-world examples which are compared with those of other robust design methods. Copyright

A Measurement System Analysis Approach for Hard-to-Repeat Events

ABSTRACT When measurements are nonreplicable, assessing the measurement system is a difficult task and cannot be done using the conventional gauge repeatability and reproducibility methods. This is due to the fact that the object measured is affected by the measurement and/or changes with time. This article describes an alternative method that can be used to study the repeatability and reproducibility of a measurement system in case the measured quantity varies over time. The essence of the method is that each part is measured simultaneously by multiple gauges.

Utilizing Kaizen process and DFSS methodology for new product development

Purpose The purpose of this paper is to propose a new framework for early design stage utilizing the benefits of Kaizen events, and Design for Six Sigma (DFSS) methodology. To gain a better understanding of the proposed method, a case study of a diesel engine development was presented where the proposed methodology was followed. Design/methodology/approach This paper proposes a hybrid Kaizen DFSS methodology consisting of four Kaizen milestone events with pre-work preceding these events. The events are in line with the four phases of DFSS methodology (define, characterize, optimize, and verify). Findings In order for the proposed method to succeed, few key enablers should be available such as management buy-in and support, effective resources utilization, and proper planning. However, this methodology should be utilized for key projects where criticality is high and deadlines are nearby. Practical implications As proved by two projects, one of them is presented in this paper; the use of the proposed methodology is effective and can bring significant positive changes to an organization. Originality/value Although Kaizen is an old and well-known process, it is to the best of the author’s knowledge that Kaizen has not been utilized in the early design stages of new product development projects. In this paper, a hybrid methodology combining traditional DFSS systematic approach conducted using Kaizen improvement events is proposed and supported by a real-life case study.

Impact of Optimality Criteria on Metamodeling Accuracy Under Scarce Sampling Plans

Metamodeling has been widely used in place of complex numerically intensive simulations to perform design reliability assessment and optimization. Due to cost and time constraints, most complex simulations can only afford a limited number of runs with a relatively large number of factors. The accuracy of a metamodel is affected by the degree of the underlying non-linearity, the sample size, the sampling strategy, and the type of the metamodel. In this study, the effect of the DOE optimality criteria on the accuracy of the Kriging metamodel is investigated under scarce sampling plans. Uniformity optimization is performed using some of the most popular uniformity measures, such as Centered Discrepancy (CL 2 ), Maximin, and Entropy criteria. Case studies consist of eight analytical closed-form functions drawn mostly from real engineering applications with five to seven factors each and various degrees of nonlinearity. The percent improvements in Relative Root Mean Square Error (RRMSE) over random Latin Hypercube Sampling (LHS) and S/N ratio are used to evaluate the performance and robustness of each criterion. Based on the results of the case studies investigated, it is concluded that the Entropy, E(2,1) 1 , and Maximin, M(5,1), have yielded the best results in terms of overall performance and consistency.

Analyzing sensitivity measures using moment-matching technique

Sensitivity indices are used to rank the importance of input design variables or components by estimating the degree of uncertainty of output variable influenced by the uncertainty generated from input variables or components. With the advent of highly complex engineering simulation models that describe the relationship between input variables and output response, the need for an efficient and effective sensitivity analysis is more demanding. Traditional importance measures either requires extensive random number generations or unable to measure variables interaction effects. In this article, a generalized approach that can provide efficient and accurate global sensitivity indices is developed. The approach consists of two steps; running an orthogonal array based experiment using moment-matched levels of the input variables followed by a variance contribution analysis. The benefits of the approach are demonstrated through different real life examples.

An economical analysis of residential hybrid solar-wind power generators in Abu Dhabi

As a reaction to environmental footprint, high cost and long payback period of building renewable energy power plants, governments started to scale down renewable energy to the commercial and residential building entities. In this article, the feasibility of residential solar, wind, and hybrid solar-wind power generators to meet electricity demand to a typical villa in Abu Dhabi is analyzed. The evaluation is based on historical data for seasonal electrical consumption and seasonal observations of renewable energy sources (solar and wind). Economical metrics such as Annual worth (AW), Future worth (FW), Benefit to Cost Ratio (B/C) and Internal Rate of Return (IROR) methods are used to analyze the economics of the system. Based on these economic metrics both solar and hybrid systems prove feasibility with very slight margin. Wind system, however, does not prove any feasibility as a stand-alone system.

Selection of free zone industrial plants proposals using analytic network process (ANP)

The interest of establishing and expanding free zones (FZ) to promote the national industrial sector grew rapidly in the last two decades. In this paper, plant proposals assessment and decision-making approach is developed for local communities or governments to evaluate plant benefit to the local communities. Local community leaders are faced with the challenge of deciding which industrial proposals are better for the national growth in the short and long run. This challenge arises from the large amount of performance measures that affect new proposed plants and local community’s success. In this study, an analytic network process (ANP) is used to help local communities in making rational decisions. The proposed approach is supported by a case study to evaluate proposals presented to a local free zone in Abu Dhabi, UAE.

MPP search method using limited radial importance sampling

Competitiveness and current market conditions push product development to strive for more product functionality, fewer design cycles, reduced cost and better quality. One practical approach to achieve that is through building robustness in early design stages utilising Design for Six Sigma (DFSS) disciplines. In this paper, we propose a new search method for the Most Probable Point (MPP), which is a good metric for measuring product robustness. The new method is based on a simulation within a limited radial sampling region determined by an initial uniform DOE that guarantees a good coverage of design space. Numerical non-linear examples are used to compare and verify the validity of the new method.

Stochastic scheduling of workforce-constrained preventive maintenance activities in petroleum plants

Maintenance is one of the major contributors of fixed cost or cost of ownership in a refinery or a chemical plant with a typical cost of

Fault Detection of Fuel Systems Using Polynomial Regression Profile Monitoring

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