Forthcoming articles

International Journal of Experimental Design and Process Optimisation

International Journal of Experimental Design and Process Optimisation (IJEDPO)

These articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

Forthcoming articles must be purchased for the purposes of research, teaching and private study only. These articles can be cited using the expression "in press". For example: Smith, J. (in press). Article Title. Journal Title.

Articles marked with this shopping trolley icon are available for purchase - click on the icon to send an email request to purchase.

Register for our alerting service, which notifies you by email when new issues are published online.

Open AccessArticles marked with this Open Access icon are freely available and openly accessible to all without any restriction except the ones stated in their respective CC licenses.
We also offer which provide timely updates of tables of contents, newly published articles and calls for papers.

International Journal of Experimental Design and Process Optimisation (5 papers in press)

Regular Issues

    Abstract: Polymers are one of the most extensively used materials in the manufacturing industry. Modified to the requirement or specification for a particular application, a variety of methods may be used in processing these materials. To fulfil the requirement of the application and improve the performance of end product, an optimal combination of process parameters is required. This may be achieved through optimization, a promising tool, which provides better performance at a reduced cost. By employing a suitable optimization technique, the properties of polymers can be predicted without performing experiments, which would be very beneficial in terms of time and money saving by preserving materials normally consumed during the experimental optimization phase. In this study, different optimization techniques such as Genetic Algorithm (GA), Artificial Neural Network (ANN), Particle Swarm Optimization (PSO), Taguchi method and others, which have been used to optimize the process parameters in polymers, are discussed in detail. In addition, the detailed algorithm and mathematical expressions used to apply these optimization techniques have also been presented.
    Keywords: Optimization algorithm; controlling factors; design of experiments; performance; process parameter.

  • Batch sequential NOAB designs by way of simultaneous construction and augmentation   Order a copy of this article
    by Zachary Little, Jeffery Weir, Raymond Hill, Brian Stone, Jason Freels 
    Abstract: Space-filling designs help experimenters to represent simulation outputs efficiently when entire input spaces cannot be exhaustively explored. Batch sequential designs allow for intermediate analyses to occur as later batches of experimental design points are being tested, given the ability to change later design points based on the outputs observed, and stop the experiment when the current observations are deemed sufficient to reduce experimental cost. Nearly orthogonal-and-balanced (NOAB) designs have good space-filling properties and can accommodate design spaces with continuous, discrete, and categorical factors. In this paper, mixed-integer linear programming (MILP) formulations used to find NOAB resolution III, IV, and V designs are extended to construct batch sequential NOAB designs, where design stages can use different NOAB approaches. A case study is presented where a simultaneous construction approach results in overall more desirable designs than when using design augmentation, yet requires a predefined number of points for each design stage.
    Keywords: design of experiments; mixed factor; space filling; nearly orthogonal-and-balanced; mixed-integer linear program; meta-model.

  • Multi-objective Machining Parameter Optimization of Aluminum Alloy 6063 by the Taguchi-Artificial Neural Network/Genetic Algorithm Approach   Order a copy of this article
    by Babafemi Malomo, Kolawole Oladejo, Adebayo Fadairo, Olusola Oladosu, Temitayo Jose 
    Abstract: This study investigates the turning of aluminum alloy 6063 to optimize the material removal rate (MRR) and surface roughness (Ra) simultaneously. L27 Taguchis orthogonal experiments were conducted by incorporating machining parameters of speed (260, 470, 840 rev/min), feed (0.2, 0.3, 0.4 mm/rev) and depth of cut (0.5, 1.0, 1.5). Analysis of variance (ANOVA) and signal-to-noise ratio were applied to determine the optimal control settings and validated by confirmatory tests. The performance characteristics were modelled by second-order regression, artificial neural network (ANN) and genetic algorithm (GA). The results indicate that the optimal conditions for MRR (375 mm3/min) and Ra (1.298
    Keywords: machining parameters; material removal rate; surface roughness; artificial neural network; genetic algorithm.

  • Non-Sequential Augmentation Strategies to Address Separation in Logistic Regression   Order a copy of this article
    by Anson Park, Michelle Mancenido, Douglas Montgomery 
    Abstract: Previous research on small sample multi-factor D-optimal designs for the logistic regression model has demonstrated that these designs are prone to encountering separation, a phenomenon where the responses are separable by a hyperplane in the design space. Separation causes the non-existence of maximum likelihood parameter estimates and represents a serious problem for model fitting purposes. In this paper, several non-sequential design augmentation strategies, where additional experimental trials are performed following an initial experiment that has encountered separation, are investigated. Small D-optimal initial designs are generated for several representative logistic regression models, and the effectiveness of each augmentation strategy in eliminating separation is evaluated through a simulation study. Results indicate that augmenting design runs (trials) in regions of maximum prediction variance (MPV) is the most effective strategy for eliminating separation. However, MPV augmentation tends to produce designs with lower D-efficiencies. The paper illustrates that MPV augmentation reliably eliminates separation and can be used in practice to obtain usable parameter estimates for the logistic regression model.
    Keywords: Design of Experiments; Optimal Design; Logistic Regression; Separation; Non-Existence; Maximum Likelihood; Augmentation.

  • Optimization of Fuel in Fire Tube Saturated Steam Boiler   Order a copy of this article
    by Uzair Ibrahim, Sarah Farrukh, Arshad Hussain, Muhammad Bilal Khan Niazi 
    Abstract: Considerable improvements have been made to minimize the fuelrnconsumption in industrial boilers since a sizable portion of operational cost can be reduce with fuel optimization. In this study a combination of air preheater and condensing economizer is proposed to optimize the fuel usage in fire tube heat boiler. The process utilizes 10% excess air that is preheated to 96oC in an air preheater using the heat of stack flue gases. This improves the boilers efficiency by 3%. Moreover, makeup water is also heated to 84oC in condensing economizer using the heat of stack flue gases coming from air preheater. This proposed assembly extracts most of the energy from stack flue gases before it start to condense. Simulation on ASPEN HYSYS
    Keywords: Fire tube boiler; Fuel optimization; Air preheater; Condensing economizer; Efficiency; Sensitivity analysis; Aspen HYSYS.