Forthcoming articles

 


International Journal of Experimental Design and Process Optimisation

 

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International Journal of Experimental Design and Process Optimisation (3 papers in press)

 

Regular Issues

 

  • ENGINEERING OPTIMIZATION OF PROCESS PARAMETERS FOR POLYMERS: AN OVERVIEW   Order a copy of this article
    by ANUJA AGRAWAL, RAMINDER KAUR, R.S. WALIA 
    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.