International Journal of Experimental Design and Process Optimisation (5 papers in press)
Engineering optimisation of process parameters for polymers: an overview
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 optimisation, a promising tool, which provides better performance at a reduced cost. By employing a suitable optimisation 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 optimisation phase. In this study, different optimisation techniques such as genetic algorithm (GA), artificial neural network (ANN), particle swarm optimisation (PSO), Taguchi method and others, which have been used to optimise the process parameters in polymers, are discussed in detail. In addition, the detailed algorithm and mathematical expressions used to apply these optimisation techniques have also been presented.
Keywords: optimisation algorithm; controlling factors; design of experiments; DOE; performance; process parameter.
Batch sequential NOAB designs by way of simultaneous construction and augmentation
by Zachary C. Little, Jeffery D. Weir, Raymond R. Hill, Brian B. Stone, Jason K. 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 resolutions 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; NOAB; mixed-integer linear program; meta-model.
Multi-objective machining parameter optimisation of aluminium alloy 6063 by the Taguchi-artificial neural network/genetic algorithm approach
by Babafemi O. Malomo, Kolawole A. Oladejo, Adebayo A. Fadairo, Olusola A. Oladosu, Temitayo I. Jose
Abstract: This study investigates the turning of aluminium alloy 6063 to optimise the material removal rate (MRR) and surface roughness (Ra) simultaneously. L27 Taguchi's 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 μm) were in agreement with the confirmatory tests. Regression models showed that the optimal points for MRR and Ra can be enhanced by the effect of interactions, but the ANN predicted the experimental data with better accuracy. The GA further elicited a set of optimal solutions for improving machining performance.
Keywords: machining parameters; material removal rate; MRR; surface roughness; artificial neural network; genetic algorithm.
Non-sequential augmentation strategies to address separation in logistic regression
by Anson R. Park, Michelle V. Mancenido, Douglas C. 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 completely or quasi-completely 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 local and Bayesian D-optimal initial designs are generated for several representative logistic regression models, and a Monte Carlo simulation methodology is then used to investigate the effectiveness of each augmentation strategy in eliminating separation. Results of the simulation study show 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; separation; non-existence; logistic regression; maximum likelihood; augmentation.
Optimisation of fuel in fire tube saturated steam boiler
by Uzair Ibrahim, Sarah Farrukh, Arshad Hussain, Muhammad Bilal Khan Niazi
Abstract: Considerable improvements have been made to minimise the fuel consumption in industrial boilers since a sizable portion of the operational cost can be reduced with fuel optimisation. In this study a combination of air preheater and condensing economiser is proposed to optimise the fuel usage in a fire tube heat boiler. The process utilises 10% excess air that is preheated to 96°C 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 84°C in the condensing economiser using the heat of stack flue gases coming from the air preheater. This proposed assembly extracts most of the energy from stack flue gases before it start to condense. A simulation using ASPEN HYSYS® shows that using preheated make up water and preheated combustion air, fuel demand in the boiler is reduced by 10%, thus making the process more economical.
Keywords: fire tube boiler; fuel optimisation; air preheater; condensing economiser; efficiency; sensitivity analysis; ASPEN HYSYS; flue gas; saturated steam; natural gas; combustion air; combustion efficiency.