Authors: Anuja Agrawal; Raminder Kaur; R.S. Walia
Addresses: Department of Applied Chemistry and Polymer Technology, Delhi Technological University, Delhi – 110042, India ' Department of Applied Chemistry and Polymer Technology, Delhi Technological University, Delhi – 110042, India ' Department of Mechanical Engineering, Punjab Engineering College, Chandigarh – 160012, India
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.
International Journal of Experimental Design and Process Optimisation, 2019 Vol.6 No.2, pp.89 - 126
Received: 27 Sep 2018
Accepted: 10 Feb 2019
Published online: 22 Aug 2019 *