Jb Hull

Soft computing applications in dynamic model identification of polymer extrusion process

Abstract This paper proposes the applications of soft computing to deal with the constraints in conventional modelling techniques of the dynamic extrusion process. The proposed technique increases the efficiency in utilising the available information during the model identification. The resultant model can be classified as a ‘grey-box model’ or has been termed as a ‘semi-physical model’ in the context. The extrusion process contains a number of parameters that are sensitive to the operating environment. Fuzzy rule-based system (FRBS) is introduced into the analytical model of extrusion by means of sub-models to approximate those operational-sensitive parameters. In drawing an optimal structure for each sub-model, a hybrid algorithm of genetic algorithm with fuzzy system (GA-fuzzy) has been implemented. The sub-models obtained show advantages such as linguistic interpretability, simpler rule-base and less membership functions (MFs). The developed model is adaptive with its learning ability through the steepest decent error back-propagation algorithm. This ability might help to minimise the deviation of the model prediction when the operational-sensitive parameters adapt to the changing operating environment in the real situation. The model is first evaluated through simulations on the consistency of model prediction with the theoretical analysis. Then, the usefulness of adaptive sub-models during the operation is further explored in existence of prediction error.

Orthogonal fuzzy rule-based systems: selection of optimum rules

In this paper, the concept of orthogonal fuzzy rule-based systems is introduced. Orthogonal rules are an extension to the definition of orthogonal vectors when the vectors are vectors of membership functions in the antecedent part of rules. The number and combination of rules in a fuzzy rule-based system will be optimised by applying orthogonal rules. The number of rules, and subsequently the complexity of the fuzzy rule-based systems, are directly associated with the number of input variables and distinguishable membership functions for each individual input variable. A subset of rules can be used if it is known which subset provides closer behaviour to the case when all rules are used. Orthogonal fuzzy rule-based systems are proposed as a judgment as to whether the optimal rules are selected. The application of orthogonal fuzzy rules becomes essential when fuzzy rule-based systems containing many inputs are used. An illustrative example is presented to create a model for the solder paste printing stage of surface mount tech-nology.

Development of a smart technique in plastic identification independent of the components dimensions

Using an ultrasound attenuation technique and fuzzy rule-based system analysis, an intelligent technique is developed to produce a signature independent of component dimensions for a range of different polymers (plastics) such as PET, PE, PS, etc. The technique can be employed in recycling process to sort different polymeric materials in a relatively fast response time and in an automated/semi-automated system. The early study indicates that the method is a powerful tool for the identification of components of complex shape where access to all surfaces may be restricted.