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Application of artificial neural networks for modelling correlations in age hardenable aluminium alloys

Musharavati F., Hamouda A. S. M.

Journal of Achievements in Materials and Manufacturing Engineering

2010

Vol. 41, nr 1-2

140-146

Purpose: This paper discusses some of the preliminary results of an ongoing research on the applications of artificial neural networks (ANNs) in modelling, predicting and simulating correlations between mechanical properties of age hardenable aluminium alloys as a function of alloy composition. Design/methodology/approach: Appropriate combinations of inputs and outputs were selected for neural network modelling. Multilayer feedforward networks were created and trained using datasets from public literature. Influences of alloying elements, alloy composition and processing parameters on mechanical properties of aluminium alloys were predicted and simulated using ANNs models.Two sample t-tests were used to analyze the prediction accuracy of the trained ANNs. Findings: Good performances of the neural network models were achieved. The models were able to predict mechanical properties within acceptable margins of error and were able to provide relevant simulated data for correlating alloy composition and processing parameters with mechanical properties. Therefore, ANNs models are convenient and powerful tools that can provide useful information which can be used to identify desired properties in new aluminium alloys for practical applications in new and/or improved aluminium products. Research limitations/implications: Few public data bases are available for modelling properties. Minor contradictions on the experimental values of properties and alloy compositions were also observed. Future work will include further development of simulated data into property charts. Practical implications: Correlations between mechanical properties and alloy compositions can help in identifying a suitable alloy for a new or improved aluminum product application. In addition, availability of simulated structure-process-property data or charts assists in reducing the time and costs of trial and error experimental approaches by providing near-optimal values that can be used as starting point in experimental work. Originality/value: Since the simulated data provides near-optimal values, manufacturers of new and/or improved aluminum alloys can use the simulated data as guidelines for narrowing down extensive experimental work. This in turn reduces the process design cycle times. Designers of new and/or improved aluminum products can also use the simulated data as a guideline for correlating property-application information, which is useful in preliminary design phase.

Manufacturing process planning optimisation in reconfigurable multiple parts flow lines

Ismail N., Musharavati F., Hamouda A. S. M., Ramli A. R.

Journal of Achievements in Materials and Manufacturing Engineering

2008

Vol. 31, nr 2

671-677

Purpose: This paper explores the capabilities of genetic algorithms in handling optimization of the critical issues mentioned above for the purpose of manufacturing process planning in reconfigurable manufacturing activities. Two modified genetic algorithms are devised and employed to provide the best approximate process planning solution. Modifications included adapting genetic operators to the problem specific knowledge and implementing application specific heuristics to enhance the search efficiency. Design/methodology/approach: The genetic algorithm methodology implements a genetic algorithm that is augmented by application specific heuristics in order to guide the search for an optimal solution. The case study is based on the manufacturing system. Raw materials enter the system through an input stage and exit the system through an output stage. The system is composed of sixteen (16) processing modules that are arranged in four processing stages. Findings: The results indicate that the two genetic algorithms are able to converge to optimal solutions in reasonable time. A computational study shows that improved solutions can be obtained by implementing a genetic algorithm with an extended diversity control mechanism. Research limitations/implications: This paper has examined the issues of MPP optimization in a reconfigurable manufacturing framework with the help of a reconfigurable multiparts manufacturing flow line. Originality/value: The results of the case illustration have demonstrated the practical use of diversity control implemented in the MGATO technique. In comparison to MGAWTO, the implemented MGATO improves the population diversity through a customized threshold operator. It was clear that the MGATO can obtain better solution quality by foiling the tendency towards premature convergence.