The application of artificial intelligence in optimisation of automotive components for reuse

Purpose: Automotive component reuse as one of the product recovery strategy is now gaining importance in view of its impact on the environment. Research and development on components design and manufacturing as well as tools and methods to facilitate reuse are under way in many countries. To enable reuse, components have to be assessed and its reliability and life time predicted. This paper presents the development work on an optimisation model for assessing potential automotive components for reuse using artificial intelligence approaches. Design/methodology/approach: As a part of the study, the paper currently focuses on initial study on ease of disassembly design. The model for predicting reliability and durability of reuse components is then developed using Artificial Neural Networks (ANNs) and further optimised for reliability and life cycle cost using Genetic Algorithm (GA). Findings: The proposed model will enable the local automotive industry to effectively assess potential components for reuse in support of further design and manufacturing improvements. Research limitations/implications: This study hopes to contribute to design for reuse by assessing high potential and reliable reuse components at the lowest costs. Originality/value: Artificial intelligence methods, such as artificial neural networks (ANNs) and genetic algorithm (GA), can be applied to solve problem as they can provide satisfactory and acceptable solutions for many complex problems.