Abstract
Abstract
Sophisticated computer aided engineering (CAE) simulation tools for injection moulding have been available and are now widely used in industrial practices. As a result, the design and manufacturing of injection-moulded parts have been literally transformed from a ‘black art’ to an engineering discipline based on scientific principles. It is well recognized that computer simulation tools help engineers to gain process insight and to pinpoint blind spots and problems that are overlooked. Nevertheless, there remains a missing link in CAE, which lies in the ability to identify effectively the optimal design and process variables, as it is hampered by the sheer amount of computer-generated data and complex non-linear interactions among those input variables. This paper presents the system implementation and experimental verifications of an integrated CAE optimization tool that couples a process simulation program with optimization algorithms to determine intelligently and automatically the optimal design and process variables for injection moulding. In addition, this study enables evaluation and comparison of various local and global optimization algorithms in terms of computational efficiency and effectiveness for injection moulding, as presented in this paper.