Typically, during the development phase, the freedom to make improvements to rigging is in practice reduced to a small number of real trials and is limited by the effort connected with the changes.

As opposed to real-world trials autonomous optimisation using simulation tools provides significant advantages in the thoroughness of the search for solutions and in the speed of the search. It can also handle many interconnected variables at the same time and automatically rank their influence on the outcomes.

Autonomous optimisation allows for the simultaneous modification of many parameters. As well, the quality criteria resulting from the changes can be individually and quantitatively evaluated. Combined with established tools from statistical design of experiments, casting process simulation can be used to autonomously optimise casting processes and designs.

The software follows several targeted outcomes simultaneously and finds the best compromise between them based on first principles. The automated assessment of all simulated quality criteria automatically throws up the most successful design candidates for consideration by the methods engineer.

The casting process simulation tool MAGMA5 can be used to ensure optimised and robust casting layouts and process windows before the first metal is poured. The software searches for the best possible process parameters, optimal runner and gate positions and dimensions, as well as locations and sizes of risers and chills. Foundry engineers can use autonomous optimisation as a virtual field for experimentation, to simultaneously achieve different quality and cost targets.

The goal of retaining the user-friendliness of the simulation tool while integrating this new methodology was achieved through the implementation of capabilities for parametric geometry creation and automatic parameter variation, together with tools for statistical analysis of autonomous designs of experiments and genetic algorithms for autonomous optimisation. The simultaneous assessment of the derived results enables the foundry engineer to easily compare and evaluate outcomes from numerous simulations. Dependencies between design and process variables, quality criteria and objectives are clearly visualised.

Thirty years after the introduction of casting process simulation, foundry engineers now can combine single simulations, autonomous DOEs and autonomous optimisations to gain better process understanding and to establish robust casting processes making quality castings at the lowest possible cost.

For further details contact Ametex on 083 306 1867 or visit www.ametex.co.za

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