Robust cast product design driven by virtual experimentation and optimization with MAGMA5 – baseline technology for a resource efficient product development

Horst Bramann, Erik Hepp, Guido Dietrich, Heiner Michels



State of the art manufacturing industries are highly focused on sensitive handling of natural resources, recycling strategies and minimization of waste. This global challenge is a chance for casting processes, because consequent resource-optimized engineering is hallmarked by highest complexity of component geometries, robust manufacturing methods and at the same time demands on dimensional accuracy and mechanical properties to the highest degree.

The early hedged decision for an optimal production process in terms of weight reduction potential, properties of the cast component, economic efficiency and robustness in particular requires extensive product and process knowledge especially at the beginning of the CAE development cycle.

Enabler for this early stage decision process is front-loading, the systematic transfer of process knowledge on dependencies and occurring variations of the manufacturing conditions. The aim of the front-loading process is to provide information on the function, performance, technological and other properties of a product and the manufacturing process to responsible product engineers and designers in utilizable quality as early as possible.

The hereby abstracted contribution gives an introduction on the application, setup and potential of the enhanced methodology of virtual casting experimentation as a component of innovative cast part development within modern CAE processes.

Risk, fault-elimination and optimization strategies, methods, and results of primary production and various associated processes are presented on simple hands-on and comprehensible examples. Process optimizations for steel and ADI aiming at energy efficient heat treatment and optimal microstructures, mechanical properties, stresses and distortions are detailed. Examples for optimized lightweight iron and aluminium automobile and machinery construction are discussed. The parameter dependent analysis of defect formation and in particular their elimination by means of state of the art statistical tools provided by MAMGA5 is laid out. In-depth regard is given to the key strategical and practical aspects of CAE optimization based on the combination of experience, knowledge and virtually generated process understanding as a basis for improved decision-making structures and a cornerstone of continuous improvement towards innovative resource-conscious components and robust manufacturing.

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