See some of the most relevant publications about MAGMA applications and developments especially in the area of die casting.

Simulation Evolves to Autonomous Optimization

Today’s requirements on the development of a casting and the corresponding metal casting process demand methodologies and tools which allow a maximization of process robustness and profitability at the earliest possible point in time. Opposed to real-world trials, autonomous optimization using simulation tools provides significantly more flexibility.

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Energy and raw material savings in foundries through thorough utilization of simulation

Foundries are “world champions“ in effectively recycling their materials. More than 90% of all cast parts are made from re-melted scrap metal. But it doesn’t stop with the metal: molding materials (sand) and water are efficiently re-used, leading to almost no waste.

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Latest Advancements in Modelling and Simulation for High Pressure Die Castings

1.0 Introduction: Innovations and modifications in the techniques of high pressure die casting or tooling are forced by trends in part design, part load as well as by costs and times for development and manufacturing processes. All current trends require continuous improvement in planning of part performance and production processes. The quality of parts and the efficiency of development and manufacturing processes are primarily depending on the quality and accuracy of the planning process.

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Automatic Computerized Optimization in Die Casting Processes

Due to the multitude of factors affecting the quality of castings and the complex interactions of physics, metallurgy and casting geometry, empirical knowledge was the principal resource on which "optimized manufacturing engineering" relied. Foundry simulation can quantify experience and therefore only test a "state", whereas the conclusions from the calculations and improvements require the hands of an expert.

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Two for One – Transferring Proven Filling Characteristics From a Single to a Two Cavity Die Casting Die

The demands on the productivity and robustness on the high pressure die casting process for high quality components are continuously rising. At the same time, financial considerations mean that an exact and reliable planning of the die layout and production process is required. Today, experience provides a basis for a dependable production planning. With the ever-increasing complexity of die castings, however, the application of experience from previous projects to new castings is increasingly difficult and questionable. A new and novel approach using autonomous optimization makes it possible to use information gained from an existing casting process.

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