CAM on the Cloud

HPC Competence Center

Arctur prides itself in not being just another HPC infrastructure provider, but rather one which always provides personal support even to users which are not so knowledgeable of HPC, so ensuring that every HPC project achieves or even surpasses user expectations. Their motto is “More than just HPC.” As an SME, they understand the issues that other SMEs face when dealing with HPC.
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The Enterprise

Stellba is a Germany-based SME working on hydropower plant maintenance, repair and overhaul, engineering and manufacturing one-of-a-kind products for the green energy sector with the goal to optimize energy efficiency.
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How HPC makes the difference

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The HPC resources can enable Stellba to simulate more complex machining tasks more quickly.

This success story is an outcome of CloudFlow project(*).

Simulating and optimizing the manufacturing process before the machines actually start making a new product is one of the key stages in manufacturing engineering. The aim is to minimize manufacturing time, to avoid wasting raw material (resources) and to safeguard the machines from being damaged. All these steps are very time-consuming and minimizing the time to find the best possible solution is crucial concerning the costs for the company.

The relevant process for the end user is the computation of the best tool path to machine a turbine blade. To find an optimal tool path requires many selections and decisions by the engineer, e.g. material, methodology, and each chosen configuration requires a dedicated simulation run. As these simulations are basically independent from each other, using a parallel computing infrastructure is crucial. The HPC resources can enable Stellba to simulate more complex machining tasks more quickly. In fact, the time to compute a best possible toolpath is now only one third of what was necessary before. This provides the opportunity to increase the quality of the machining. Tool paths are now calculated in parallel. The CAM workflow allows the end user to prepare all data sets at once to produce a good machining plan and execute them at once and in parallel instead of having to wait for each individual result in front of his desktop before the next variant can be computed.

(*)The CloudFlow project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement No 609029.