On this Experiment, LCM sets up simulation models for prototype electric motors including electromagnetic, thermal, mechanic and other aspects using LCM's system modeler SyMSpace which is running in the cloud environment. SyMSpace then finds the best-suited motor configuration using its genetic multi-objective optimizer. At the end of this process, the necessary production data is automatically generate from the model.
Using SyMSpace in the cloud environment provides the user with sufficient calculation power even for extensive models and deep optimization runs (up to 15.000 individual simulation cycles) - on demand and on pay per use basis.
Due to the streamlined process without human data handover, Hanning can process a prototype request from design to optimization to production data in very fast time. The target is to get the finished prototype within 5 working days.
The challenge for the ISV is to improve the simulation models for interfacing with the manufacturing processes including the external suppliers of the prototype materials.
|Address:||Altenbergerstr. 69, Linz 4040|
|Comment:||The experiment leader is the Linz Center of Mechatronics GmbH (LCM)|
Significant innovations, use case requirements and lessons learned - (3) close
- Electric machine design process including electromagnetic finite element computation, winding design, and post-processing are available on a pay-per-use basis as a cloud solution. To the knowledge of the experiment partners, such a solution has never before been presented.
- Seamless, automated creation of production data avoids data handover errors and provides fast transition from simulation and design to manufacturing of motor prototypes. The full parametric manner of realization also provides a feature never available before.
- The capability to bring a customer requirement in the form of a motor specification to a fully functional motor prototype within one week is a revolutionary USP for Hanning. Despite the fact that this capability still needs to be demonstrated in real life, the steps taken into this direction have greatly improved Hanning’s process know-how and agility.
The realized progress advances the state of the art in several aspects:
The establishment of the cloud cluster solution for running SyMSpace has greatly improved the engineering capacity of LCM which no longer includes the bottleneck of local computation resources. Being able to reserve as many cloud computing resources as necessary at any given time takes away the need to invest into hardware and maintenance just for covering peak loads. This means a great improvement of customer satisfaction.
Additionally, already five industrial partners are testing SyMSpace on a pay-per-use basis in the cloud. The main USPs are the low barrier for access (no upfront costs, no long-time installation, etc.) and the low total costs.
A certain interest has also been coming from academic players. As SyMSpace will see an open source release in the near future, this seems to be an interesting opportunity to run and eventually publish or contribute own algorithms from the academic research around electric machines. With the help of the cloud solution, the access barrier for remote partners has completely fallen away: beginning of September 2018, the solution was presented to a partner at the WEMPEC consortium at University of Madison, Wisconsin who is considering applying the SyMSpace cloud solution for research in magnetic bearings.
This example shows how the international contact, also across research areas has been facilitated using the cloud cluster solution of SyMSpace. This is an important driver for the innovation potential at LCM which, as a trans-academic-industrial player heavily relies on close contacts with other research institutions.
The demonstration of the faster and more reliable prototyping production process is expected to be a major improvement both internally and externally at Hanning. However, due to the delays and difficulties experienced in the implementation of the winding process, this step still needs to be realized. The potential, however, is impressive as demonstrated in the KPI metrics below.
Table 12: experiment 1 impact summary
The experiment setup was well prepared and discussed by the experiment partners LCM and Hanning. The implementation of the solution in the CloudiFacturing Marketplace, however, was unclear and needed significant efforts during the early stages of the experiment. This partly concerned the technical solution and partly was felt in the definition and in conveying the experiment structure to the consortium.
While this has added some unplanned efforts to the experiment which then reduced the time available for the practical implementations, the cause seems quite unavoidable for wave 1 of the experiments since the development of the Marketplace happened in parallel to the experiments. This situation will, therefore, automatically improve for the experiments in wave 2.
Applications areas and sectors - (2) close
- simulate and optimize manufacturing processes or manufacturing tools,
- leverage factory data to learn from it, and
- optimize manufacturing processes and/or manufacturability of goods.
Manufacturer of customer specific electrical drive systems
- No products are linked