Highly flexible and highly efficient: electric motor production at Siemens Tübingen
At the Siemens factory in Tübingen, located in south-west Germany, about 450 employees are involved in the production of gearboxes for electric motors. These SIMOGEAR geared motors are used in airport baggage handling systems, for example. Components for about 600 geared motors are produced every day on 40 machine tools. The variance of the geared motors is close to 1037!
Needless to say, the individualization of products in Tübingen represents a daily challenge. The geared motor factory in Tübingen demonstrates how the systematic use of digitalization products makes such a diverse manufacturing environment significantly more productive.
"As a Siemens lead factory, we are responsible for establishing optimum processes before we implement them into other factories and assembly centers worldwide" explained Satyanarayan Chavhan, Digitalization Project Manager in the Tübingen factory.
"In today's VUCA world we can't afford to perform experiments in a real production environment. That's why one of our most important principles is always to verify new things in the digital world first!"
VUCAVUCA stands for Volatility, Uncertainty, Complexity and Ambiguity and refers to the dynamic and rapidly changing environment of business management.
Machining simulation using the digital twin of a machine tool
Sophisticated machining operations in very specific clamping situations are carried out in the Tübingen factory on high-quality milling machines and lathes. The name of the game is to master the widest possible range of challenges in this complex production environment. CNC programs can no longer be generated and verified at the machine itself. If possible, any potential problems should be identified in advance. For multi-channel lathes in particular - in other words, when tools on different tool holders simultaneously act on the part in different CNC programs - simulation of the machining process is more or less unavoidable. One reason for this is, for example, the constantly changing clamping situations and technologies. In such cases, collision monitoring and collision prevention can essentially only be implemented in a cost- and time-effective manner via simulation.
The "running-in" phase for all the different CNC programs must not delay the production of motor components or lead to machines being blocked. The high hourly operating rates for these complex multi-channel machines forbid any unnecessary downtimes. Unproductive times - for example machine set-up times - must be minimized. It is also important to know the CNC program runtime and therefore the machine assignment of the particular machining strategy in advance. This helps to optimize the use of resources - machines, tools, and employees.
The CNC production planning department in Tübingen therefore makes consistent use of the CAD/CAM-CNC process chain. In addition to generating the CNC program offline using NX CAM, the digital twin of each machine tool in NX CAM is of considerable importance. Simulation with the virtual Sinumerik (VNCK) and the virtual machine ensures a high degree of process reliability, even in this complex machining environment.
Benefits for machine utilization
"Since we've been using the digital twin, we've significantly reduced machine utilization time for producing the first prototypes at the real machine", explained Stefan Nothdurft, Head of CNC Production Planning. Siemens subsequently modeled the digital twins for older machines. For new machine tools, the machine digital twin is part of the requirement specification for the machine manufacturer.
Plant Simulation - digital image of workflows in a machine fleet
After simulating machining at the machine tool using the CAD/CAM-CNC process chain and checking for errors and determining machining time, the next step is to plan and simulate the best possible production concept. Another digital twin now comes into play - Plant Simulation. Plant Simulation makes it possible to simulate the production workflow, the material flow and all the production runtimes.
The simulation tool models either the complete machine fleet of a production facility or at least the section under review. Each machine is then assigned all the relevant times, from the machining and set-up time to the average fault and failure times. The best routes and plant combinations can then be determined for the various products and for different scenarios, for example minimizing throughput time.
Arnold Hauler, responsible for production and assembly planning in the Tübingen plant described a specific example in the factory: Different manufacturing options were considered for making a gearbox housing. Thanks to Plant Simulation, it was possible to decide on the best solution in terms of utilizing human and machine resources as well as optimizing costs. Comparing the different manufacturing options in the real world would have entailed high capital investment costs for additional equipment. So, without simulation, the comparison could not have been done in the first place.
Using digital twins helps to simulate the schedule process modifications, the new concepts and their impact in a very short time over a complete business year, without having to intervene in an active production landscape.
Verifying virtually optimized processes
After optimizing the individual process steps in the simulation phase, the results must be checked against reality, in other words evaluated in a real-life production environment.
This is where Analyze MyPerformance comes to the fore. The software tool determines the various OEE parameters, that is the machine utilization level, how frequently it is not productive and for what reasons. Compared to higher-level ERP systems such as SAP, Analyze MyPerformance has the advantage that the utilization level of each individual machine is visible, even within a fleet of several different machine tools. This makes it possible to see whether the machines in the fleet are fully compatible with each other. It thus becomes clear whether an individual machine is slowing down all the other machines, and in turn slowing down the whole process.
The immediate availability of the process data via "Analyze MyPerformance" represents a significant benefit. Arnold Hauler recalls how factory planners used to have to carry out long-winded and complex time measurements over long periods. Today, a bottleneck analysis based on OEE parameters to optimize the production process is very fast and also very precise. The effectiveness of changes to the process workflow can be checked within just a few hours or days.
More on digitally optimized geared motor production at Siemens Tübingen
The Siemens motor plant in Tübingen makes systematic use of the opportunities provided by digitalization. On the left is a video showing what has already been achieved in optimizing the part program flow using the "Manage MyPrograms" from the Siemens portfolio. More on "Analyze MyPerformance" on YouTube: