Pooling knowledge for higher productivity

Within the framework of the major research project X-Forge (Everything as a Service) funded by the state of Baden-Württemberg, tool manufacturer MAPAL was the consortium leader of the Productivity as a Service (ProdaaS) area. MAPAL was cooperating with the machine manufacturer F. Zimmermann and the measurement technology specialist Blum to be able to offer customers solutions from a single source. 

As an further project partner, Fraunhofer IPA had set out to facilitate the underlying business model between the partners and to assess the value added for the customers. An first pilot project came to an end in mid-2024 after a total of three years. Based on this, the digital service offering is to be continuously expanded over the coming years. 
 

CAM process planning with tool selection, path planning and selection of process parameters provides considerable leverance for increasing productivity. While commissioning a component, further adjustment on the machine are most often necessary to arrive at an optimal result. Today, the planning dimension is largely decoupled from machining. Knowledge gained on the machine does not necessarily make its way to process planning. Insufficient feedback inhibits the learning effect and results in the machine operator having to start from scratch with each new drilling process. A structured knowledge base from practical application is simply missing in CAM process planning.

The project partners contribute various information to the “Productivity-as-a-Service” offer for stable and efficient machining processes. The assessment of the wear on the tools is relevant understanding under what conditions a bore, for example, was made. It also provides information about the current tool life and allows tool life prognoses. Blum extracts the corresponding data in a dedicated software service and makes it available. 
 

The pilot project, which took place at Karl Walter Formenbau, involved a Tool Performance Optimizer. With it, the user is able to correct the settings of the process parameters for drilling in the event of deviations to reduce downtime due to breakage or unplanned tool changes. 

The database also makes it possible to build on structured, methodically collected and evaluated experience from the past during the planning phase for new, previously unknown applications with the help of similarity searches. The Tool Performance Optimizer is to be marketed via two sales channels. In addition to the traditional solution business offered from a single source, it will also be found on large platforms based on Gaia-X.

The goal of the next development step is to record any deviations in an assistance system and warn the user immediately during operation with a traffic light system. For the coming years, self-learning services for autonomous parameter optimization and intelligent CAM process planning is in the pipelines. Finally, end-to-end automation from the drawing to the finished component should be possible from 2029.