12.03.2025

Pooling knowledge for higher productivity

Customer Productivity as a Challenge for MAPAL and cooperation partners

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By harnessing all the data created during machining, companies make their processes around ten percent more productive. This was the result of a project where MAPAL together with industry partners pooled previously distributed knowledge and made it available as a digital value-added service. 

Productivity as a service as part of the funded large-scale research project X-Forge

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. 
 

Two employees discuss the properties of various tools while other employees make adjustments to machines in the background.
The “Productivity as a Service” project is investigating the possibilities of linking separate process and production data and using self-learning algorithms to develop a service that will help manufacturers with future challenges.   ©MAPAL
The members of the working consortium already monitor processes with many sensors that deliver the corresponding data. However, existing systems only offer a limited view of the entire system of machines, tools and workpieces. While highly complex expert systems are available to the users on the machine, these individual components do not interact with one another. In reality, it’s hardly feasible to bring them together. If problems occur, it is accordingly difficult to analyse them afterwards ode to optimise ongoing processes.

Structured knowledge base for process planning

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. 
 

This measurement data is compared with a MAPAL knowledge database in a process analysis to check if the parameters set on the machine follow the manufacturer's specifications. At the same time, the status assessment of the machines from F. Zimmermann is incorporated, which provides information about the condition of the spindle. Finally, a higher-level software module links the various sources together and makes information available in a structured manner. In an error state analysis, the user can find a cause at a press of a button.

Pilotprojekt Tool Performance Optimizer

Beim Pilotprojekt, das bei Karl Walter Formenbau stattfand, ging es um einen Tool Performance Optimizer. Dem Anwender ist es damit gelungen, die Einstellung der Prozessparameter für das Bohren bei Abweichungen zu korrigieren, um Stillstandzeiten durch Bruch oder ungeplante Werkzeugwechsel zu reduzieren.  

Weiterhin ist mit der Datenbank die Möglichkeit gegeben, auch bereits in der Planung für neue, bisher unbekannte Anwendungen mit Hilfe von Ähnlichkeitssuche auf strukturierte, methodisch gesammelte und bewertete Erfahrungen aus der Vergangenheit aufzubauen. Der Tool Performance Optimizer soll über zwei Absatzkanäle vermarktet werden. Neben dem klassischen Lösungsgeschäft mit einem Angebot aus einer Hand wird er auch auf einer großen Plattform zu finden sein, die als Datenökosystem auf Gaia-X basiert. 

Ziel des nächsten Entwicklungsschrittes ist es, auftretende Abweichungen in einem Assistenzsystem zu erfassen und den Anwender gleich während des Betriebs mit einem Ampelsystem zu warnen. Für die kommenden Jahre sind noch selbstlernende Services für autonome Parameteroptimierung und Intelligente CAM-Prozessplanung vorgesehen. Schließlich soll ab 2029 auch eine durchgehende Automatisierung von der Zeichnung bis zum fertigen Bauteil möglich werden. 

Portrait MAPAL Redakteur Ostertag Mathias

Kontakt

Mathias Ostertag Public Relations mathias.ostertag@mapal.com Tel.: +49 7361 585 3566

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