Robotics and IIoT
Efficient robot monitoring
Microchips are installed almost everywhere - whether in smartphones, washing machines or cars. With increasing digitalization, the demand for semiconductors has been growing rapidly in all areas of the economy for years. As manufacturers' capacities are not growing adequately, there is a chip shortage, which has been exacerbated by the coronavirus crisis and the war in Ukraine. Semiconductor manufacturers must design their production processes to be highly efficient and maximize output.
In order to make maximum use of production capacities, processes need to be automated. Under the extreme conditions of chip production in clean rooms, robotic systems are therefore increasingly being used to further minimize human intervention in production and logistics. Increasing automation generates a large amount of data that needs to be used consistently. This is because analyzing this data can answer questions such as Where is a machine failure imminent? Is maintenance work necessary? Are the automated processes running as planned? Analytics platforms create transparency here, ensure smooth operation and help to identify potential for increasing efficiency and reducing costs. They can also create the basis for the new and further development of products and for data-driven business models.
However, the use of analytics platforms in industry is not yet common practice: according to FPT Germany, on average only 1% of the data generated in production companies is analyzed. This is often due to a lack of overview of the different data formats, which are available in various systems (silos) depending on the sensors and controllers used. However, the potential of data analytics should not be underestimated: "Companies that use the data they generate to add value increase their revenue by up to 50% and have been able to reduce production costs by up to 45%," explains Frank Bignone, Head of Global Digital Transformation at FPT.
Analytics platforms provide the technical infrastructure to process and analyze these different data formats so that specific optimization measures can be derived from the actual state. Data-driven evaluations also help to avoid production interruptions in semiconductor production.
A practical use case
Once companies have recognized this advantage, the question arises as to which technologies are best suited. This was also the case for Fabmatics, a manufacturer of autonomous robotic systems based in Dresden. Its complex products for the automation of handling, transport and storage processes, such as the Hero Fab series of transport robots, generate a large amount of data that has not yet been systematically recorded and evaluated. Fabmatics wanted to use the potential of this data to optimize the monitoring of logistics processes and offer its customers additional digital services in the future.
Together with the digital service provider Telekom MMS, the robotics software company Wandelbots and the IoT network Smart Systems Hub, Fabmatics has developed a prototype for an analytics platform. As part of the Digital Product Factory (DPF) co-innovation format, a cloud-based data management solution was created in just three months that Fabmatics customers can use to monitor the performance of their robots.
With the IoT solution based on Amazon Web Services (AWS), data from sensors and controllers can be analyzed, stored and processed despite different formats. Time series databases assign time stamps to the recorded values and changes can be shown over time. Deviations from the normal state are signaled and can trigger maintenance, for example.
Users can use the web-based dashboard to graphically display the data analysis in different perspectives. The platform also documents uncategorized process data in order to map the manufacturing process as completely as possible in data. The result is a virtual image of real production - a digital twin. Plotly Dash was used as the front-end technology to make data science and machine learning components more accessible to users.
Within the team of experts, Telekom MMS was responsible for implementing the cloud architecture. Open source components were primarily used in the development in order to be able to flexibly adapt the platform to new requirements in the future.
The data management platform will create transparency for all status data of Fabmatics' handling and transport robots in the future. The detection of anomalies serves as the basis for defining predictive maintenance cycles and prevents unforeseen production downtimes. The successful development of the prototype has shown that the technical implementation of analytics platforms is possible in a relatively short time and makes data from production practically usable. The Minimal Viable Product (MVP) created also offers Fabmatics a promising opportunity to expand its portfolio with digital services in the area of process optimization.
This article appeared in issue 6/23
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