An automated gripper hand in a smart factory

Factory of the future at TU Graz

T-Systems provided an important link that solved the puzzle facing Austria’s Graz University of Technology.

Aside from a few pioneers, smart factories to date have been relegated to being a concept only. While a few companies were just beginning to consider Artificial Intelligence (AI) to take their production to the next level in terms of efficiency and flexibility, Covid-19 came along and pushed them out of their comfort zones. Even Gartner analysts expect smart factories to become mainstream within the next two to five years.
Two people who very quickly sensed the potential and seized the opportunity were professors Dr. Franz Haas and Dr. Rudolf Pichler from the Institute of Production Engineering at Graz University of Technology (TU Graz). They started a consortium of experienced manufacturing solutions providers as part of a call for proposals from the corresponding Federal Ministry (BMVIT previously, BKI now) to set up a real “smart” pilot factory.

The setup

A woman sits in front of a machine wearing safety goggles

According to the scientists, TU Graz had a choice of two options while setting up the Smart Factory: “Either to rely on a complete suite from a single major vendor, which would result in a vendor lock-in, making it difficult to use third-party solutions; or adopt a best-of-breed approach by using specialized partial solutions from different providers.”

TU Graz chose the latter option. The Smart Factory would have components provided by a consortium of service providers: enterprise resource planning (ERP), product lifecycle management (PLM) and manufacturing execution system (MES). The initial scope for T-Systems was to provide cloud expertise to support Big Data analytics along with a comprehensive Security solution comprising Security Information and Event Management (SIEM) and Security Operations Center (SOC) services.

However, the consortium soon came upon a major roadblock: how will the various solutions be integrated with the plants and machines so that the added values of the Smart Factory are realized? How to achieve a seamless production process through manufacturing, quality inspection and assembly to labelling, packaging and shipping – without any manual intervention?  

Smart Factory at the TU Graz

The Technical University in Graz, Austria, is putting agile and data-secure manufacturing concepts of the future into practice. Find out in this video how T-Systems has taken on a key role in making the futuristic concept of the Smart Factory a reality.

Overcoming the roadblock

The map was all drawn up: Leverage Teamcenter from Siemens as an adaptable PLM system, an ERP system from proAlpha, and a Manufacturing Execution System (MES) from Solidat. Added to this is MindSphere that Siemens operates for Big Data analytics and, contributed by T-Systems, a Hadoop cluster in a Microsoft Azure Cloud. Since comprehensive security is crucial for a networked and open factory, T-Systems provides the design of the firewall architecture and SIEM/SOC services.

But in data-driven production processes with necessarily continuous and secure communication between OT and IT (Operation Technology/Information Technology), the classic “V characteristics” of Big Data play a decisive role. Their sheer volume alone determines the computing power required. The variety of data types and structures is a challenge in terms of standardization and distribution of all incoming data. The fact that they can be formatted differently from one data source to another brings variability into play. And last but not least, the velocity with which data is generated and made available is decisive for how quickly the right decisions are ultimately made — whether by humans or machines. 

For this purpose, T-Systems introduced a PDM WebConnector as the prime jigsaw piece in the TU Graz project. It has standardized interfaces to all known systems and allows automatic transfer of data between the sub-processes. 

At the beginning, the PDM WebConnector receives the current data of the PLM (Product Lifecycle Management) from Siemens Teamcenter, integrates it with the configuration data from the ERP system, and provides the processed data for production via the Manufacturing Execution System (MES) towards the shopfloor. There, the workpiece is produced. Then the PDM WebConnector delivers the result back to the PLM system for quality control purposes. From pre-assembly and main assembly and renewed quality checks to marking, packaging, and shipping, the PDM WebConnector transfers all relevant information to the systems involved. Even the transportation robots between the stations are connected to the central processes.

Roland Wiesmüller, Sales Manager of Digital & Analytics at T-Systems in Austria, says: “It’s really fair to say that without the PDM WebConnector, the Smart Factory in Graz would remain just a dream: It connects the various control systems and resources (machines and equipment in production) with each other and ensures smooth data communication along the entire value chain. Therefore, it practically provides a kind of Esperanto, a language that all systems involved in the process can understand”.

The ‘manual’ problem

The Smart Factory of TU Graz

The Smart Factory, which covers more than 300 square meters, started its operations at TU Graz in April 2021 and demonstrates the advantages of completely integrated manufacturing using the live example of a shaft gearbox for a robot arm. Prior to that, the project faced a rather tricky issue. The Smart Factory uses Computerized Numerical Control (CNC) machines that can automatically produce workpieces with high precision, even for complex shapes. But these high-precision machines need exact tools to match their processes and requirements. For this purpose, a tool presetter is used to allow the appropriate tools to be put together from a selection of different tool attachments. Employees need to select the attachments that match the respective program from a pool and mount them in the machine, making it a highly manual and error-prone process. A time-consuming comparison with the system identifier of the tool has always been necessary. An incorrect tool can not only spoil a complete series, but also damage the machine. 

“The problem now is that, for the worker doing the assembly, the tools on the trolley cannot be clearly assigned to the program”, Michael Schmollngruber, Team Lead BI & Big Data Consulting at T-Systems Austria, explains. “There is a missing link from the tool to its identifier in the system at this point in the process”. This also brings the fifth V of Big-Data — Veracity — into play.

Putting the ‘smart’ into the ‘factory’

To solve this problem, TU Graz uses an AI-based object recognition service, which ensures that the right tool is installed for the program in question. To achieve this, employees currently still use a smartphone and take a photo of the tool via an app. This photo is sent to a cloud backend (Azure), where a specially trained algorithm identifies the tool shown in the photo and matches it with the NC programming. Within a few seconds, the employee receives a verification or falsification — whether they have selected the correct tool and receives further information via the ID of the tool, such as the upcoming production orders in which the tool is used or information from the PLM system. 

The solution speeds up the selection of the correct tool, avoids rejects and quality defects, and significantly increases production efficiency. It can also be expanded to larger tool pools at any time or, for example, converted into an augmented reality variant that makes the use of smartphones unnecessary. Deutsche Telekom has already provided the necessary 5G campus network in the Smart Factory at the Graz University of Technology.

Container technology Docker is used in the backend. When the request reaches the Azure Cloud, the complete object analysis functionality is started serverless — and switched off again after identification is complete. In this way, the application is provided on demand. In addition, it can scale effortlessly — if multiple requests arrive at the same time — so that fast response times are achieved for all users. The AI algorithm used is an in-house development by T-Systems. In contrast to the usual approach, no real tools were used to train the model. The team took advantage of the fact that every object in the Smart Factory has a Digital Twin: the training data was generated from the CAD models of the tools.  

Portrait of Rudolf Pichler

The expertise of T-Systems around seamless data transfer, security, and data analytics in production makes the cooperation with the Telekom subsidiary particularly valuable for us.

Rudolf Pichler, Head of Institute of Production Engineering at Graz University of Technology (TU Graz)

Proof in the pudding

The Smart Factory project removed manual processes, making way for faster output, better product and process quality, and the ability to recalibrate to changing customer requirements at a short notice. 

The design changes are imported by engineering via the PLM system and the production of the new workpiece starts in no time. Due to the transparency of the system and the flexibility of the PDM WebConnector, data can also be supplied from other development systems. On the AI front, the tool selection solution not only speeds up the selection of the correct tool, but it also avoids scrap and quality defects. 

With its pilot, TU Graz has shown what the factory of the future can look like: efficient, agile, scalable, secure, and safe. The factory is not only open to interested parties to look at, but it also allows the TU Graz to manage contract research and execute cooperation in diverse company-related development topics such as R&D. 

The industry can also gain other benefits such as a modular design for high agility, high-cost efficiency, minimized susceptibility to errors in the production process, integrated OT/IT security at the highest level, and big data analytics for continuous optimization and continuous improvement.

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