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Case Study

Kärcher Automates Materials Data Processes with Ansys Granta MI Software

“Even if I calculate it conservatively, I get a result of something like almost 700 person hours saved per year, which is almost 0.4 full-time person equivalents.”

— Daniel Carmine Manocchio
Manager of the Materials Technology Lab, Kärcher

Kärcher, a global leader in cleaning equipment, relies on sophisticated materials testing to keep its innovations safe, durable, and compliant. A team of just nine materials engineers oversees roughly 700 laboratory examinations every year, covering everything from raw-material approvals and color measurements to failure analysis. To handle this, Kärcher adopted the Ansys Granta MI Enterprise platform as a centralized data management system for their materials information and data.

The materials information in Granta MI software includes all the types of materials that Kärcher uses, ranging from plastics and polymers to adhesives and tapes. This information comprises physical properties, test data, and aesthetic characteristics. The Kärcher team turned to the Granta MI Scripting Toolkit for Python to streamline their process, integrate their other systems with Ansys tools, and eliminate errors and extensive manual effort while importing data to Granta MI software. This effort saved hundreds of engineering man-hours in the process.

Challenges

Kärcher, a global leader in cleaning equipment, relies on sophisticated materials testing to keep its innovations safe, durable, and compliant. A team of just nine materials engineers oversees roughly 700 laboratory examinations every year, covering everything from raw-material approvals and color measurements to failure analysis. To handle this, Kärcher adopted the Ansys Granta MI Enterprise platform as a centralized data management system for their materials information and data.

The materials information in Granta MI software includes all the types of materials that Kärcher uses, ranging from plastics and polymers to adhesives and tapes. This information comprises physical properties, test data, and aesthetic characteristics. The Kärcher team turned to the Granta MI Scripting Toolkit for Python to streamline their process, integrate their other systems with Ansys tools, and eliminate errors and extensive manual effort while importing data to Granta MI software. This effort saved hundreds of engineering man-hours in the process.

karcher high pressure washer

Kärcher professional high-pressure washer

Engineering Solutions 

  • Bespoke applications: The Kärcher team developed bespoke applications and importers to automate the flow of data from testing machines directly into Granta MI software. One such application connects directly to Granta MI, validates the sample number, checks for data collisions, and uploads the information. 
  • One-click workflows: For more advanced testing software, such as Zwick's TestXpert, the Kärcher team created a one-click workflow. Using the testing software's built-in scripting language, they added a custom button to the interface. When an engineer clicks the button after a test, the software automatically exports the data and launches a custom Python importer, which processes and uploads all tabular and curve data to the correct record in Granta MI software in seconds.
  • Integrations with other systems: The automation was extended beyond testing machines. For example, Kärcher developed a script that uses an application programming interface (API) to connect to the UL Yellow Card database. This script runs on a schedule, automatically fetching the latest regulatory and flammability data and populating the relevant material records in Granta MI software.
karcher autonomous scrubber

Kärcher autonomous scrubber

Benefits

  • Time savings: Granta MI software saved Kärcher nearly 700 engineering hours per year, which is equivalent to 0.4 full-time equivalent (FTE), by automating data handling. For the small team, this is a “crucial survival factor.”
  • Increased Efficiency and Accuracy: The platform virtually eliminated data entry errors and duplicate tests. Engineers are able to focus on analysis, not data wrangling.
  • Agile approach:  The easy-to-use Python approach lets the materials team at Kärcher tailor every workflow “as we like it, as we want it, and as we need it,” in a self-sufficient way, without relying on IT or other departments.
materials laboratory at karcher

Materials laboratory at Kärcher