{"id":169051,"date":"2025-10-17T07:29:00","date_gmt":"2025-10-17T05:29:00","guid":{"rendered":"https:\/\/renewable-carbon.eu\/news\/?p=169051"},"modified":"2025-10-16T14:49:08","modified_gmt":"2025-10-16T12:49:08","slug":"kiks-launches-open-beta-phase-ai-platform-for-the-plastics-industry","status":"publish","type":"post","link":"https:\/\/renewable-carbon.eu\/news\/kiks-launches-open-beta-phase-ai-platform-for-the-plastics-industry\/","title":{"rendered":"KIKS launches open beta phase \u2013 AI platform for the plastics industry"},"content":{"rendered":"\n

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The KIKS (K\u00fcnstliche Intelligence und Kunststoffe<\/em>, German for artificial intelligence and plastics<\/em>) research project is entering its next phase: the platform is now available to anyone as an open beta version<\/strong>.<\/p>\n\n\n\n

KIKS is our response to the growing challenges facing the plastics industry \u2013 from sustainability and raw material availability to digitalization. The platform provides companies with AI-supported, data-driven tools that can be used along the entire value chain. KIKS is funded by the German Federal Ministry of Research, Technology and Space. While our project partner TwinsCrew supports the project as a plastics specialist with in-depth domain knowledge, we contribute to the project\u2019s success with our expertise in AI, machine learning, and data science.<\/strong><\/p>\n\n\n\n

Four specific applications with direct practical benefits are already available today \u2013 made possible by our very comprehensive plastics database based on publicly available data:<\/p>\n\n\n

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PolyTwin<\/strong>: Uses the k-nearest neighbors algorithm to identify suitable material alternatives from our database \u2013 ideal for supply bottlenecks or for sustainable material decisions. An integrated sustainability filter facilitates the search for environmentally friendly plastics with similar properties.<\/p>\n\n\n\n

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PolyMix<\/strong>: Uses classic data science and machine learning methods to analyze the behavior of plastics with different fillers. The tool enables data-driven prediction of material properties for new composites \u2013 without the need for time-consuming new data collection. This shortens development times and saves resources.<\/p>\n\n\n\n

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PolyFill<\/strong>: Fills in missing values in technical data sheets based on our extensive material database. Machine learning algorithms are used to predict missing characteristic values \u2013 a quick way to better assess material behavior.<\/p>\n\n\n\n

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PolyGuide<\/strong>: An intelligent chatbot that uses natural language to facilitate access to the database. The LLM-supported system automatically translates questions into SQL queries and provides well-founded answers \u2013 ideal for decision-makers and plastics manufacturers.<\/p>\n\n\n\n

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Test our platform for free<\/h3>\n\n\n\n
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With the launch of the open beta version, we invite companies, research institutions, and interested parties to test the platform free of charge and without obligation<\/strong> at https:\/\/kiks.ehrenmueller.de<\/a><\/strong>. The PolyTwin tool is immediately available to everyone. If you would like to try out additional features<\/strong>, simply send an email to kiks@ehrenmueller.ai<\/a>.<\/strong> <\/p>\n\n\n\n

test the KIKS-Plattform<\/a><\/p>\n\n\n\n

Linkedin<\/a><\/p>\n\n\n\n

Collaboration with KIKS<\/h3>\n\n\n\n

KIKS also offers a wide range of collaboration options<\/strong>:<\/p>\n\n\n\n