Digitization of Plastics Engineering

Digital innovation and technology

Small and medium-sized enterprises (SMEs) in the plastics technology sector in particular face the challenge of collecting, processing and utilizing relevant process data for the use of data-supported methods. In view of the increasing shortage of skilled workers, growing quality requirements and the increasing need to use recycled materials, a targeted, digital transformation process is essential. Conventional solutions reach their limits here, as they are often not flexibly transferable to different production scenarios and require a high level of technical expertise. DUETT ("Digitalization in plastics processing under consideration of two use cases, integrated into a knowledge and technology transfer concept") therefore relies on an open and sustainable introduction of artificial intelligence and data-driven methods through a comprehensive and target group-specific transfer concept. A unique feature is the practical implementation of two use cases (automated inline detection of optical defects and data-based anomaly detection in recyclate processing), which are used to demonstrate the possible applications and potential of AI. The final results are practical, adaptable tools and learning modules that enable independent, future-proof digitalization and AI use in SMEs.

Possible applications in practice

DUETT is developing digital solutions for the introduction of artificial intelligence in plastics processing. SMEs in particular benefit from the innovation, as they can use DUETT to get started with digitalization, automate process monitoring and detect errors or anomalies at an early stage. The methods are practical, flexibly adaptable and promote independent applications of AI in production. In addition to plastics processors, all companies that strive for sustainable process optimization, quality assurance and the use of recycled materials are addressed. Across all industries, new fields of application are emerging in proactive error detection, the optimization of complex production chains and in the training and further education of future specialists.