2: Integration of Sensor and Data Systems: Students can evaluate suitable sensors and data acquisition systems for monitoring environmental and operational conditions. (K5)

3. Process Analysis and Optimization: Students are capable of analyzing process data, identifying typical types of errors, and developing optimization measures using model-predictive controllers. (K6)

4. Communication and Data Integration: Students can use communication standards and interfaces within the production cell to efficiently integrate data and optimize production processes. (K5)

5. Data Science Integration: Students understand the requirements and interfaces for data science in digitized plastic production. (K4)

1. Identification and evaluation of suitable sensors for monitoring temperature, humidity, cavity pressure, structure-borne sound, and other operating conditions. (K5)
2. Application and use of common data acquisition systems to collect and analyze process and operating data. (K5)
3. Analysis and optimization of process and quality data using model-predictive controllers. (K6)
4. Application of communication standards such as EUROMAP 67 and OPC 40079 for data integration in production cells. (K4)
5. Identification of typical error types and frequencies during plastic component production and development of solutions. (K5)
6. Execution and analysis of case studies to illustrate theoretical content. (K5)
7. Application of the basics of data structuring and their significance for digital production. (K4)
8. Explanation of the structure and functionality of forming tools and their role in digital production. (K4)

• Brief introduction to the most important processes for manufacturing semi-finished and finished parts from plastics (injection molding, extrusion, thermoforming, blow molding) and structure of the manufacturing cells.
• Basic design and operation of molding tools
• Definition of cyber-physical systems (CPS, CPPS)
• Unique identifiability of the forming tool
• Sensors to detect the environmental conditions (ambient temperature, humidity ...)
• Sensors to detect the internal operating condition (structure-borne sound, ultrasound, cavity pressure, thermocouples, infrared, ...)
• Typical setting and process data and their data structure
• Common measurement data acquisition systems
• Interfaces and communication standards in the manufacturing cell (EUROMAP 67, OPC 40079)
• Model predictive process and quality controllers in plastics processing
• Typical defect types and frequencies during component manufacture
• Important requirements for and interfaces to Data Science
• Selected case studies