Cross-process-chain dataset archive: Combined data collection from injection molding and screw driving

Industrial Cross-Process-Chain Datasets This dataset combines time-series data from injection molding and screw driving operations in plastic component assembly. It enables research in cross-process quality analysis, material impact studies, and predictive modeling by providing synchronized data from multiple manufacturing stages. A wide range of error configurations or process variations where introduced to the data (`class_values`) to allow for complex analysis tasks. Overview The dataset captures complete manufacturing chains where injection-molded plastic components are subsequently assembled using screw driving operations. Each experiment (identified by `upper_workpiece_id`) contains synchronized measurements from four process streams, enabling analysis of how upstream process parameters affect downstream assembly quality. Process Format Measurements Purpose Upper workpiece injection molding CSV Pressure (target and actual), velocity, volume, [state] Primary component fabrication Lower workpiece injection molding TXT Pressure (target and actual), velocity, volume Secondary component fabrication Left position screw driving JSON Torque, angle, gradient Assembly operation (left screw) Right position screw driving JSON Torque, angle, gradient Assembly operation (right screw) Experimental Setup Injection Molding: Material: Thermoplastic with varying recyclate and glass fiber content Process monitoring: Pressure, velocity, volume measurements Sampling frequency: ~1 kHz Cycle time: Variable (2-8 seconds typical) Screw Driving: Delta PT 40x12 screws optimized for thermoplastics Target torque: 1.4 Nm (range: 1.2-1.6 Nm) Sampling frequency: 833.33 Hz Two screws per assembly (left and right positions) Dataset Features Each experiment includes: Static data: Process parameters, quality metrics, timestamps, material classifications Time series data: Complete process recordings from all four streams Metadata: Experiment IDs linking all process stages Quality labels: Classification by material composition and process conditions Acknowledgments These datasets were collected and prepared by: RIF Institute for Research and Transfer e.V. Universität Kassel, Institute of Materials Engineering Technical University Dortmund, Institute for Production Systems The preparation and provision of the research was supported by: German Ministry of Education and Research (BMBF) European Union's "NextGenerationEU" program The research is part of this funding program More information regarding the research project is available here