Injection-Molded Polypropylene Parts: Experimental Process Dataset with Part Weights, Energy Consumption and CAD Geometry

Injection-Molded Polypropylene Parts: Experimental Process Dataset with Part Weights, Energy Consumption and CAD Geometry Overview This dataset contains experimental data from polypropylene parts produced by plastic injection molding under controlled variations of process parameters. The dataset is intended to support research in data-driven manufacturing, process modeling, surrogate modeling, multi-objective optimization, process variability analysis, energy efficiency, and quality assessment in plastic injection molding. For each injection batch, the dataset provides: Process parameters used during the injection cycle. Individual part-weight measurements for batch (5 injected parts by batch). Average part weight. Energy consumption. Cycle time. Qualitative viability label. CAD geometry of the injected parts and sprue system. The dataset includes one structured CSV dataset and a CAD file describing the injected part geometry together with the sprue. Associated publication. This dataset originates from the experimental campaign reported in the following peer-reviewed article, which should be consulted for the full description of the experimental setup, process variables, and methodology: Pérez-González, J., Sánchez-Calleja, I., Fernández-Gorgojo, A., Ferrero-Guillén, R., Martínez-Gutiérrez, A., & Díez-González, J. (in press). Robust Multi-Objective Optimization of Injection Molding Parameters: An Experimental SHAP-Guided Memetic NSGA-II Approach. The International Journal of Advanced Manufacturing Technology. DOI: [to be added upon publication]. Companion experimental work. The same 1000 injection batches and the same injected parts described in this dataset were also analyzed for surface defect characterization using robotic inspection and quantitative multidefect modeling. The corresponding image data (raw and labeled) and quantitative defect measurements are published in a separate Zenodo record associated with: Companion Zenodo dataset: 10.5281/zenodo.20322729 Sánchez-Calleja, I., Ferrero-Guillén, R., Martínez-Gutiérrez, A., Díez-González, J., & Perez, H. (in press). Explainable Closed-Loop Injection Molding Parameter Tuning via Robotic Inspection and Quantitative Multidefect Modeling. DOI: [to be added upon publication]. Both datasets share the same batch identifier (1–1000) and can be merged to obtain the complete experimental record — process parameters, weights, energy and cycle time on this side; surface defect quantification on the companion side. Users of this dataset are encouraged to read both articles before any reanalysis, and to consult the companion dataset when surface defect information is required. Experimental Setup Item Description Material SABIC 579S homopolymer polypropylene Manufacturing process Plastic injection molding with a MiniMat 60 injection machine, from Mateu & Solé Mold type Two-cavity multicavity mold Parts per injection cycle Two injected parts Part geometry Provided as CAD file CAD content Injected part geometry and sprue Dataset type Experimental process dataset The experiments were carried out using a two-cavity multicavity mold, producing two parts per injection cycle. The CAD file included in this dataset provides the geometry of the injected pieces together with the sprue, supporting interpretation, reuse, and potential simulation-based studies. Dataset Files /Dataset.csv/Parts_geometry.stp File Description Dataset.csv Unified process dataset containing injection molding parameters, individual part weights, average weight, energy consumption, cycle time, and viability label for each batch. Parts_geometry CAD model containing the injected part geometry and sprue (in mm). Experimental Design The dataset contains 1000 injection batches, each batch contains 5 injected parts. 3 prior parts were also injected and discarded before the 5 registered parts to account for process transitory stability. Batch range Design strategy Description 1–768 Design of Experiments (DoE) The dataset was constructed by varying the injection molding process parameters according to their predefined ranges and step values. 769–1000 Maximin sampling criterion Additional configurations were generated by maximizing the distance between nearest individuals. This strategy was used to avoid a perfectly regular grid of data and to improve the coverage of the process parameter space. The first part of the dataset follows a structured DoE based on the variation of the main injection molding parameters. The second part introduces additional parameter combinations selected using a maximin criterion, increasing the diversity of configurations and reducing the dependency on a perfectly squared parameter mesh. Unified Process Dataset The file Dataset.csv contains one row per injection batch. Each row includes the process configuration used during the injection cycle and the corresponding measured or derived process outputs. Structure of Dataset.csv Column Description Unit / Values batch Injection batch identifier - label_viability Qualitative viability label of the process/result:G: Feasable injected part, can contain defects.Y: Feasable injected part, but with very noticeable short shots.R: Unfeasible injected part, injection incomplete or too erratic. G,Y,R Tinj Injection temperature °C tinj Injection time s Pinj Injection pressure bar Ph Holding pressure bar Bp Back pressure bar th Holding time s P1_weight Individual weight measurement of part 1 g P2_weight Individual weight measurement of part 2 g P3_weight Individual weight measurement of part 3 g P4_weight Individual weight measurement of part 4 g P5_weight Individual weight measurement of part 5 g weight_avg Average part weight associated with the batch g energy Energy consumption associated with the batch Wh cycle_time Injection molding cycle time s Data Description The dataset combines process inputs and manufacturing outputs. Process Parameters Parameter Description Range of Values Step Value* Tinj Injection temperature [180-240] ºC 20ºC tinj Injection time [0.5 - 2] s 0.5 s Pinj Injection pressure [5 - 35] bar 10 bar Ph Holding pressure [5 - 35] bar 15 bar Bp Back pressure [15 - 30] bar 15 bar th Holding time [4 - 6] s 2 s *Step Values corresponding to the first 768 batches. Process Outputs Output Description Unit / Values P1_weight-P5_weight Individual part-weight measurements for the batch g weight_avg Average part weight computed from the available valid measurements g energy Energy consumption associated with the injection batch Wh cycle_time Total cycle time of the injection process s label_viability Qualitative viability label describing the process/result condition G,Y,R CAD Geometry The dataset includes a CAD file describing the injected geometry. Item Description Geometry type Injected polypropylene parts Included geometry Injected pieces and sprue Mold configuration Two-cavity multicavity mold Production output Two parts per injection cycle Purpose Geometry documentation, interpretation, reuse, and simulation support The CAD geometry allows users to relate the experimental process data to the physical shape of the injected component and the feeding system used during the injection process. Intended Use This dataset is suitable for: Data-driven modeling of plastic injection molding processes. Surrogate model development and validation. Multi-objective optimization of injection molding parameters. Analysis of part weight, energy consumption, and cycle time. Study of process variability and robustness. Quality assessment in injection-molded polypropylene parts. Benchmarking of machine learning and explainable AI methods in manufacturing. Educational and research purposes in manufacturing, polymer processing, and smart manufacturing. Citation If you use this dataset, please cite it as: Pérez-González, J., Sánchez-Calleja, I., Fernandez-Gorgojo, A., Ferrero-Guillén, R., Martínez-Gutiérrez, A., & Díez-González, J. (2026). Injection-Molded Polypropylene Parts: Experimental Process Dataset with Part Weights, Energy Consumption and CAD Geometry [Data set]. Zenodo. https://doi.org/10.5281/zenodo.20309380 License This dataset is released under the Creative Commons Attribution 4.0 International License. CC BY 4.0 Contact For questions about the dataset, please contact the corresponding author. Corresponding author: Jesús Pérez-GonzálezEmail: [e-mail]: 0009-0007-7683-6401 Funding This work was partially supported by the Spanish Research Agency (AEI) under grant number PID2023-153047OB100, by the Department of Education of the Regional Government of Castile and León, and the Universidad de León. The author Iván Sánchez-Calleja acknowledges funding for doctoral studies of the University of León.