Production Management and Technology M-18

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Performance Metrics and Approximations for Container Terminal Horizontal Transport (LOGMS Data)
(2026-07-14)
Ilenburg, Simon
;
Möhle, Florian
;
Witte, Svenja
This dataset contains simulation-based performance data for seaport container terminals, focusing on the interaction between Quay Cranes and Terminal Trucks. It includes a variety of operational scenarios (Fixed vs. Combined allocation) and compares simulated productivity with analytical approximations. Key metrics include productivity, waiting times, and equipment utilization, providing a robust foundation for terminal logistics improvements and mathematical model validation.
Data Type: Simulation Data
23 19
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Tool Wear Experiment Dataset for the Evaluation of Sensor Informativeness in Longitudinal Turning (ExtraDrey)
(2026-06-02)
Schibsdat, Sebastian
This dataset was collected at the Institute of Production Management and Technology (TUHH IPMT) to investigate tool wear behaviour in CNC longitudinal turning and to evaluate the suitability of different in-process sensor modalities for predicting maximum flank wear using machine learning. Experiments were performed on two workpiece materials (C45+N, X5CrNi18-10) using indexable inserts. Process parameters were varied according to a Fractional Factorial Design, are fully documented in the accompanying metadata, and were held constant throughout the full life of each cutting edge until failure. For each 30-second cutting experiment, the following in-situ online measurements are available: three-axis cutting forces and tool-side acceleration; structure-borne acoustic emission; rake-face temperature. In-situ offline measurements, recorded at 30-second intervals, include scattered light from the workpiece surface (Aq and macro profile angle) and microscope images of the cutting edge from three perspectives — flank face (FF), rake face (RF), and cutting-edge top view (CE) — at standard 150× and optionally 100× magnification, together with surface heightmaps via focus variation microscopy. Segmentation masks identifying the flank wear zone are available for selected images. The maximum flank wear (VB_Max, µm) is provided as a quantitative wear label for each experiment, embedded in the sensor data files and listed in the accompanying documentation spreadsheet. It was creted through a machine vision programm. The dataset is suitable for training and benchmarking machine learning models for tool condition monitoring, multi-modal sensor fusion, and wear-progression modelling. The combination of synchronised time-series sensor signals with microscope images, surface heightmaps, and segmentation masks supports investigation of the individual and combined informativeness of different sensor types for wear prediction. The segmentation masks additionally enable computer vision approaches to automated wear measurement. The documented Fractional Factorial design allows statistical analysis of parameter effects on wear behaviour, and the sensor signals can be used to validate physics-based wear models.
Data Type: Experimental Data
557 766
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Dataset of Simulated and Approximated Logistic Operating Curves for Closed Queueing Systems at Seaport Container Terminals
(2026-02-20)
Möhle, Florian
This dataset comprises comprehensive simulation data and modeling results based on Logistic Operating Curves for closed queueing systems. It includes productivity data of ship-to-shore cranes as a function of the number of vehicles deployed in horizontal transport. The dataset combines discrete-event simulation results with values derived from approximation equations based on Logistic Operating Curves. By systematically comparing simulated performance indicators with the corresponding approximation results, the quality and accuracy of the analytical modeling approach can be evaluated. The primary purpose of this dataset is to provide a transparent and reproducible basis for assessing the applicability of Logistic Operating Curves to container terminal operations. In particular, it supports the analysis of how shared queueing concepts influence ship-to-shore crane productivity and overall terminal performance. The data belong to the publication “Logistic Operating Curves for Closed Queueing Systems: Enhancing Container Terminal Productivity through Shared Queues”, presented at the APMS 2026 Conference – Shaping the Future of Industry through Sustainable, Data-Driven, and Human-Centric Production Systems. The results originate from a research project funded by the German Research Foundation (DFG), project number 508626914.
Data Type: Simulation Data
71 53
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Experiment Data for the Publication: A Novel Data Concept for Cutting Processes through Comprehensive Experimental Setup enabling Grey-box Models
(2025-09-15)
Schibsdat, Sebastian
The dataset consists of several wear tests and is supplementary to the paper 'Special Issue Wear: A Novel Data Concept for Cutting Processes through Comprehensive Experimental Setup Enabling Grey-Box Models'. The experiments cover two different cutting edges of various indexable inserts, each of which was used with constant process parameters until failure. The following data is available as in-situ online measurements: Three-axis force and acceleration; structure-borne noise; and rake face temperature. In-situ offline data includes scattered light from the workpiece surface (Aq and macro profile angle) and microscope images of the cutting edge with extraced heightmap(clearance face, rake face and cutting edge).
Data Type: Experimental Data
229 219

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