ArticleData in brief2025
Methodology and experimental data of geometry-dependent temperature-time-profiles in laser-based powder bed fusion of polyamide 12.
Article in Data in brief, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In laser-based powder bed fusion of plastics, the thermal history of the melt is essential to achieving high part properties. While the impact of part size on thermal behavior and properties is well understood, industrial processes frequently use fixed parameter sets, resulting in geometry-dependent variations in density and dimensional accuracy. This dataset documents the relationship between process parameters, thermal signals, and part properties resulting from laser processing. Experiments were carried out on an industrial system using polyamide 12 powder. A series of single-layer, cuboid, and tensile test samples with systematically varied energy input, scan speed, hatch distance, and scan vector length were produced under constant laser power. An infrared thermography camera monitored the temperature-time profiles during processing at 200 Hz. Characteristic features such as the peak of the thermal signal and decay time were extracted using automated data processing routines from these thermal signals. Complementary measurements include single-layer thicknesses, cuboid densities, dimensional accuracy measurements, and tensile testing. The thermal data are provided as raw, uncalibrated measurements, processed mean values, and standard deviations of extracted features for each sample. All data are openly available in standardized formats to facilitate reuse. Potential applications include predictive model development, benchmarking in situ monitoring methods, analyzing geometry-dependent parameter effects, and training adaptive control algorithms. A first analysis of this dataset is presented in a co-publication [1].
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