1501 32 18790 3 74627 document 18790 Spectroscopy_Sensor_Filament_Dataset.zip application/zip dde11332ea261dbb7fe7445d74cd65cf MD5 304223 2025-02-28 09:37:58 https://researchdata.bath.ac.uk/1501/1/Spectroscopy_Sensor_Filament_Dataset.zip 1501 1 1 application/zip other The folder contains three subfolders, each named after a sensor: AS72651, AS72652, and AS72653. Inside each of these subfolders, there are three additional folders corresponding to different measurement distances: 12mm, 16mm, and 20mm. The collected data for each filament is stored within these distance-specific folders. en public cc_by

Spectroscopy_Sensor_Filament_Dataset.zip

data archive 10752 disk0/00/00/15/01 2025-03-03 08:16:22 2025-03-08 05:53:27 2025-03-03 08:16:22 data_collection show Al Gorkem Anil G.Al@bath.ac.uk 0000-0003-4098-6065 University of Bath TRUE FB0010 dept_elec_eng filament recognition, machine learning, autonomous additive manufacturing, spectroscopy sensor The file contains data collected from three sensors: AS72651, AS72652, and AS72653. Each of these sensor-specific files includes three folders representing different measurement distances: 12mm, 16mm, and 20mm. These folders contain data collected from 12 different filaments. Additionally, there is data recorded without a filament, labeled as "no_object." The following example of organizational structure is consistent across all filament data. Folder : AS72651 ->12mm->first measurement: abs_carbonfiber1.csv ->second measurement: abs_carbonfiber2.csv ->third measurement: abs_carbonfiber3.csv ->three measurements together: abs_carbonfiber.csv Each collected dataset is stored in CSV format. The data can be utilized for filament recognition using machine learning models, enabling the identification of different filament types based on sensor measurements. 2025-03-03 University of Bath public RightsHolder University of Bath Engineering and Physical Sciences Research Council https://doi.org/10.13039/501100000266 EP/V051083/1 Manufacturing in Hospital: BioMed 4.0 cent_dmade The methodology is described in the associated paper. en 1 10.15125/BATH-01501 https://doi.org/10.3390/s25051543 pub open Dataset