Dataset for "Filament Type Recognition for Additive Manufacturing Using a Spectroscopy Sensor and Machine Learning"
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.
Cite this dataset as:
Al, G.,
2025.
Dataset for "Filament Type Recognition for Additive Manufacturing Using a Spectroscopy Sensor and Machine Learning".
Bath: University of Bath Research Data Archive.
Available from: https://doi.org/10.15125/BATH-01501.
Export
Data
Spectroscopy … Filament_Dataset.zip
application/zip (304kB)
Creative Commons: Attribution 4.0
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.
Creators
Gorkem Anil Al
University of Bath
Contributors
University of Bath
Rights Holder
Documentation
Data collection method:
The methodology is described in the associated paper.
Funders
Engineering and Physical Sciences Research Council
https://doi.org/10.13039/501100000266
Manufacturing in Hospital: BioMed 4.0
EP/V051083/1
Publication details
Publication date: 3 March 2025
by: University of Bath
Version: 1
DOI: https://doi.org/10.15125/BATH-01501
URL for this record: https://researchdata.bath.ac.uk/1501
Related papers and books
Al, G. A., and Martinez-Hernandez, U., 2025. Filament Type Recognition for Additive Manufacturing Using a Spectroscopy Sensor and Machine Learning. Sensors, 25(5), 1543. Available from: https://doi.org/10.3390/s25051543.
Contact information
Please contact the Research Data Service in the first instance for all matters concerning this item.
Contact person: Gorkem Anil Al
Faculty of Engineering & Design
Electronic & Electrical Engineering
Research Centres & Institutes
Centre for Digital, Manufacturing & Design (dMaDe)
