- Titel
- Development of Intelligent Process Automation Strategies to Enhance Selective Laser Melting Performance and Resource Efficiency
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:l189-qucosa2-1027694
- Konferenz
- 22. AALE-Konferenz. Rosenheim, 25. Februar - 27. Februar 2026
- Quellenangabe
- Tagungsband AALE 2026
; Mechatronische Systeme für die Automatisierung: Fortschritt durch Präzision und Qualität
Herausgeber: Hochschule für Technik, Wirtschaft und Kultur Leipzig
Erscheinungsort: Leipzig
Erscheinungsjahr: 2026
Seiten: 299-307
ISBN: 978-3-910103-08-5 - Erstveröffentlichung
- 2026
- DOI
- https://doi.org/10.33968/2026.29
- Abstract (EN)
- The research project FASER advances additive manufacturing processes for metallic materials, focusing on Selective Laser Melting (SLM). FASER stands for “Fehlerfreies Additives Fertigen durch adaptive Sensorik zur Optimierung der Energie- und Ressourceneffizienz” (Faultless Additive Manufacturing through Adaptive Sensor Technology for the Optimisation of Energy and Resource Efficiency). Conducted at the Technical University of Applied Sciences Wildau, it implements a comprehensive zero-defect strategy using market-standard equipment like the One Click Metal MPrint+ SLM machine and MPure powder removal station. These systems are enhanced with adaptive multi-sensor technology and intelligent process control to boost quality, cut energy use, and minimize material waste. Central to FASER is flexible camera-based multi-sensor integration, combining optical, thermographic, and spectroscopic methods for real-time monitoring in harsh SLM environments with dust, vapors, and emissions. Recent studies highlight the industrial potential of miniaturized optical systems paired with robust Precision Time Protocol (PTP) synchronization for precise data alignment. Highresolution thermal imaging and surface inspections reveal correlations between temperature gradients and particle adhesion, enabling continuous process oversight. [1, 2]. Multi-sensor integration enables continuous process monitoring by combining high-resolution thermal imaging with surface inspection. This approach reveals correlations between temperature gradients and particle adhesion [3]. The recorded data are processed in real time using image analysis and computer vision methods. Deep learning then provides robust feature extraction, for example through edge detection and class weighting in image classification tasks [4–6].
- Freie Schlagwörter (DE)
- 22. Konferenz für Angewandte Automatisierungstechnik in Lehre und Entwicklung an Hochschulen (AALE)
- Freie Schlagwörter (EN)
- Additive Manufacturing, Adaptive Sensor Technology, Selective Laser Melting, Process Monitoring, Resource Efficiency
- Herausgeber (Institution)
- Hochschule für Technik, Wirtschaft und Kultur Leipzig, Leipzig
- Version / Begutachtungsstatus
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:l189-qucosa2-1027694
- Veröffentlichungsdatum Qucosa
- 03.03.2026
- Dokumenttyp
- Konferenzbeitrag
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
CC BY 4.0