Key performance indexes for the evaluation of geometrical characteristics and subsurface defects through laser line monitoring of laser metal deposition process

• Published in: Optics and laser technology 2025-04, Vol.182, p.112085, Article 112085
• Main Authors: Latte, Marco, Mazzarisi, Marco, Guerra, Maria Grazia, Campanelli, Sabina Luisa, Galantucci, Luigi Maria
• Format: Article
• Language: English
• Subjects: Additive manufacturing; Image processing; Key performance index; Laser line; Laser metal deposition; Process monitoring
• ISSN: 0030-3992
• DOI: 10.1016/j.optlastec.2024.112085

•In-process monitoring is essential in Laser Metal Deposition processes.•The laser line scanning enables detailed reconstructions of deposited geometry.•Deep analysis of deposition was achieved by means of specifically tailored KPIs.•Geometrical model application on scan data allows the study of subsurface defects. Although additive manufacturing (AM) is experiencing a wide diffusion, several limitations persist in the fabrication of metal components, such as low productivity, poor dimensional accuracy, and uncertainty regarding the mechanical properties of the final parts. The main cause of these undesirable effects lies in the intrinsic complexity of the metal AM processes, such as Laser Metal Deposition (LMD). Therefore, accurate monitoring and optimization of process parameters are crucial to ensure the overall quality of the product. Nowadays, various optical methods for monitoring geometrical characteristics are under development. However, insufficient attention has been paid to the potential benefits of using Key Performance Indexes (KPIs) tailored for in-process monitoring of LMD. This paper deals with the evaluation of some KPIs computed utilizing data obtained from a prototype laser line scanner mounted on the deposition head. The system was used to scan AISI 316L monolayer samples produced by the LMD process. Ad-hoc image processing algorithms were employed to process the data, reconstruct the morphology of the component, and extract geometrical information from tracks and layers. Moreover, to assess the occurrence of subsurface defects not directly detectable by the scan, an innovative procedure for creating a geometrical model based on monitoring data was devised. This model derived fundamental KPIs capable of detecting inter-track porosity. Results were then validated through metallographic analyses. The study demonstrated the effectiveness of the proposed procedure in assessing process performance and detecting deposition defects arising from undesired variations in process conditions.