A Novel Spatter Detection Algorithm for Real-Time Quality Control in Laser-Directed Energy Deposition-Based Additive Manufacturing
Laser-Directed Energy Deposition (LDED) has recently been widely used for 3D-printing metal components and repairing high-value parts. One key performance indicator of the LDED process is represented by melt pool stability and spatter behavior. In this research study, an off-axis vision monitoring s...
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MDPI AG
2025-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/12/3610 |
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| author | Farzaneh Kaji Jinoop Arackal Narayanan Mark Zimny Ehsan Toyserkani |
| author_facet | Farzaneh Kaji Jinoop Arackal Narayanan Mark Zimny Ehsan Toyserkani |
| author_sort | Farzaneh Kaji |
| collection | DOAJ |
| description | Laser-Directed Energy Deposition (LDED) has recently been widely used for 3D-printing metal components and repairing high-value parts. One key performance indicator of the LDED process is represented by melt pool stability and spatter behavior. In this research study, an off-axis vision monitoring system is employed to characterize spatter formation based on different anomalies in the process. This study utilizes a 1 kW fiber laser-based LDED system equipped with a monochrome high-dynamic-range (HDR) vision camera and an SP700 Near-IR/UV Block visible bandpass filter positioned at various locations. To extract meaningful features from the original images, a novel image processing algorithm is developed to quantify spatter counts, orientation, area, and distance from the melt pool under harsh conditions. Additionally, this study analyzes the average number of spatters for different laser power settings, revealing a strong positive correlation. Validation experiments confirm over 93% detection accuracy, underscoring the robustness of the image processing pipeline. Furthermore, spatter detection is employed to assess the impact of spatter formation on deposition continuity. This research study provides a method for detecting spatters, correlating them with LDED process parameters, and predicting deposit quality. |
| format | Article |
| id | doaj-art-e18dc95745d84f1eacbfc8fa3900b4c6 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-e18dc95745d84f1eacbfc8fa3900b4c62025-08-20T03:27:39ZengMDPI AGSensors1424-82202025-06-012512361010.3390/s25123610A Novel Spatter Detection Algorithm for Real-Time Quality Control in Laser-Directed Energy Deposition-Based Additive ManufacturingFarzaneh Kaji0Jinoop Arackal Narayanan1Mark Zimny2Ehsan Toyserkani3Multi-Scale Additive Manufacturing Laboratory, Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON N2L 3G1, CanadaSchool of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough TS1 3BX, UKPromation, 2767 Brighton Rd, Oakville, ON L6H 6J4, CanadaMulti-Scale Additive Manufacturing Laboratory, Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON N2L 3G1, CanadaLaser-Directed Energy Deposition (LDED) has recently been widely used for 3D-printing metal components and repairing high-value parts. One key performance indicator of the LDED process is represented by melt pool stability and spatter behavior. In this research study, an off-axis vision monitoring system is employed to characterize spatter formation based on different anomalies in the process. This study utilizes a 1 kW fiber laser-based LDED system equipped with a monochrome high-dynamic-range (HDR) vision camera and an SP700 Near-IR/UV Block visible bandpass filter positioned at various locations. To extract meaningful features from the original images, a novel image processing algorithm is developed to quantify spatter counts, orientation, area, and distance from the melt pool under harsh conditions. Additionally, this study analyzes the average number of spatters for different laser power settings, revealing a strong positive correlation. Validation experiments confirm over 93% detection accuracy, underscoring the robustness of the image processing pipeline. Furthermore, spatter detection is employed to assess the impact of spatter formation on deposition continuity. This research study provides a method for detecting spatters, correlating them with LDED process parameters, and predicting deposit quality.https://www.mdpi.com/1424-8220/25/12/3610laser-directed energy depositionspatterprocess monitoringimage processingdefects |
| spellingShingle | Farzaneh Kaji Jinoop Arackal Narayanan Mark Zimny Ehsan Toyserkani A Novel Spatter Detection Algorithm for Real-Time Quality Control in Laser-Directed Energy Deposition-Based Additive Manufacturing Sensors laser-directed energy deposition spatter process monitoring image processing defects |
| title | A Novel Spatter Detection Algorithm for Real-Time Quality Control in Laser-Directed Energy Deposition-Based Additive Manufacturing |
| title_full | A Novel Spatter Detection Algorithm for Real-Time Quality Control in Laser-Directed Energy Deposition-Based Additive Manufacturing |
| title_fullStr | A Novel Spatter Detection Algorithm for Real-Time Quality Control in Laser-Directed Energy Deposition-Based Additive Manufacturing |
| title_full_unstemmed | A Novel Spatter Detection Algorithm for Real-Time Quality Control in Laser-Directed Energy Deposition-Based Additive Manufacturing |
| title_short | A Novel Spatter Detection Algorithm for Real-Time Quality Control in Laser-Directed Energy Deposition-Based Additive Manufacturing |
| title_sort | novel spatter detection algorithm for real time quality control in laser directed energy deposition based additive manufacturing |
| topic | laser-directed energy deposition spatter process monitoring image processing defects |
| url | https://www.mdpi.com/1424-8220/25/12/3610 |
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