A Novel Spatter Detection Algorithm for Real-Time Quality Control in Laser-Directed Energy Deposition-Based Additive...

Area of Science:

• Additive Manufacturing
• Materials Science
• Process Monitoring

Background:

• Laser-Directed Energy Deposition (LDED) is crucial for 3D metal printing and part repair.
• Melt pool stability and spatter behavior are key performance indicators in LDED.
• Effective monitoring is needed to ensure high-quality deposition.

Purpose of the Study:

• To develop and validate an image processing system for characterizing spatter formation in LDED.
• To correlate spatter characteristics with LDED process parameters.
• To assess the impact of spatters on deposition continuity and predict part quality.

Main Methods:

• An off-axis vision monitoring system with a high-dynamic-range camera and bandpass filter was used.
• A novel image processing algorithm quantified spatter count, orientation, area, and distance.
• Experiments were conducted on a 1 kW fiber laser-based LDED system.

Main Results:

• The image processing algorithm achieved over 93% detection accuracy in harsh conditions.
• A strong positive correlation was found between laser power and average spatter count.
• Spatter detection was successfully used to evaluate deposition continuity.

Conclusions:

• The developed vision system and image processing pipeline provide a robust method for monitoring LDED.
• This approach enables the correlation of spatter formation with process parameters.
• It offers a pathway for predicting and improving the quality of 3D-printed metal components.