Experiments with machine vision for polymer flowability analysis in powder bed fusion

• Main Authors: Ituarte, Iñigo Flores, Huotilainen, Eero, Wiikinkoski, Olli, Tuomi, Jukka
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2018
• Subjects: Additive Manufacturing; Powder Bed Fusion; DRNTU::Engineering::Mechanical engineering::Prototyping
• Online Access: https://hdl.handle.net/10356/88587; http://hdl.handle.net/10220/45852
• Institution: Nanyang Technological University

This research explores the real-time process control of polymer flowability in Powder Bed Fusion (PBF). To do so, a novel system based on machine vision and an image-processing algorithm was developed and tested in an open hardware and software PBF system. The system has the ability to analyze the quality of the powder bed by computing a defect ratio of the powder bed after each recoating operation. Then, this ratio is used as a performance variable in three full factorial Design of Experiments (DOE). The results show that the installation of machine vision and image processing system can potentially provide a signal to repeat the recoating process and correct the defect on the powder bed. At the same time, recoating process parameters can be adjusted dynamically to guarantee an optimum quality of the powder bed and minimize possible build failures.