Correlating Displacement Sensors and In-Situ Optical Imaging for the Layer Management in a Laser Powder Bed Fusion Process

Additive manufacturing (AM) allows for the creation of complex geometries that cannot be created with traditional manufacturing methods. The strategy of this project is to utilize various sensors in tandem with the camera available within the machine to manage the pertinent powder layer. This study was carried out by a group of four students in collaboration with the partner company. For this study, the on-machine camera has been used for in-process monitoring purposes. This involves capturing the data in the form of images and post-processing them. Two different methods - Mean Intensity and Machine Learning based - were explored. Gauge Repeatability and Reproducibility (GR&R) studies were conducted for the mean intensity-based method and the results showed that the process is repeatable across different setups. The laser triangulation sensor was used to correlate with the camera images. The second method, (Convolutional Neural Network (CNN) based machine learning model), classified the images with 94% accuracy. Both of these methods were deployed on the machine by creating a user-friendly interface.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Sabarad, Satvik Irappa

Advisor dc:contributor.advisor

• Hardt, David E.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/147574

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/147574