Additive Manufacturing (AM) or 3D Printing has emerged as a promising manufacturing technology due to its advantages such as offering design freedom, accelerated time-to-market, and reduced material wastage as compared to conventional subtractive processes.
Nevertheless, a key challenge in AM is to ensure that parts built are consistent and meet quality requirements. Inconsistencies in AM-built parts, coupled with the fact that current non-destructive evaluation (NDE) methods are not optimized for AM processes, have hindered the widespread adoption of AM, especially in industries where product certification is crucial.
This invention is a monitoring system and defect detection tool for real time and in-situ inspection of the powder bed fusion (PBF) AM process.
The technology owner is seeking collaborations with industrial partners, particularly with AM service bureaus or AM system operators for testing and prototype development.
The technology employs the combination of sensor fusion methodology using an optical camera and an infrared camera, providing a multi-control approach for real-time quality control of the PBF process. The defect detection tool is equipped with a deep learning capabilities using convolutional neural networks for real time defect recognition, defect classification as well as establishing the relationships between defect signatures with quality-control metric of the fabricated products.
Compared to other process monitoring solutions, this technology monitors a field of view beyond just the melt pool, and offers
The technology owner provides to collaborators the installation and calibration of the in-situ monitoring system, as well as a licensable software for quality management of the PBF products.
The technology is ideal for operators of powder bed fusion additive manufacturing systems (e.g. service bureaus, equipment owners).
This technology will serve as the basis for standardization and certification of AM products.
The Global Additive Manufacturing market, including hardware software, materials and services, stands at $9.3 billion in 2018, and is projected to reach $41.6 billion in 2027. Printing large components like propellers can take days or even weeks to complete. If anomalies occurs during the printing process affecting part quality, the whole part may have to be discarded. According to published cost models, the risk related cost due to build failure is the second largest cost, occupying 26% of the total unit cost. This highlights that process instability can severely affect the overall value proposition of AM. This technology thus has the potential to minimize risk-related costs and significantly reduce the production costs of the PBF process.