Tapping artificial intelligence to improve materials engineering

CHALLENGE & OPPORTUNITY

The most important considerations in manufacturing elements for the aerospace and space industries are always cost, weight and reliability. Many parts are made using a process called metal additive manufacturing, a relatively new, largely digital method of automated metal part manufacturing using metal powder or metal wire as raw material. While the process provides more complex parts and significantly reduces material waste, production steps, on-hand inventory and the amount of distinct parts needed for an assembly, sample parts must be routinely inspected for quality under a microscope or using X-ray Computed Tomography. This is tedious, time-consuming work.

UB SUPPORT

UB research experts and assistants partnered with the company on multiple R&D projects to address a substantial need in additive design and manufacturing processes by developing a novel fabrication feasibility framework. Five categories of assessments were used which qualitatively and quantitatively evaluated fabrication feasibility for additive manufacturing. Now, the capability of a part to be 3D printed is determined prior to the actual printing. Such an analysis can support rapid assessments of whether or not to select additive manufacturing as the fabrication strategy. As a result, it can lead to a reduction in errors and printing failures and also avoid ineffective use of available resources. The company is now working on implementing these image recognition activities into several active projects where convolutional neural network-based inspection of metal additive manufacturing parts is present. Joint efforts continue towards further development of the software, including the development of a metal predictive model, machine learning to facilitate geometric re-design and more comprehensive build orientation. Moog has hired hundreds of UB engineering graduates and interns to date.