GOALI: Improved Tool-Path Design to Reduce Assembly Costs of High-Speed-Machined Wrought and Additive Metal Parts

University Of Texas At Dallas, Richardson TX

Investigators

Abstract

This Grant Opportunity for Academic Liaison with Industry (GOALI) project provides the fundamental research needed to significantly improve the quality of aluminum parts used in aircraft production. These high-value parts are made by cutting complex shapes out of large blocks of aluminum on high-speed milling machines. Because all parts need to fit perfectly when assembled into an aircraft, it is critical that they have very precise dimensions. When parts fail to assemble correctly, significant delays and cost overruns are incurred by the aerospace industry because of re-work and scrap. Furthermore, the demand for more precise parts creates significant technical and economic burdens on the machine shops that make them. Therefore, this research project aims to create new high-speed machining techniques that improve part quality, raise production rates, and reduce material losses. In addition to helping the aerospace industry, machine shops in the United States will benefit from the project as they currently face costly trial-and-error when manufacturing aircraft parts. As such, this project directly aids in economic welfare and national security, since the aerospace and machining industries are strongly tied to both. Knowledge from the research will also help spur competitive new manufacturing, such as the machining of 3D-printed metal parts. The participation of diverse students in this project (including a disabled veteran) as well as the exposure of engineering education to teens with autism, will provide for valuable educational and societal impacts. The research goal is to investigate initial residual stress distributions in wrought and additively manufactured aluminum alloys, and how this knowledge can lead to new tool-path designs that improve dimensional tolerances for high-speed-machined parts. The technical approach involves combining inverse Cauchy stress prediction, machining-induced residual stress modeling, and residual stress measurements to determine whether initial residual stresses cause dimensional distortions in monolithic aluminum parts to a greater extent than the aggregated thermal, wear, and machining effects. Crack compliance tests, neutron diffraction, and high-density point-cloud geometry mapping of feature-rich monolithic parts will be used to identify residual stress patterns that regularly exist in wrought aluminum materials prior to machining. Knowledge of the residual stresses will be used to design new stress-compensated tool-paths for high-speed machining processes. Contributions of the research include: knowledge regarding residual stress patterns and their effect on geometric deviations during high-speed machining; new statistical inference techniques that combine inverse stress predictions with experimental measurements to characterize residual stresses as random fields; and methods to design new stress-compensated stochastic machine tool-path trajectories. Besides their significance in improving high-speed-machining operations, the developed research techniques will also be tested on emerging hybrid (additive/subtractive) manufacturing processes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.