SBIR-STTR Award

Direct to Phase II

Improvements in depot-level manufacturing (components, machines, controls, software) for the Warner-Robins Air Logistics Complex (WR-ALC) processes can accelerate throughput and reduce delays. In line with the LSE 2040 Attribute 4. Efficient Depot Robins, USAF has invested significantly in modern machinery to enhance capabilities and operations of depot maintenance shops increase efficiency and readiness. This modern machinery is increasingly complex, requiring regular and unscheduled maintenance, troubleshooting, and repair, without which the machinery can interrupt production. When facing an unfamiliar repair, maintainers rely on traditional maintenance resources, which use 2D images and text to convey increasingly complicated instructions and procedures. These obsolete resources are prone to errors as maintainers interpret static, 2D images and instructions to identify and understand corresponding 3D machinery components. Reducing equipment failures and mishaps that ultimately delay aircraft maintenance requires advanced tools, machinery, knowledge, and skills to solve maintenance issues. A depot’s advanced machinery is only effective when in working order and only remains in working order when maintainers have the tools, knowledge, and skill to quickly and efficiently maintain, troubleshoot, and repair equipment. Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), collectively Extended Reality (XR), for maintenance enables users to interact with machinery and subcomponent instructions and information by utilizing 3D overlays on an object to guide users through maintenance and repair procedures as if they are a SME. Our solution seeks to apply step-by-step AR training and job execution aids to maintain machinery required within manufacturing processes at WR-ALC to swiftly transition to a highly efficient maintenance process. We propose the development of an on-demand, hardware-agnostic AR training and task execution job aid that improves efficiency, reduces error rates and increases the accuracy of depot-level machinery maintenance. The long-term (over Phase II and III) solution aims to utilize AR to transform existing “paper” technical manuals into interactive 3D experiences with a myriad of prompts, graphics, animations, and overlays on actual machinery. The key result will include a prototype Simplified Intelligent Augmented Reality for Machinery Maintenance or SIA-MM training tool with multi-mode capability: AR solution functions to both walk users through actual maintenance task execution or as initial and refresher training. First, these will include Real Environment AR (On-site): AR overlay procedures on actual machinery and equipment. Second, Open Space AR (Training Mode): Uses virtually generated 3D machinery images to instruct/refresh users on procedures before execution.