Reconfigurable Manufacturing Systems (RMS) is a modern engineering technology addressing changes in manufactured products via rapid reconfiguration and improved flexibility of manufacturing systems – machines, controllers, design methods, and software modules. RMS consists of networked robotic machines working in collaboration with humans. To transition existing machine tools and industrial robots to RMS, it is necessary to enable self-awareness in these robots, namely, real-world situational awareness and a capability to adapt their modes of operation to the situation. Self-awareness is a computational capability for which this proposal provides estimation, planning, and control technology.
Briefly, the proposed technology relies on capturing user specifications in the form of Linear Temporal Logic (LTL) formulas. The proposed technology uses advanced graph search algorithms to find an optimal sequence of decisions or actions (namely, the shortest path) to satisfy these specifications, which can adapt quickly to changing requirements. The long-term project objective is to develop mathematical, algorithmic, and simulation tools to enable a networked collaborative self-aware robotic RMS solution to be prototyped at Warner-Robins Air Logistics Complex and later commercialized for deployment in other manufacturing facilities.
ALAE Solutions successfully completed the Phase I contract in support of a new sensor technology for tracking Air Force assets. The diverse team included the USAF, aircraft maintenance engineers, ALAE engineers, GSU research professors, and engineers from the CISCO RF Government group. During the Phase I feasibility evaluation, the team completed several important deliverables. These included research and selection of an RF tracking technology suitable for DoD installations, feasibility studies into range and suitability for the client use case, development of an RF simulation as a digital twin (DT) to optimize antenna placement, and market research toward potential end-users and other DoD gap needs. New RFID technologies were demonstrated to track assets over the entire flight line with minimal infrastructure. A prototype plan was identified and confirmed with the USAF sponsors and stakeholders for several receivers and tags to be deployed and evaluated in an operational scenario in a Phase II effort. We expect to research cybersecurity, cloud-based applications, and message integration with other systems as we move forward in Phase II, which was awarded on 08/05/2021, with further research and an improved performance prototype of the sensor technology with optional features.
Following a successful Phase II Air Force SBIR award, ALAE Solutions is now actively demonstrating the value of integrating Smart Manufacturing Technology (SMT) with the typical manufacturing process by developing, testing, and evaluating novel Predictive Maintenance and Digital Twin technologies. Working closely with researchers from Georgia Southern University we hope to ascertain that AF depot component inspection, repair, and quality control processes are operating reliably, efficiently, and expeditiously. Current AF depot maintenance practices are reactive and lack the tools to improve the assets’ readiness further. There is an urgent need to improve industrial processes and introduce analytical methods to produce a product that meets or exceeds Navy and Air Force requirements and specifications.
Many Condition Based Maintenance (CBM) technologies have failed to provide optimum maintenance strategies because of inadequate and poor choices for extracted features or Condition Indicators (CIs). ALAE Solutions proposes a dual approach Predictive Maintenance (PM) Paradigm to address critical shop components/systems’ on-process maintenance/systems in real-time. Our approach involves the utilization of Intelligent CBM to develop, test, and evaluate a dynamic Smart Predictive Maintenance (SPM) methodology for critical manufacturing systems supported by a novel Digital Twin framework to optimize the manufacturing system design, maintenance practices, and policies.
Data has become an essential part of new and “smart” technology insertion to the manufacturing floor in recent years. Most industrial processes are controlled by PLCs or computer control apparatus and are instrumented with sensors dedicated to the control activities. ALAE Solutions has taken advantage of these sensors and complemented them with devices specifically designed to monitor faults of failure modes (for example, accelerometers measuring vibration signals). By maximizing SMT use in sensing and data mining, ALAE Solutions is presently developing and demonstrating intelligent software technologies for CBM, advanced diagnostic, prognostic, real-time health monitoring, and remaining-useful-life (RUL) reporting. This will enable all information about the manufacturing process to be available when needed, where it is needed, and in the form that it is needed across entire manufacturing supply chains, complete product lifecycles, and multiple ALCs. The benefits to the AF depot at WR AFB and other ALCs and the maintenance community, in general, include continuous online health monitoring and assessment, optimum maintenance overhaul/repair schedules for equipment spare-parts/personnel/facilities/tools, maintenance cost reduction, improvement of equipment reliability/safety (avoiding catastrophic failures), optimum planning/scheduling for equipment and facilities and reduced maintenance induced failures.
Commercial applications span numerous industries, and broad interest is expected from governmental and private entities, within the U.S. and abroad concerned with the maintenance or production of complex end-items or systems. Potential beneficiaries include DoD and other governmental aircraft and other system maintainers, private manufacturers, and other professional services. Commercialization would increasingly extend to all sectors requiring highly adaptable, reconfigurable modes of industrial operation.
ALAE Solutions has demonstrated the functionality of the Reconfigurable Test Adapter (RTA) developed under a phase II Air Force (AF) SBIR, topic AF161-014. The RTA is an innovative solution to addresses the problem of costly, time consuming re-hosting of legacy test program sets (TPS) from legacy Automatic Test Systems (ATS) into modernized systems such as the AF Versatile Depot Automated Test Station (VDATS.)
Approximately 8,000 TPS are currently planned to be re-hosted on next generation systems and this product showcases our work related to obsolescence of legacy ATS. The goal of the project is to provide significant life cycle sustainment cost savings while increasing reliability and maintainability of the ATS used to repair/return critical avionics assets to warfighters. Shop through-put for UUT repair/calibration increases and, as a result of the improved accuracy of the test equipment, the MTBF increases giving the AF more flight time over UUT supported by the legacy ATS
This innovation will decrease total ownership cost (TOC) while concurrently increasing mission capable rates for Air Force Sustainment Command (AFSC) ATS. The TOC savings will be experienced by greatly decreasing Supply Chain Management (SCM) inventory costs, and decreasing USAF 402 EMXG and AFLCMC/WNA engineering costs by retiring legacy ATE when migrating to an approved DoD FoT such as VDATS. Legacy ATS are difficult to operate, costly to maintain, use more power and frequently are down for repair
Rehosting TPS is currently costly and requires advanced engineering knowledge of the UUT. The RTA would provide a significant cost & NRE time savings for any effort involving re-host to the VDATS….. or other approved DoD FoT platform.
Ezebuugo Nwaonumah joined the ALAE Solutions Team in 2020 to augment our effort to demonstrate Prognostic Health Management (PHM) technologies targeting Air Force depot machinery. Mr. Nwaonumah brings a masters in Mechatronics (with AI Robotics Research and PLC Projects) to the team and will play a major role in developing our Digital Twin Framework and realizing a systematic approach to Predictive Maintenance. Eze has relevant experience creating continuous improvement plans for CNC machines by applying FMEA and LEAN manufacturing standards to minimize machine part failure. His experience at Anthony Inc. where he designed CAD drawings and programmed CNC and product testers along with his research in wheeled mobile robots will underpin our current and future clients.
Air Logistics and Engineering (ALAE) Solutions is pleased to announce we have been selected for a Phase I Small Business Innovative Research (SBIR) award for the AF193-022 topic supporting the Air Force. We are working with the Aircraft Maintenance Group (AMXG) at Robins AFB to demonstrate a proof-of-concept location sensor with improved location detection range and accuracy. Our researchers will evaluate available Radio Frequency IDentification (RFID) technologies, demonstrate a location sensor technology, and develop an innovative locating solution to improve the Air Force tracking capabilities for Aircraft Ground Equipment.