Re: Sinclair CFO-20N Service Manual
Kian Trip
NOTE: The Solicitations and topics listed on this site are copies from the various SBIR agency solicitations and are not necessarily the latest and most up-to-date. For this reason, you should use the agency link listed below which will take you directly to the appropriate agency server where you can read the official version of this solicitation and download the appropriate forms and rules.
OBJECTIVE: Develop the ability to produce optimized (strength/stiffness/weight) part geometry, using lathes and milling machines as constraints, to feed Computer Aided Manufacturing (CAM) software for machining centers.
DESCRIPTION: Currently, optimization software develops a mesh-based output for optimized parts. The user inputs the various parameters (required strength, stiffness, or weight) and the optimization code calculates the topology to meet the user requirements. This mesh-based output is not generally in a format directly usable to create a part by either additive manufacturing or subtractive manufacturing. For additive manufacturing, a second software suite is needed to process the mesh-based output into usable format to produce the part. The mesh-based output is unusable for subtractive manufacturing without significant engineering input.Additively manufactured components have an advantage of being able to be created in complex shapes, which are unable to be made using subtractive methods. However, the use of multi-axis numerical control subtractive manufacturing machines allows similarly complex shapes to be created. The issue for subtractive based manufacturing centers around tool-path and access (e.g., can the tool get into a space and move in the same space).The disadvantage of additive manufacturing is that both the process and material must be qualified and tested together in order to provide sufficient properties to be evaluated for airworthiness. Unlike additive manufacturing, components manufactured by subtractive manufacturing can be evaluated for airworthiness quickly by analysis. Analysis of subtractive manufactured components requires material properties from the manufacturer and part geometry in order to be evaluated for airworthiness.This SBIR topic seeks to combine the strengths of material qualification associated with subtractive manufacturing and the benefits of optimization software to provide the best possible parts in the least amount of time. To accomplish these goals, the Navy seeks the development of a software package that performs optimization for strength, stiffness, and weight as goals while using machinability as a constraint. The output from the Computer Aided Design (CAD) in the form of a common platform independent file type (e.g., Parasolid, Standard for the Exchange of Product model data (STEP), Initial Graphics Exchange Specification (IGES), or ACIS). The output geometry should be optimized for the chosen objective and be machinable by multi-axis mill and/or lathe.
PHASE I: Design and develop a software to analyze/optimize a component for a particular objective (e.g. strength or stiffness or weight). Demonstrate the feasibility of the software to constrain the analysis/optimization using a multi-axis subtractive machine as a constraint (i.e. the component must manufactured on a multi-axis mill or lathe). The Phase I will include prototype plans to be developed in Phase II.
PHASE II: Build and demonstrate a prototype inspection device, and any interfacing electronics, to inspect the CDP. Final demonstration will be in a test environment representative of the CDP aboard ship.
PHASE III: Finalize a prototype for robustness and shock testing [Ref 2]. Test the prototype at Naval Air Warfare Center Aircraft Division, Lakehurst, New Jersey.Transition to appropriate end users.Wire rope has a wide range of applications in industry, including bridges, elevators, cranes, overhead hoists, ski-lifts, ship moorings and off-shore oil rigs. Broken wire count is a standard method for determining when to replace cables in everything from cranes to winches, so a method of easily identifying broken wires could be beneficial in many non-naval applications.
OBJECTIVE: Develop better and more robust automatic target classifiers capable of providing improved accuracy, identification, and classification of complex or subtle dynamics by leveraging advanced mathematical and machine learning tools.
DESCRIPTION: Current tactical platforms are challenged when it comes to target identification and classification algorithm development. They are unlikely to routinely encounter more complex dynamics of targets of interest and when they do, the raw data is not likely to be recorded. Therefore, data from other collection systems and/or computer models must be used to model and simulate the dynamics and build the required algorithms. The advancement of powerful super computers has made near-real physical modeling possible [Ref. 1], allowing modeling of almost any target with its environment and achieving very good agreement between models and observations. It is important to note though, some approximations are usually required but those terms are generally small and are usually considered insignificant.Advanced mathematical and machine learning techniques may be used to resolve this apparent paradox between exploiting a high-dimensional feature space with data intensive machine learning and a lack of understanding of the underlying dynamics. With this approach, one could build and train equivalently effective algorithms with built-in physics, i.e., coupled non-linear differential expressions, to ensure the algorithms are robust. Finally, the learned physics-based models could be used to extend accurate classification to other objects of similar class using sparsely sampled data, computer models, and scaled model data.Machine learning techniques, e.g., Support Vector Machines (SVM), Dynamic Mode Decomposition (DMD) [Ref. 5], Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNN) or Convolutional Neural Networks (CNN), are effective at picking up and exploiting small differences in data, especially for spatiotemporal coupled systems where the feature space is very large in higher order dimensions. As a result, improved performance can be achieved with access to higher dimensional data with finer temporal resolution and higher fidelity. Getting this data can be difficult for a tactical platform and using traditional computer modeling may not be sufficient due to its data approximations. However, scaled model data might be used to better capture the underlying dynamics and provide a critical element for the advancement of machine learning algorithms. Scale modeling cannot be a complete alternative and may be dismissed in the development and test of classification systems because of the expense when scaling to large class.One other important consideration when using machine learning algorithms are generalization errors or systematic biases. Because these algorithms are sensitive to high dimensional features, they can often key on intangible artifacts like non-real sensor phenomenon or peculiarities present in the data collection. The traditional black box approach sometimes makes it difficult to detect or completely eliminate these types of errors; but all attempts must be made to do so. One way to do this is to ensure the algorithms are grounded in a priori knowledge of physical laws. As with human intelligence, machine intelligence must also be confined to the realm of reality.Recent mathematical tools have been developed that might be leveraged to resolve the apparent paradox of capturing the desired level of complexity in a machine learning algorithm and knowledge of the underlying physical mechanism it is exploiting. Examples of methods or techniques that may provide the desired results include the work by Raissi et al. [Refs. 2, 3], which has demonstrated the ability to translate noisy observations in space and time into non-linear partial differential equations. This was done by embedding a deep hidden physics layer in a Neural Network; it is able to learn the underlying dynamics during training [Ref. 2]. The resulting Neural Networks form the basis for new classes of algorithms with a priori built in knowledge of the underlying physical laws [Ref. 3]. This could allow better and more robust extrapolation to other objects within the same spatiotemporal framework using limited observations and/or augmented with computer and scaled model data. Another example of a technique used for complex dynamics is Dynamic Mode Decomposition [Ref. 5], which have shown the capability to extract governing equations of a dynamic system from sensor and image data collected on that system.Combining new mathematical tools, hidden physics layers, scaled and computer models, and sparse observational data, it should be possible to build better and more robust intelligent machine learning algorithms. These new systems could process higher-dimensional input data at the same speeds or faster to achieve reduced missed identification or classification and increased correct identification and classification performance all the while providing higher confidence in those decisions. Existing data fusion metrics from Single Integrated Air Picture (SIAP) [Ref. 6] or the popular Stone Soup metrics package can be used to assess accuracy in identification and classification against existing systems as a baseline.Work produced in Phase II may become classified. Note: The prospective contractor(s) must be U.S. owned and operated with no foreign influence as defined by DoD 5220.22-M, National Industrial Security Program Operating Manual, unless acceptable mitigating procedures can and have been implemented and approved by the Defense Counterintelligence and Security Agency (DCSA). The selected contractor and/or subcontractor must be able to acquire and maintain a secret level facility and Personnel Security Clearances. This will allow contractor personnel to perform on advanced phases of this project as set forth by DCSA and NAVAIR in order to gain access to classified information pertaining to the national defense of the United States and its allies; this will be an inherent requirement. The selected company will be required to safeguard classified material IAW DoD 5220.22-M during the advanced phases of this contract.
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