Description:
OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Advanced Materials
OBJECTIVE: Design and develop innovative manufacturing and inspection processes that leverage the tenets of Digital Thread and the Model-Based Enterprise (MBE) to enable a Digital Transformation within the Department of Defense (DoD).
DESCRIPTION: Model-Based Definition (MBD) utilizes 3D datasets to contain and convey a product’s definition during the manufacturing process. The larger MBE can leverage this data in downstream processes such as production, quality assurance, and logistics to consume part-specific manufacturing information in new, innovative ways. Through a previous research effort, NAVAIR developed a custom workflow for MBD parts to tie manufacture and inspection data to the part model using the Quality Information Framework (QIF) Standard. MBD has also been leveraged in industry to analyze measurement uncertainty associated with Coordinate Measurement Machines when creating part inspection plans. Through QIF, all inspection data can be associated back to the model and utilized by logistics throughout the sustainment phase of the part's lifecycle. NAVAIR identified a number of capability gaps while developing the above workflow, some unique to the defense industry. The intent of this effort is to address the capability gaps identified for the current workflow.
There are a number of factors that impact the accuracy of a measurement such as the environment in which the measurement is taken, the system taking the measurement (such as a Coordinate Measurement Machine [CMM]), and the way the dimension was defined in the Technical Data Package. The combination of these factors contribute to the uncertainty associated with each measurement. Measurement uncertainty leverages guard banding rules to restrict the tolerance range to minimize the potential to accept "bad" parts or reject otherwise "good" parts. These limits are often based on the cost implications associated with those errors. However, any deviation from the technical requirements of a Critical Safety Item (CSI) could result in loss of life or loss of aircraft. The consequence of failure for a CSI is so much greater than the cost to produce the individual part that traditional guard banding rules do not apply. The Navy has a specific need to develop a unique set of guard banding rules and measurement uncertainty principles based on part criticality as opposed to cost.
Non-contact Articulating Arms (such as a Romer Arm) have the ability to generate point cloud data quicker than contact CMMs. The point cloud data can produce valuable quality information and help augment the workload of a CMM, a bottleneck in the Organic Industrial Base (OIB). However, the OIB does not currently leverage articulating arms as inspection tools, because the measurement uncertainty is not well quantified. This effort aims to quantify the measurement uncertainty of non-contact articulating arms for inspection purposes.
The Navy has the means to calculate measurement uncertainty for CMM inspection plans. Current techniques leverage an initial condition for the inspection plan, which requires input from the CMM operator. The CMM operator currently needs to manually add/remove inspection points to find an optimized inspection plan that meets the measurement uncertainty requirements. The downside to this approach is that it is unclear whether a local or global optimization has been achieved with respect to the time and cost required to perform the inspection. The Navy is seeking a tool that can automatically optimize the inspection plan for time and cost while maintaining the required measurement uncertainty.
The goal of this effort is to modify the previously developed workflow, based on the outcome of the above objectives. Currently, there is an abundance of applications and file exchanges/handoffs. This effort will integrate the various operations into one Digital Enterprise Tool, such as DEXcenter, where various workflows could be exercised to support functionality at the enterprise-level. This effort will focus on integrating this new workflow into a Digital Enterprise Tool that the OIB can leverage.
PHASE I: Phase I will focus on addressing the previously identified capability gaps in the current workflow. This includes, but is not limited to, the development of new guard banding rules based on part criticality, measurement uncertainty principles for articulating arms, and a tool to optimize inspection plans for time and cost based on the measurement uncertainty requirements. Demonstrate the feasibility of a tool or set of tools that can address the above capability gaps in a lab environment. A lab environment may leverage a test artifact with controlled model based technical requirements captured in the QIF format to evaluate the tool’s performance. The Phase I effort will include prototype plans to be developed under Phase II.
PHASE II: Develop a new process workflow for the OIB that leverages the solutions developed in Phase I. This workflow shall integrate with existing manufacturing practices to reduce any burden associated with deployment of MBE to the OIB. It will also consist of the re-packaging and deployment of the new workflow to run directly on Navy databases. Phase II of this effort will integrate the various operations into one Digital Enterprise Tool. Once deployed, demonstration and validation will be performed using actual Navy data in prototype manufacturing environment.
PHASE III DUAL USE APPLICATIONS: To demonstrate the developed capability, the tool will be leveraged on production parts to fully characterize the measurement uncertainty of that inspection plan. The new capability should minimize any unique modifications of the part to complete the analysis in a production environment. Once complete, the tool will be transitioned for ownership by NAVAIR under the guidance of PEO-CS Digital Thread Team and/or NAWCAD LKE’s Digital Enterprise Tools Branch.
There are many industries outside of the Navy including, but not limited to, the medical field and the aerospace industry that produce critical parts where the consequence of failure cannot be easily quantified by cost. Those industries would benefit from criticality-based guard banding rules.
Manufacturers that produce a high quantity of a particular component will benefit from even a small reduction in the time it takes to perform an inspection. Specialized, expensive manufacturing techniques like a CMM can negatively impact the inspection process. Nonorganic manufacturing facilities would also benefit from quicker, cheaper, optimized inspection plans.
REFERENCES:
1. Taylor, B. N., & Kuyatt, C. E. (1994). NIST Technical Note 1297: Guidelines for evaluating and expressing the uncertainty of NIST measurement results. NIST. https://www.nist.gov/pml/nist-technical-note-1297
2. Working Group 1. (2008). JCGM 100: 2008: Evaluation of measurement data – Guide to the expression of uncertainty in measurement. Joint Committee for Guides in Metrology. https://www.bipm.org/documents/20126/2071204/JCGM_100_2008_E.pdf/cb0ef43f-baa5-11cf-3f85-4dcd86f77bd6
KEYWORDS: Model-Based Definition; Digital Thread; Measurement Uncertainty; Guard Banding; Manufacturing; Coordinate Measurement Machines
