Developing Methods for Engineering Process Metrics Capture and Analysis

Symposium PresentationApproved for public release; distribution is unlimited

Instrumenting the Acquisition Design Process: Developing Methods for Engineering Process Metrics Capture and Analysis

Excerpt from the Proceedings of the Nineteenth Annual Acquisition Research SymposiumThere is a deficit of data on the detailed execution of design acquisition processes, data which is needed to truly understand and improve them. Simultaneously, the movement to digital engineering, and specifically model based engineering, offers a key opportunity to gather continual data needed to move acquisition processes forward. To address this issue, methods must be developed and implemented to capture key process metrics on the full product life cycle, which includes conception, design, development, and test. The engineering acquisition process should be instrumented, capturing engineering metrics at a level of granularity sufficient to provide actionable information to other acquisition programs. These methods would be implemented on a set of diverse engineering programs, utilizing internal engineering design tools, product data and life-cycle management tools, and manpower reporting systems to capture data. This paper first discusses a number of specific examples of process instrumentation undertaken by the authors, then concludes with recommended lines of research for fully instrumenting acquisition processes.Approved for public release; distribution is unlimited

System Qualities Ontology, Tradespace and Affordability (SQOTA) Project Phase 5

Motivation and Context: One of the key elements of the SERC's research strategy is transforming the practice of systems engineering and associated management practices- "SE and Management Transformation (SEMT)." The Grand Challenge goal for SEMT is to transform the DoD community 's current systems engineering and management methods, processes, and tools (MPTs) and practices away from sequential, single stovepipe system, hardware-first ,document-driven, point- solution, acquisition-oriented approaches; and toward concurrent, portfolio and enterprise-oriented, hardware-software-human engineered, model-driven, set-based, full life cycle approaches.This material is based upon work supported, in whole or in part, by the U.S. Department of Defense through the Office of the Assistant Secretary of Defense for Research and Engineering (ASD(R&E)) under Contract H98230-08-D-0171 and HQ0034-13-D-0004 (TO 0060).This material is based upon work supported, in whole or in part, by the U.S. Department of Defense through the Office of the Assistant Secretary of Defense for Research and Engineering (ASD(R&E)) under Contract H98230-08-D-0171 and HQ0034-13-D-0004 (TO 0060)

Tradespace and Affordability – Phase 2

MOTIVATION AND CONTEXT: One of the key elements of the SERC’s research strategy is transforming the practice of systems engineering – “SE Transformation.” The Grand Challenge goal for SE Transformation is to transform the DoD community’s current systems engineering and management methods, processes, and tools (MPTs) and practices away from sequential, single stovepipe system, hardware-first, outside-in, document-driven, point-solution, acquisition-oriented approaches; and toward concurrent, portfolio and enterprise-oriented, hardware-software-human engineered, balanced outside-in and inside-out, model-driven, set-based, full life cycle approaches.This material is based upon work supported, in whole or in part, by the U.S. Department of Defense through the Office of the Assistant Secretary of Defense for Research and Engineering (ASD(R&E)) under Contract H98230-08- D-0171 (Task Order 0031, RT 046).This material is based upon work supported, in whole or in part, by the U.S. Department of Defense through the Office of the Assistant Secretary of Defense for Research and Engineering (ASD(R&E)) under Contract H98230-08- D-0171 (Task Order 0031, RT 046)

System Qualities Ontology, Tradespace and Affordability (SQOTA) Project – Phase 4

This task was proposed and established as a result of a pair of 2012 workshops sponsored by the DoD Engineered Resilient Systems technology priority area and by the SERC. The workshops focused on how best to strengthen DoD’s capabilities in dealing with its systems’ non-functional requirements, often also called system qualities, properties, levels of service, and –ilities. The term –ilities was often used during the workshops, and became the title of the resulting SERC research task: “ilities Tradespace and Affordability Project (iTAP).” As the project progressed, the term “ilities” often became a source of confusion, as in “Do your results include considerations of safety, security, resilience, etc., which don’t have “ility” in their names?” Also, as our ontology, methods, processes, and tools became of interest across the DoD and across international and standards communities, we found that the term “System Qualities” was most often used. As a result, we are changing the name of the project to “System Qualities Ontology, Tradespace, and Affordability (SQOTA).” Some of this year’s university reports still refer to the project as “iTAP.”This material is based upon work supported, in whole or in part, by the U.S. Department of Defense through the Office of the Assistant of Defense for Research and Engineering (ASD(R&E)) under Contract HQ0034-13-D-0004.This material is based upon work supported, in whole or in part, by the U.S. Department of Defense through the Office of the Assistant of Defense for Research and Engineering (ASD(R&E)) under Contract HQ0034-13-D-0004

Analysis of an Algorithm for Identifying Pareto Points in Multi-Dimensional Data Sets. 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization

In this paper we present results from analytical and experimental investigations into the performance of divide & conquer algorithms for determining Pareto points in multidimensional data sets of size n and dimension d. The focus in this work is on the worst-case, where all points are Pareto, but extends to problem sets where only a partial subset of the points is Pareto. Analysis supported by experiment shows that the number of comparisons is bounded by two different curves, one that is O(n (log n)^(d-2)), and the other that is O(n^log 3). Which one is active depends on the relative values of n and d. Also, the number of comparisons is very sensitive to the structure of the data, varying by orders of magnitude for data sets with the same number of Pareto points. Nomenclature n = number of points in a data set d = dimension of the data set TZ … , , = Table of n records, each record having d attributes tz … ,, = A record with d attributes ti, zi, … = The ith attribute in a record DC = Divide & Conquer algorithm pbf[n,d] = estimator for number of comparisons in DC algorithm, n points and dimension d mbf[n,d] = estimator for number of comparisons in marriage step of DC algorithm I I

Experimental Study of Wing Structure Geometry to Mitigate Process-Induced Deformation

Small uncrewed aerial vehicles that are fabricated with material extrusion additive manu- facturing often have wings that are single-perimeter structures with sparse internal structure. The large distance between internal supports creates an “unsupported-wall distance”, which leaves the wing skin prone to deformation during fabrication. This work explores and quan- tifies the relationship between the deformation of the wing skin and three geometric param- eters: (1) unsupported-wall distance, (2) local surface curvature, and (3) extrusion width. A three-level full factorial study was devised in which wing sections of varying surface curva- ture, unsupported-wall distance, and extrusion width were fabricated with polymer material extrusion additive manufacturing. The surfaces of the wing sections were then digitized into point clouds with a coordinate measuring machine, and the point cloud data were directly compared to the GCode used to print each wing section. The deformation data was analyzed to quantify the relationship between deformation and the experimental parameters. From the experiment, a non-dimensional term was identified that captures a bounding relationship between the geometric parameters and the deformation. Finally, a mathematical expression was developed to serve an upper bound on unsupported-wall distance based on extrusion width and surface curvature.Mechanical Engineerin

Using Mean Curvature of Implicitly Defined Minimal Surface Approximations to Generate New Unit Cells for Lattice Design

Triply Periodic Minimal Surfaces (TPMS) are smoothly varying surfaces that exhibit zero mean curvature at all points on the surface. TPMS can be modeled with high accuracy us- ing discrete differential geometry techniques. However, generating a useful number of unit cells with this approach would be computationally expensive, and variable lattices would be impossible. Level sets of Fourier series approximations are often used instead. While these approximations have continuous geometry, they no longer retain zero mean curvature like the exact TPMS. In this paper, we calculate the mean curvature of the commonly used approximations of the gyroid and D-surface TPMS. Using isosurfaces of the mean curvature from these approximates, we define, similar but unique surface topologies. The development of these surfaces expands the list of lattices available to designers, broadening the lattice design space. Application to other approximations and further study of the application of these new surfaces is discussed.Mechanical Engineerin

Algorithms to Identify Pareto Points in Multi-Dimensional Data Sets

*Signatures are on file in the Graduate School. ii The focus in this research is on developing a fast, efficient hybrid algorithm to identify the Pareto frontier in multi-dimensional data sets. The hybrid algorithm is a blend of two different base algorithms, the Simple Cull (SC) algorithm that has a low overhead but is of overall high computational complexity, and the Divide & Conquer (DC) algorithm that has a lower computational complexity but has a high overhead. The hybrid algorithm employs aspects of each of the two base algorithms, adapting in response to the properties of the data. Each of the two base algorithms perform better for different classes of data, with the SC algorithm performing best for data sets with few nondominated points, high dimensionality, or fewer total numbers of points, while the DC algorithm performs better otherwise. The general approach to the hybrid algorithm is to execute the following steps in order: 1. Execute one pass of the SC algorithm through the data if merited 2. Execute the DC algorithm, which recursively splits the data into smaller problem size