Product lifecycles are complex heterogeneous systems. Applying control methods to lifecycles requires significant human capital. Additionally, measuring lifecycles relies primarily on domain expertise and estimates. Presented in this dissertation is a way to semantically represent a product lifecycle as a cyber-physical system for enabling the application of control methods to the lifecycle. Control requires a model and no models exist currently that integrate each phase of lifecycles. The contribution is an integration framework that brings all phases and systems of a lifecycle together. First presented is a conceptual framework and technology innovation. Next, linking product lifecycle data dynamical is described and then how that linked data could be certified and traced for trustworthiness. After that, discussion is focused how the trusted linked data could be combined with machine learning to drive applications throughout the product lifecycle. Last, a case study is provided that integrates the framework and technology. Integrating all of this would enable efficient and effective measurements of the lifecycle to support prognostic and diagnostic control of that lifecycle and related decisions. / Ph. D. / The manufacturing sector is on a precipice to disruptive change that will signifcantly alter the way industrial organizations think, communicate, and interact. Industry has been chasing the dream of integrating and linking data across the product lifecycle and enterprises for decades. However, inexpensive and easy to implement technologies to integrate the people, processes, and things across various enterprises are still not available to the entire value stream. Industry needs technologies that use cyber-physical infrastructures efectively and efciently to collect and analyze data and information across an enterprise instead of a single domain of expertise. Meeting key technical needs would save over $100 billion annually in emerging advanced manufacturing sectors in the US. By enabling a systems-thinking approach, signifcant economic opportunities can be achieved through an industrial shift from paper-based processes to a digitally enabled model-based enterprise via the digital thread. The novel contribution of this dissertation is a verifed and validated integration framework, using trusted linked-data, that brings all phases and systems of the product lifecycle together. A technology agnostic approach was pursued for dynamically generating links. A demonstration is presented as a reference implementation using currently available technology. Requirements, models, and policies were explored for enabling product-data trustworthiness. All methods were developed around open, consensus-based standards to increase the likelihood of scalability. The expected outcome of this work is efcient and efective measurements of the lifecycle to support data-driven methods, specifcally related to knowledge building, decision support, requirements management, and control of the entire product lifecycle.

For manufacturers of complex interdisciplinary system solutions, the Digital Thread is an essential element that serves as the backbone of technical and organizational data flows. This study proposes a graph-based Digital Thread that integrates a Digital Model and a derived Digital Twin in a native graph database in a polyglot architecture. The approach includes dynamic processes such as advanced interdisciplinary change management and additional data from different lifecycle phases, using objects and relations described by attributes to minimize redundancy while enabling view creation and filtering. Companies often have discipline-oriented IT landscapes that create data silos and prevent contextbased integration of data and process models. The Digital Thread as a backbone represents an approach to overcome these silos as a system lifecycle-spanning system.

<div>The manufacturing industry has begun its transition into a digital age, where data-driven decisions aim to improve product quality, output, and efficiency. Decisions made based on manufacturing data can help identify key problem areas in an assembly line and mitigate any defects from progressing through to the next step in the assembly process. But what if the products’ as manufactured data was inaccurate or didn’t exist at all? Decisions based on incorrect data can lead to defective parts being passed as good parts, costing manufacturers millions of dollars in rework or recalls. When specifically referring to mechanically fastened assemblies, products that experience rotation, like an aircraft propeller, or compress to create a seal, like an oil pipe flange, all require specific torque pattern sequences to be followed during assembly. When incorrectly torqued, the parts can have catastrophic failures resulting in consumer injury or ecological contamination. This paper outlines the development and feasibility of a system and its components for tracking and error-proofing the assembly of bolted joints in an industrial environment.</div><div>Using a machine vision system, the system traces the tool location relative to the mechanical fastener and records which order the fasteners were torqued in, if an error is detected, the system does not allow the user to progress through the assembly process, notifying if an error is detected. The system leverages open source machine learning algorithms from TensorFlow2 and OpenCv, that allow efficient object detection model training. The proposed system was tested using a series of tests and evaluated using the STEP method. The data collected aims to understand the system's feasibility and effectiveness in an industrial setting. </div><div>The tests aim to understand the effectiveness of the system under standard and variable industrial work conditions. Using the STEP method and other statistical analysis, an evaluation matrix was completed, ranking the system's ability to successfully meet all predetermined benchmarks and successfully record the torque pattern used to assemble apart</div>

<p>A thesis study investigating the development and evaluation of a computer vision (CV) system for a manufacturing assembly task is reported. The CV inference results are compared to a Manufacturing Process Plan and an automation method completes a buyoff in the software, Solumina. Research questions were created and three hypotheses were tested. A literature review was conducted recognizing little consensus of Industry 4.0 technology adoption in manufacturing industries. Furthermore, the literature review uncovered the need for additional research within the topic of CV. Specifically, literature points towards more research regarding the cognitive capabilities of CV in manufacturing. A CV system was developed and evaluated to test for 90% or greater confidence in part detection. A CV dataset was developed and the system was trained and validated with it. Dataset contextualization was leveraged and evaluated, as per literature. A CV system was trained from custom datasets, containing six classes of part. The pre-contextualization dataset and post-contextualization dataset was compared by a Two-Sample T-Test and statistical significance was noted for three classes. A python script was developed to compare as-assembled locations with as-defined positions of components, per the Manufacturing Process Plan. A comparison of yields test for CV-based True Positives (TPs) and human-based TPs was conducted with the system operating at a 2σ level. An automation method utilizing Microsoft Power Automate was developed to complete the cognitive functionality of the CV system testing, by completing a buyoff in the software, Solumina, if CV-based TPs were equal to or greater than human-based TPs.</p>

<p>  </p> <p>There has been a growing interest within the aerospace industry for shifting towards a digital twin approach, for reliable assessment of individual components during the product lifecycle - across design, manufacturing, and in-service maintenance, repair & overhaul (MRO) stages. The transition towards digital twins relies on continuous updating of the product lifecycle datasets and interoperable exchange of data applicable to components, thereby permitting engineers to utilize current state information to make more-informed downstream decisions. In this thesis, we primarily develop a framework to store, track, update, and retrieve product lifecycle data applicable to a serialized component, its features, and individual locations. </p> <p>From a structural integrity standpoint, the fatigue performance of a component is inherently tied to the component geometry, its material state, and applied loading conditions. The manufacturing process controls the underlying material microstructure, which in turn governs the mechanical properties and ultimately the performance. The processing also controls the residual stress distributions within the component volume, which influences the durability and damage tolerance of the component. Hence, we have demonstrated multiple use cases for fatigue life assessment of critical aerospace components, by using the developed framework for efficiently tracking and retrieving (i) the current geometric state, (ii) the material microstructure state, and (iii) residual stress distributions.</p> <p>Model-based definitions (MBDs) present opportunities to capture both geometric and non-geometric data using 3D computer-aided design (CAD) models, with the overarching aim to disseminate product information across different stages of the lifecycle. MBDs can potentially eliminate error-prone information exchange associated with traditional paper-based drawings and improve the fidelity of component details, captured using 3D CAD models. However, current CAD capabilities limit associating the material information with the component’s shape definition. Furthermore, the material attributes of interest, viz., material microstructures and residual stress distributions, can vary across the component volume. To this end, in the first part of the thesis, we implement a CAD-based tool to store and retrieve metadata using point objects within a CAD model, thereby creating associations to spatial locations within the component. The tool is illustrated for storage and retrieval of bulk residual stresses developed during the manufacturing of a turbine disk component, acquired from process modeling and characterization. Further, variations in residual stress distribution owing to process model uncertainties have been captured as separate instances of the disk’s CAD models to represent part-to-part variability as an analogy to track individual serialized components for digital twins. The propagation of varying residual stresses from these CAD models within the damage tolerance analysis performed at critical locations in the disk has been demonstrated. The combination of geometric and non-geometric data inside the MBD, via storage of spatial and feature varying information, presents opportunities to create digital replica or digital twin(s) of actual component(s) with location-specific material state information.</p> <p>To fully realize a digital twin description of components, it is crucial to dynamically update information tied to a component as it evolves across the lifecycle, and subsequently track and retrieve current state information. Hence, in the second part of the thesis, we propose a dynamic data linking approach to include material information within the MBDs. As opposed to storing material datasets directly within the CAD model in the previous approach, we externally store and update the material datasets and create data linkages between material datasets and features within the CAD models. To this end, we develop a model-based feature information network (MFIN), a software agnostic framework for linking, updating, searching, and retrieving of relevant information across a product’s lifecycle. The use case of a damage tolerance analysis for a compressor bladed-disk (blisk) is demonstrated, wherein Ti-6Al-4V blade(s) are linear friction welded to the Ti-6Al-4V disk, comprising well-defined regions exhibiting grain refinement and high residuals stresses. By capturing the location-specific microstructural information and residual stress fields at the weld regions, this information was accessed within the MFIN and used for downstream damage tolerant analysis. The introduction of the MFIN framework facilitates access to dynamically evolving as well as location-specific data for use within physics-based models.</p> <p>In the third part of thesis, we extend the MFIN framework to enable a physics-based, microstructure sensitive and location-specific fatigue life analysis of a component. Traditionally, aerospace components are treated as monolithic structures during lifing, wherein microstructural information at individual locations are not necessarily considered. The resulting fatigue life estimates are conservative and associated with large uncertainty bounds, especially in components with gradient microstructures or distinct location-specific microstructures, thereby leading to under usage of the component’s capabilities. To improve precision in the fatigue estimates, a location-specific lifing framework is enabled via MFIN, for tracking and retrieval of microstructural information at distinct locations for subsequent use within a crystal plasticity-based fatigue life prediction model. A use case for lifing dual-microstructure heat treated LSHR turbine disk component is demonstrated at two locations, near the bore (fine grains) and near the rim (coarse grains) regions. We employ the framework to access (a) the grain size statistics and (b) the macroscopic strain fields to inform precise boundary conditions for the crystal plasticity finite-element analysis. The illustrated approach to conduct a location-specific predictive analysis of components presents opportunities for tailoring the manufacturing process and resulting microstructures to meet the component’s targeted requirements.</p> <p>For reliably conducting structural integrity analysis of a component, it is crucial to utilize their precise geometric description. The component geometries encounter variations from nominal design geometries, post manufacturing or after service. However, traditionally, stress analyses are based on nominal part geometries during assessment of these components. In the last part of the thesis, we expand the MFIN framework to dynamically capture deviations in the part geometry via physical measurements, to create a new instance of the CAD model and the associated structural analysis. This automated workflow enables engineers for improved decision-making by assessing (i) as-manufactured part geometries that fall outside of specification requirements during the materials review board or (ii) in-service damages in parts during the MRO stages of the lifecycle. We demonstrate a use case to assess the structural integrity of a turbofan blade that had experienced foreign object damage (FOD) during service. The as-designed geometry was updated based on coordinate measurements of the damaged blade surfaces, by applying a NURBS surface fit, and subsequently utilized for downstream finite-element stress analysis. The ramifications of the FOD on the local stresses within the part are illustrated, providing critical information to the engineers for their MRO decisions. The automated flow of information from geometric inspection within structural analysis, enabled by MFIN, presents opportunities for effectively assessing products by utilizing their current geometries and improving decision-making during the product lifecycle.</p>

<p>  The world continues to advance at a hastening pace towards a technology enabled, digital-centric future. Legacy organizations, not born in the ‘digital age’ are examining methods to adapt through Digital Transformation (DT). The US Department of Defense (DoD) is one such organization. The DoD emerged their 2018 Digital Engineering Strategy intending on transforming the enterprise from one with ‘engineering process [that] are document-intensive and stove-piped, leading to extended cycle times with systems that are cumbersome to change and sustain’ to one that is ‘transforming its engineering practices to digital engineering, incorporating technological innovations into an integrated, digital, model-based approach’. </p> <p>  The 2018 Strategy acknowledges that the integration of digital engineering will not be exclusive to the engineering communities of the DoD; rather, the integration will impact the ‘research, requirements, acquisition, test, cost, sustainment and intelligence communities’. While the Strategy is designed to explain the ‘what’ necessary to integrate digital engineering, the various DoD Services (and their subordinates) will need to develop the ‘how’ regarding implementation that is culturally appropriate to their commands.</p> <p>  The study sought to examine ‘how’ implementation of digital engineering tenets may be appropriated to the existent culture of one US Special Operations Command subordinate at the Echelon III level (namely Naval Special Warfare Group – FOUR). The results of this study are intended to provide understanding and illuminate meaning behind those themes in Digital Engineering that Subject Matter Experts within Naval Special Warfare view as suitably adaptable to their processes. The intent is to provide themes with utility towards further efforts and research aimed at phasing Digital Transformation initiatives at Naval Special Warfare Group – FOUR.</p>

Industrial revolutions bring dynamic change to industry through major technological advances (Freeman & Louca, 2002). People and companies must take advantage of industrial revolutions in order to reap its benefits (Bruland & Smith, 2013). Currently, the 4th industrial revolution, industry is transforming advanced manufacturing and engineering capabilities through digital transformation. Company X’s production system was investigated in the research. Detailed evaluation the production process revealed bottlenecks and inefficiency (Melton, 2005). Using the Digital Twin and Discrete Event Factory Simulation, the researcher gathered factory and production input data to simulate the process and provide a system level, holistic view of Company X’s production system to show how factory simulation enables process improvement. The National Academy of Engineering supports Discrete Event Factory Simulation as advancing Personalized Learning through its ability to meet the unique problem solving needs of engineering and manufacturing process through advanced simulation technology (National Academy of Engineering, 2018). The directed project applied two process optimization experiments to the production system through the simulation tool, 3DExperience wiht the DELMIA application from Dassualt Systemes (Dassault, 2018). The experiment resulted in a 10% improvement in production time and a 10% reduction in labor costs due to the optimization