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.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/85627 |
| Date | 01 November 2018 |
| Creators | Hedberg, Thomas Daniel Jr. |
| Contributors | Industrial and Systems Engineering, Camelio, Jaime A., Weiss, Brian A., Triantis, Konstantinos P., Salado Diez, Alejandro |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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