Process and Quality Modeling in Cyber Additive Manufacturing Networks with Data Analytics

A cyber manufacturing system (CMS) is a concept generated from the cyber-physical system (CPS), providing adequate data and computation resources to support efficient and optimal decision making. Examples of these decisions include production control, variation reduction, and cost optimization. A CMS integrates the physical manufacturing equipment and computation resources via Industrial Internet, which provides low-cost Internet connections and control capability in the manufacturing networks. Traditional quality engineering methodologies, however, typically focus on statistical process control or run-to-run quality control through modeling and optimization of an individual process, which makes it less effective in a CMS with many manufacturing systems connected. In addition, more personalization in manufacturing generates limited samples for the same kind of product designs, materials, and specifications, which prohibits the use of many effective data-driven modeling methods. Motivated by Additive Manufacturing (AM) with the potential to manufacture products with a one-of-a-kind design, material, and specification, this dissertation will address the following three research questions: (1) how can in situ data be used to model multiple similar AM processes connected in a CMS (Chapter 3)? (2) How to improve the accuracy of the low-fidelity first-principle simulation (e.g., finite element analysis, FEA) for personalized AM products to validate the product and process designs (Chapter 4) in time? (3) And how to predict the void defect (i.e., unmeasurable quality variables) based on the in situ quality variables. By answering the above three research questions, the proposed methodology will effectively generate in situ process and quality data for modeling multiple connected AM processes in a CMS. The research to quantify the uncertainty of the simulated in situ process data and their impact on the overall AM modeling is out of the scope of this research. The proposed methodologies will be validated based on fused deposition modeling (FDM) processes and selective laser melting processes (SLM). Moreover, by comparing with the corresponding benchmark methods, the merits of the proposed methods are demonstrated in this dissertation. In addition, the proposed methods are inherently developed with a general data-driven framework. Therefore, they can also potentially be extended to other applications and manufacturing processes.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Industrial and Systems Engineering

Department dc:contributor.department

Industrial and Systems Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2021

Author dc:creator

• Wang, Lening

Chair dc:contributor.committeechair

• Jin, Ran

Committee members dc:contributor.committeemember

• Wernz, Christian
• Kong, Zhenyu
• Johnson, Blake

• Additive manufacturing
• Data-driven Modeling
• Manufacturing Network

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

vt_gsexam:31927

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/104655