Neural Network Gaussian Process considering Input Uncertainty and Application to Composite Structures Assembly

Developing machine learning enabled smart manufacturing is promising for composite structures assembly process. It requires accurate predictive analysis on deformation of the composite structures to improve production quality and efficiency of composite structures assembly. The novel composite structures assembly involves two challenges: (i) the highly nonlinear and anisotropic properties of composite materials; and (ii) inevitable uncertainty in the assembly process. To overcome those problems, we propose a neural network Gaussian process model considering input uncertainty for composite structures assembly. Deep architecture of our model allows us to approximate a complex system better, and consideration of input uncertainty enables robust modeling with complete incorporation of the process uncertainty. Our case study shows that the proposed method performs better than benchmark methods for highly nonlinear systems.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

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

2020

Author dc:creator

• Lee, Cheol Hei

Chair dc:contributor.committeechair

• Yue, Xiaowei

Committee members dc:contributor.committeemember

• Guo, Feng
• Bish, Douglas R.

• Neural Network Gaussian Process
• Input Uncertainty
• Data-driven Manufacturing
• Composite Structures Assembly

Statement dc:rights

• In Copyright

Licence dc:rights.uri

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

Dc Identifier Other

vt_gsexam:25701

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/106566