Hierarchical multi-response Gaussian processes for uncertainty analysis with multi-scale composite manufacturing simulation

Kai Zhou, Ryan Enos, Dong Xu, Dianyun Zhang, Jiong Tang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)


Variations of constituent fiber and matrix properties and process conditions can cause significant variability in composite parts and affect their performance. The focus of this paper is to establish a new computational framework that can efficiently quantify the uncertainty propagation of parts manufactured through the resin transfer molding (RTM) process. RTM involves a sequence of inter-related processes that span multiple spatial and temporal scales. This calls for a multi-scale analysis for the nominal process, which is computationally complex and intensive. A direct Monte Carlo simulation of uncertainty quantification leads to prohibitive cost. In this research we leverage a sequentially architected multi-response Gaussian process (MRGP) meta-modelling approach to facilitate a hierarchical procedure. This can dramatically reduce the computational cost, and allow us to characterize the process outputs of interest at different scales and at the same time capture the intrinsic correlation amongst these outputs. Moreover, integrating a global sensitivity analysis with the hierarchical MRGP meta-models yields the importance ranking of uncertainty propagation paths. This computational framework provides a quantitative assessment tool of the uncertainties in composite manufacturing. Case study on curing-induced dimensional variability of a curved composite part is conducted for demonstration and validation.

Original languageEnglish
Article number111257
JournalComputational Materials Science
Publication statusPublished - May 2022
Externally publishedYes


  • Global sensitivity analysis
  • Importance ranking
  • Multi-response Gaussian process (MRGP)
  • Multi-scale simulation
  • Resin transfer molding (RTM) process
  • Uncertainty propagation analysis

ASJC Scopus subject areas

  • General Computer Science
  • General Chemistry
  • General Materials Science
  • Mechanics of Materials
  • General Physics and Astronomy
  • Computational Mathematics


Dive into the research topics of 'Hierarchical multi-response Gaussian processes for uncertainty analysis with multi-scale composite manufacturing simulation'. Together they form a unique fingerprint.

Cite this