Graph-based metamodeling for characterizing cold metal transfer process performance
Research output: Contribution to journal › Article › Scientific › peer-review
|Journal||Smart and Sustainable Manufacturing Systems|
|Publication status||Published - Feb 2019|
|Publication type||A1 Journal article-refereed|
combine submodels from multiple domains, such as materials science, thermomechanical engineering, and process planning, and it would provide a holistic systems perspective of the modeled process. An approach using causal graph-based modeling and Bayesian networks is proposed to develop a metamodel for a test case using wire and arc additive manufacturing
with cold metal transfer. The developed modeling approach is used to characterize the effect of manufacturing variables on product dimensional quality in the form of a causal graph. A quantitative simulation using Bayesian networks is applied to the causal graph to enable process parameter tuning. The Bayesian network inference mechanism predicts the effects of the parameters on results, whereas, conversely, with known targets, it can predict the required parameter values. Validation of the developed Bayesian network model is performed using experimental tests.
- Predictive modeling, Metamodeling, Bayesian network, Decision-making, Wire arc additive manufacturing, cold metal transfer