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Graph-based metamodeling for characterizing cold metal transfer process performance

Research output: Contribution to journalArticleScientificpeer-review


Original languageEnglish
JournalSmart and Sustainable Manufacturing Systems
Issue number2
Publication statusPublished - Feb 2019
Publication typeA1 Journal article-refereed


Achieving predictable, reliable, and cost-effective operations in wire and arc additive manufacturing is a key concern during production of complex-shaped functional metallic components for demanding applications, such as those found in aerospace and automotive industries. A metamodel combining localized submodels of the different physical phenomena during welding can ensure stable material deposition. Such a metamodel would necessarily
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

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