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Effects of process related variations on fillablity simulation of thin-walled IN718 structures
School of Innovation, Design and Engineering, Mälardalen University, Eskilstuna, Sweden.
Swerea SWECAST AB, Jönköping, Sweden.
TPC Components AB, Hallstahammar, Sweden.
School of Innovation, Design and Engineering, Mälardalen University, Eskilstuna, Sweden.
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2018 (English)In: International Journal of metalcasting, ISSN 1939-5981, E-ISSN 2163-3193, Vol. 12, no 3, p. 543-553Article in journal (Refereed) Published
Abstract [en]

Simulation tools have improved significantly and are now capable of accurately predicting mould filling behavior. The quality of prediction is highly dependent on material properties and set-up of boundary conditions for the simulation. In this work material properties were measured and casting conditions were analyzed to accurately replicate the casting process in simulation. The sensitivity of the predictions to minor process variations commonly found in foundries was evaluated by comparing simulation and cast samples. The observed discrepancies between simulation and cast samples were evaluated and discussed in terms of their dependency on process variations. It was concluded that the simulation set-up was capable of reasonable predictions and could replicate the asymmetry of the filling however did not accurately predict the absolute value of the unfilled area. It was discovered that asymmetric flow due to variations in the orientation of the casting mould during filling could have greater influence on the predictions than the actual variation in fill time. The quality of simulation is dependent on equipment and techniques used in the foundry as well as the metallurgical model to simulate the process. 

Place, publisher, year, edition, pages
Springer, 2018. Vol. 12, no 3, p. 543-553
Keywords [en]
casting, filling, inconel, prediction, simulation, thin-walled, Foundries, Molds, Sensitivity analysis, Thin walled structures, Asymmetric flows, Casting conditions, Metallurgical model, Process Variation, Quality of predictions, Forecasting
National Category
Metallurgy and Metallic Materials
Identifiers
URN: urn:nbn:se:hj:diva-41202DOI: 10.1007/s40962-017-0189-9ISI: 000436927100014Scopus ID: 2-s2.0-85049332591Local ID: HOA JTH 2018OAI: oai:DiVA.org:hj-41202DiVA, id: diva2:1240676
Available from: 2018-08-22 Created: 2018-08-22 Last updated: 2019-04-10Bibliographically approved

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Jarfors, Anders E.W.

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