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Predicting and Validating Multiple Defects in Metal Casting Processes Using an Integrated Computational Materials Engineering Approach

Lu, Yan
http://orcid.org/0000-0001-6608-003X
http://rave.ohiolink.edu/etdc/view?acc_num=osu1562346439311635

Abstract Details

2019, Doctor of Philosophy, Ohio State University, Materials Science and Engineering.
Metal casting is a manufacturing process of solidifying molten metal in a mold to make a product with a desired shape. Based on its own unique fabrication benefits, it is one of the most widely used manufacturing processes to economically produce parts with complex geometries in modern industry, especially for transportation and heavy equipment industries where mass production is needed. However, various types of defects typically exist in the as-cast components during the casting processes, which may make it difficult for post-processing and limit the service life and further application of products. It becomes imperative to analyze the processes in actual manufacturing conditions to predict and prevent those casting defects. Since it can be quite time consuming and costly to assess the processes experimentally, a computer-aided approach is highly desirable for product development and process optimization. In recent decades, computer-aided engineering (CAE) techniques have been rapidly developed to simulate different casting processes, which have great benefits to tackle casting defects in a more practical and efficient way. This work focuses on using ProCAST®, a finite element analysis (FEA) software, together with other necessary simulation and modeling techniques, including Computer-Aided Design (CAD), Calculation of Phase Diagrams (CALPHAD) and Cellular Automaton (CA), to study relevant defects in actual metal casting foundries. Specifically, three different cases have been mainly investigated, including (i) veining defect caused by thermal cracking in resin-bonded silica sand molds/inserts for sand casting process; (ii) thermal fatigue cracking in H13 steel dies/inserts for high pressure die casting process; and (iii) Hydrogen-induced gas porosity in A356 castings for gravity casting process with permanent molds. For each case, CAD model was designed and FEA model was constructed with validated materials database based on CALPHAD simulation, experiment tests and/or literature references. Coupled calculations of heat transfer, fluid flow for mold filling, and/or stresses and strains were run to obtain thermal and structural data for subsequent defects analyses and predictions. More importantly, key experiments at laboratory scale were designed and performed to reproduce those defects. Test results were employed to correlate and validate the predictions from simulation. The highlight of this dissertation is that an improved model and/or prediction criterion is proposed for each defect case and is dedicated to engineering applications, including (i) a statistics-based cracking criterion of resin-bonded silica sand molds or inserts in casting processes; (ii) a temperature-based fatigue life prediction criterion for thermally-induced cracking in H13 steel dies for die casting; and (iii) a coupled CA-FE model for location-specific prediction of gas porosity in A356 gravity castings with permanent molds. This research is aiming at demonstrating that the integration of different CAE techniques and key experimental validations can help tackle the defects in various casting processes in a time-efficient and cost-effective manner. The results and the approach may be of great benefits to casting engineers for defect assessments and design optimizations in different casting processes.
Alan Luo (Advisor)
Glenn Daehn (Committee Member)
Wei Zhang (Committee Member)
153 p.
Engineering; Materials Science
casting process simulation; defect prediction; resin-bonded silica sand; H13 steel dies; A356 alloy; ICME

Recommended Citations

Citations

  • Lu, Y. (2019). Predicting and Validating Multiple Defects in Metal Casting Processes Using an Integrated Computational Materials Engineering Approach [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1562346439311635

    APA Style (7th edition)

  • Lu, Yan. Predicting and Validating Multiple Defects in Metal Casting Processes Using an Integrated Computational Materials Engineering Approach. 2019. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1562346439311635.

    MLA Style (8th edition)

  • Lu, Yan. "Predicting and Validating Multiple Defects in Metal Casting Processes Using an Integrated Computational Materials Engineering Approach." Doctoral dissertation, Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1562346439311635

    Chicago Manual of Style (17th edition)

osu1562346439311635
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This open access ETD is published by The Ohio State University and OhioLINK.