Simulations of Die Casting With Uncertainty Quantification
- Authors
- Shahane, Shantanu; Mujumdar, Soham; Kim, Namjung; Priya, Pikee; Aluru, Narayana R.; Ferreira, Placid; Kapoor, Shiv G.; Vanka, Surya
- Issue Date
- Apr-2019
- Publisher
- ASME
- Citation
- JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME, v.141, no.4
- Journal Title
- JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME
- Volume
- 141
- Number
- 4
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/82004
- DOI
- 10.1115/1.4042583
- ISSN
- 1087-1357
- Abstract
- Die casting is a type of metal casting in which a liquid metal is solidified in a reusable die. In such a complex process, measuring and controlling the process parameters are difficult. Conventional deterministic simulations are insufficient to completely estimate the effect of stochastic variation in the process parameters on product quality. In this research, a framework to simulate the effect of stochastic variation together with verification, validation, and uncertainty quantification (UQ) is proposed. This framework includes highspeed numerical simulations of solidification, microstructure, and mechanical properties prediction models along with experimental inputs for calibration and validation. Both experimental data and stochastic variation in process parameters with numerical modeling are employed, thus enhancing the utility of traditional numerical simulations used in die casting to have a better prediction of product quality. Although the framework is being developed and applied to die casting, it can be generalized to any manufacturing process or other engineering problems as well.
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Collections - 공과대학 > 기계공학과 > 1. Journal Articles
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