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Cooling channel free surface optimisation for additively manufactured casting tools

Abstract:
In the present study, an algorithm has been developed using the adjoint method to optimise the position and cross-section of an internal cooling channel for a 3D printed tool steel insert for use in the aluminium die-casting process. The algorithm enables the development of an optimised complex industrial mould with relatively low computational cost. A transient model is validated against multiple experimental trials, providing an adapted interface heat transfer coefficient. A steady state thermal model, based on the casting cycle and thermal behaviour at the mould surface, is developed to evaluate the spatial distribution of temperature and to serve as the initial solution for the subsequent optimisation stage. The adjoint model is then applied to optimise the cooling channel emphasising the minimisation of the temperature standard deviation for the mould surface. The original transient model is applied to the optimised mould configuration via calibration using experimental data obtained from a dedicated aluminium furnace. The optimised cooling channel geometry, which uses a non-uniform cross-section across the entire pipe surface region, improves the pressure drop and cooling uniformity across the mould/cast interface by 24.2% and 31.6%, respectively. The model has been used to optimise cooling channels for a range of industrial high-pressure aluminium die-casting (HPADC) inserts. This has yielded a significant improvement in the mould operational lifetime, rising to almost 130,000 shots compared to 40,000 shots for prior designs.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/s00170-023-11402-4

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0002-9677-1234


Publisher:
Springer
Journal:
International Journal of Advanced Manufacturing Technology More from this journal
Volume:
127
Issue:
3-4
Pages:
1293-1315
Publication date:
2023-05-26
Acceptance date:
2023-04-04
DOI:
EISSN:
1433-3015
ISSN:
0268-3768


Language:
English
Keywords:
Pubs id:
1490250
Local pid:
pubs:1490250
Deposit date:
2023-08-03

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