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Meeting TMS Specialty Congress 2024
Symposium 2nd World Congress on Artificial Intelligence in Materials & Manufacturing (AIM 2024)
Presentation Title A Data-driven Laplacian-penalized Non-rigid iterative Closest Point Reverse Deformation Model for Net-shape Investment Castings
Author(s) Jiayu Cui, Jun Wang, Donghong Wang, Da Shu, Baode Sun
On-Site Speaker (Planned) Jiayu Cui
Abstract Scope In the realm of industrial manufacturing, dimensional distortions frequently occur. This is particularly evident in investment castings where the underlying mechanisms are complex and ambiguous. Despite intensive studies, maintaining precise dimensions remains elusive. This research introduces a cutting-edge Laplacian-penalized non-rigid iterative closest point reverse deformation model to address this challenge. The learnable approach is able to provide the ideal mold design for investment castings using just one mold trial data. With more data supplied, the model can provide more accurate descriptions of deformation patterns and better reverse deformation design. Utilizing blue light scanning, 8 test reverse deformed castings from three production batches were assessed, all adhering to the stipulated dimensional standards of 0.5 angular error limit and CT2 linear dimension specifications. This breakthrough offers significant advancements in the precise dimensional control of investment castings as well as other manufacturing processes.
Proceedings Inclusion? Definite: Other

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

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