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Geometrical Simulation Model for Milling of Carbon Fiber Reinforced Polymers (CFRP)
Xingyu Fu,송경은,Dong Min Kim,Zhengyang Kang,Martin Byung-Guk Jun 한국정밀공학회 2022 International Journal of Precision Engineering and Vol.23 No.11
This paper proposes a geometrical 3D milling simulation algorithm for Carbon Fiber Reinforced Polymer (CFRP) milling. In this simulation model, milling tools are simplified into layers of circles while CFRP lami- nates are simplified into layers of Dexel lines, which can realize simulations for various complex milling conditions. Significant geometrical parameters, for ex-ample, cutting angle and cutting length, can be computed with high efficiency. With some geometry-related physical models, the machining results can be pre-dicted for the entire milling process. The effectiveness of this simulation model has been validated by the milling force prediction and the delamination pre-diction. The performance of this simulation model benefits industrial CFRP manufacturing and provides a new method for online or long-time-interval simulation of CFRP machining.
Fu Xingyu,Zhou Fengfeng,Peddireddy Dheeraj,Kang Zhengyang,Jun Martin Byung-Guk,Aggarwal Vaneet 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.3
In this work, we present a boundary oriented graph embedding (BOGE) approach for the graph neural network to assist in rapid design and digital prototyping. The cantilever beam problem has been solved as an example to validate its potential of providing physical field results and optimized designs using only 10 ms. Providing shortcuts for both boundary elements and local neighbor elements, the BOGE approach can embed unstructured mesh elements into the graph and performs an efficient regression on large-scale triangular-mesh-based finite element analysis (FEA) results, which cannot be realized by other machine-learning-based surrogate methods. It has the potential to serve as a surrogate model for other boundary value problems. Focusing on the cantilever beam problem, the BOGE approach with 3-layer DeepGCN model achieves the regression with mean square error (MSE) of 0.011 706 (2.41% mean absolute percentage error) for stress field prediction and 0.002 735 MSE (with 1.58% elements having error larger than 0.01) for topological optimization. The overall concept of the BOGE approach paves the way for a general and efficient deep-learning-based FEA simulator that will benefit both industry and Computer Aided Design (CAD) design-related areas.