摘要
This paper applied the neural network technology to surface reasoning in reverse engineering and established the neural network computation model. One of the main advantages of reasoning solid surface using neural network is that no knowledge about surface is needed, and the limited measured points on the surface will do sufficiently. This paper listed the related reasoning cases, including the elementary analytical surfaces and freeform surfaces, discussed the various issues occurring during reasoning process and proved the feasibility and efficiency of this approach from theory and practical computing cases. The results show that a neural network is an excellent aided analysis means for surface reasoning in reversing engineering and possesses practical use for the surface that is complex, incomplete and partially worn out or damaged.
This paper applied the neural network technology to surface reasoning in reverse engineering and established the neural network computation model. One of the main advantages of reasoning solid surface using neural network is that no knowledge about surface is needed, and the limited measured points on the surface will do sufficiently. This paper listed the related reasoning cases, including the elementary analytical surfaces and freeform surfaces, discussed the various issues occurring during reasoning process and proved the feasibility and efficiency of this approach from theory and practical computing cases. The results show that a neural network is an excellent aided analysis means for surface reasoning in reversing engineering and possesses practical use for the surface that is complex, incomplete and partially worn-out or damaged.
