Thermal Deformation Defect Prediction for Layered Printing Using Convolutional Generative Adversarial Network

Thermal Deformation Defect Prediction for Layered Printing Using Convolutional Generative Adversarial Network

This paper presents a Thermal Deformation defect prediction method for layered printing using Convolutional Generative Adversarial Network (CGAN). Firstly, the original manifold mesh is converted into layered image in Printing Coordinate System (PCS). The trajectory inside layered image with various...

Full description

Bibliographic Details
Main Authors: Jinghua Xu, Kang Wang, Shuyou Zhang, Guodong Yi, Jianrong Tan, Sheng Luo, Jihong Pang
Format: Article
Language:English
Published: MDPI AG 2020-09-01
Series:Applied Sciences
Subjects:
thermal deformation defect prediction
layered printing
virtual printing via digital twins
convolutional generative adversarial network (CGAN)
surface profile error
binocular stereo vision
Online Access:https://www.mdpi.com/2076-3417/10/19/6860

Internet

https://www.mdpi.com/2076-3417/10/19/6860

Similar Items