MAR-AM
Manufacturability Analyzer and Recommender for Additive Manufacturing
Alumni · Ph.D.
Ying Zhang
Published · 2019–2022Machine Learning Engineer at Corning Incorporated
Thesis Title: Development of Hybrid Machine Learning Models for Assessing the Manufacturability of Designs for Additive Manufacturing Processes
Software
1Publications
8A systematic review on data of additive manufacturing for machine learning applications: the data quality, type, preprocessing, and management
Ying Zhang, Mutahar Safdar, Jiarui Xie, Jinghao Li, …, Yaoyao Fiona Zhao
Journal of Intelligent ManufacturingOPEN ACCESS
A Web-based automated manufacturability analyzer and recommender for additive manufacturing (MAR-AM) via a hybrid Machine learning model
Ying Zhang, Yaoyao Fiona Zhao
Expert Systems with Applications
Hybrid sparse convolutional neural networks for predicting manufacturability of visual defects of laser powder bed fusion processes
Ying Zhang, Yaoyao Fiona Zhao
Journal of Manufacturing SystemsOPEN ACCESS
Predictive manufacturability assessment system for laser powder bed fusion based on a hybrid machine learning model
Ying Zhang, Sheng Yang, Guoying Dong, Yaoyao Fiona Zhao
Additive manufacturingOPEN ACCESS
Manufacturability analysis of metal laser-based powder bed fusion additive manufacturing—a survey
Ying Zhang, Sheng Yang, Yaoyao Fiona Zhao
The International Journal of Advanced Manufacturing TechnologyOPEN ACCESS
Towards an automated decision support system for the identification of additive manufacturing part candidates
Sheng Yang, Tom Page, Ying Zhang, Yaoyao Fiona Zhao
Journal of Intelligent ManufacturingOPEN ACCESS
A Comparative Study of Metal Additive Manufacturing Processes for Elevated Sustainability
Wenbo Min, Sheng Yang, Ying Zhang, Yaoyao Fiona Zhao
Machine Learning Assisted Prediction of the Manufacturability of Laser-Based Powder Bed Fusion Process
Ying Zhang, Guoying Dong, Sheng Yang, Yaoyao Fiona Zhao