Xing Cheng
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Xing Cheng has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Guoquan Ding
Key Laboratory of Airborne Plasma Dynamics, Air Force Engineering University, Xi’an 710038, China
1 shared publication - Peiyu Zhang
Key Laboratory of Airborne Plasma Dynamics, Air Force Engineering University, Xi’an 710038, China
1 shared publication - Ting Zhang
National Key Lab of Aerospace Power System and Plasma Technology, Xi’an Jiaotong University 1 , Xi’an 710049
1 shared publication - Xin Zhou
Aeronautical Engineering College, Air Force Engineering University, Xi’an 710038, China
1 shared publication - Xuede Wang
National Key Lab of Aerospace Power System and Plasma Technology, Air Force Engineering University, Xi’an 710038, China
1 shared publication - Yucong Duan
Key Laboratory of Airborne Plasma Dynamics, Air Force Engineering University, Xi’an 710038, China
1 shared publication
Latest Publications
- Open Access
Hardness Prediction of Laser Powder Bed Fusion Product Based on Melt Pool Radiation Intensity
The quality stability and batch consistency of laser powder bed fusion products are key issues that must be solved in additive manufacturing. The melt pool radiation intensity data of laser powder bed fusion contain a significant amount of forming process information, and studies have shown that the analysis of melt pool radiation intensity using data-driven methods can achieve online quality judgment; however, there are still speed and accuracy problems. In this study, we propose a data-driven model for hardness predictions of laser powder bed fusion products based on process parameters fused with power spectrum features of melt pool intensity data, which quickly and accurately predicts the microhardness of laser powder bed fusion specimens and can make constructive guidance for closed-loop feedback quality regulation in practical production. The effects of three integrated learning models, Random Forest, XGBoost and LightGBM, are also compared. The results indicate that random forest has the highest prediction accuracy in this dataset; however, it has the limitation of slow training and prediction speeds. The LightGBM algorithm has the fastest training and prediction speeds, about 1.4% and 4.4% of the random forest, respectively; however, the prediction accuracy is lower than that of random forest and XGBoost. XGBoost has the best overall comparative performance with adequate training and prediction speeds, about 23.7% and 37.9% of the random forest, respectively, while ensuring a specified prediction accuracy, which is suitable for application in engineering practices.
Open AccessHardness Prediction of Laser Powder Bed Fusion Product Based on Melt Pool Radiation Intensity
The quality stability and batch consistency of laser powder bed fusion products are key issues that must be solved in additive manufacturing. The melt pool radiation intensity data of laser powder bed fusion contain a significant amount of forming process information, and studies have shown that the analysis of melt pool radiation intensity using data-driven methods can achieve online quality judgment; however, there are still speed and accuracy problems. In this study, we propose a data-driven model for hardness predictions of laser powder bed fusion products based on process parameters fused with power spectrum features of melt pool intensity data, which quickly and accurately predicts the microhardness of laser powder bed fusion specimens and can make constructive guidance for closed-loop feedback quality regulation in practical production. The effects of three integrated learning models, Random Forest, XGBoost and LightGBM, are also compared. The results indicate that random forest has the highest prediction accuracy in this dataset; however, it has the limitation of slow training and prediction speeds. The LightGBM algorithm has the fastest training and prediction speeds, about 1.4% and 4.4% of the random forest, respectively; however, the prediction accuracy is lower than that of random forest and XGBoost. XGBoost has the best overall comparative performance with adequate training and prediction speeds, about 23.7% and 37.9% of the random forest, respectively, while ensuring a specified prediction accuracy, which is suitable for application in engineering practices.