Feng Xiong
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Feng Xiong has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Chaoyi Dong
School of Mechanical Engineering, Xi’an Technological University, Xi’an 710000, China
1 shared publication - Hu Qiao
School of Mechatronic Engineering, Xi’an Technological University, Xi’an 710021, China
1 shared publication - Xiaosheng Xin
School of Mechatronic Engineering, Xi’an Technological University, Xi’an 710021, China
1 shared publication - Ying Xiang
College of Mechanical and Engineering, Shaanxi University of Science and Technology, Xi’an 710021, China
1 shared publication - Ziyan Zhu
School of Mechatronic Engineering, Xi’an Technological University, Xi’an 710021, China
1 shared publication
Latest Publications
- Open Access
Hybrid Convolutional Neural Network–Bidirectional Long Short-Term Memory Model with Whale Optimization Algorithm for Error Prediction in On-Machine Measurement
On-machine measurement (OMM) enables real-time dimensional feedback in production, yet accuracy is often degraded by thermal drift, sensor noise, and environmental disturbances. This motivates intelligent error-prediction methods to ensure reliable, high-precision machining. This study proposes a hybrid deep learning model integrating a Convolutional Neural Network (CNN), a Bidirectional Long Short-Term Memory (Bi-LSTM) network, and a Whale Optimization Algorithm (WOA) for precise OMM error prediction. Initially, raw measurement data underwent preprocessing to remove noise and outliers. Subsequently, we use a CNN to extract features and a Bi-LSTM to model time-dependent patterns. Finally, WOA optimizes model hyperparameters globally, further boosting predictive accuracy. Comparative experiments show that the proposed model reduces RMSE, MAE, and MAPE by approximately 53.58%, 54.96%, and 57.65%, respectively, while improving the R2 score by about 11.17% over baseline methods. Results confirm the method’s superior nonlinear prediction capabilities, significantly enhancing machining accuracy and production efficiency, and demonstrating promising industrial application potential.
Open AccessHybrid Convolutional Neural Network–Bidirectional Long Short-Term Memory Model with Whale Optimization Algorithm for Error Prediction in On-Machine Measurement
On-machine measurement (OMM) enables real-time dimensional feedback in production, yet accuracy is often degraded by thermal drift, sensor noise, and environmental disturbances. This motivates intelligent error-prediction methods to ensure reliable, high-precision machining. This study proposes a hybrid deep learning model integrating a Convolutional Neural Network (CNN), a Bidirectional Long Short-Term Memory (Bi-LSTM) network, and a Whale Optimization Algorithm (WOA) for precise OMM error prediction. Initially, raw measurement data underwent preprocessing to remove noise and outliers. Subsequently, we use a CNN to extract features and a Bi-LSTM to model time-dependent patterns. Finally, WOA optimizes model hyperparameters globally, further boosting predictive accuracy. Comparative experiments show that the proposed model reduces RMSE, MAE, and MAPE by approximately 53.58%, 54.96%, and 57.65%, respectively, while improving the R2 score by about 11.17% over baseline methods. Results confirm the method’s superior nonlinear prediction capabilities, significantly enhancing machining accuracy and production efficiency, and demonstrating promising industrial application potential.