Comparative Study of Popular Deep Learning Models for Machining Roughness Classification Using Sound and Force Signals

On-line prediction of ultrasonic elliptical vibration cutting surface roughness of tungsten heavy alloy based on deep learning

(2020) Yanan Pan et al.   JOURNAL OF INTELLIGENT MANUFACTURING

The Application and Improvement of Deep Neural Networks in Environmental Sound Recognition

(2020) Yu-Kai Lin et al.   Applied Sciences-Basel

Evaluation of Deep Learning Neural Networks for Surface Roughness Prediction Using Vibration Signal Analysis

(2019) Wan-Ju Lin et al.   Applied Sciences-Basel

The use of support vector machine, neural network, and regression analysis to predict and optimize surface roughness and cutting forces in milling

(2019) Ali Yeganefar et al.   INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

Trends in audio signal feature extraction methods

(2019) Garima Sharma et al.   APPLIED ACOUSTICS

Machine health management in smart factory: A review

(2018) Gil-Yong Lee et al.   Journal of Mechanical Science and Technology

Analysis of Force Signals for the Estimation of Surface Roughness during Robot-Assisted Polishing

(2018) Beatriz de Agustina et al.   Materials

Smart Machining Process Using Machine Learning: A Review and Perspective on Machining Industry

(2018) Dong-Hyeon Kim et al.   International Journal of Precision Engineering and Manufacturing-Green Technology

Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth

(2017) D. Yu. Pimenov et al.   JOURNAL OF INTELLIGENT MANUFACTURING

Estimation of surface roughness using cutting parameters, force, sound, and vibration in turning of Inconel 718

(2017) Yogesh Deshpande et al.   Journal of the Brazilian Society of Mechanical Sciences and Engineering