Accesso libero

Prediction of surface quality in end milling based on modified convolutional recurrent neural network

INFORMAZIONI SU QUESTO ARTICOLO

Cita

[1] Liu Yue. The State, Industrial Organization and Industrial Development: Establishment and development of China Machine Tool Industry Association (1988-2016). Master’s Thesis, Central China Normal University, 2020.Search in Google Scholar

[2] Behin Elahi, Sadegh Amiri Tokaldany. Application of Internet of Things-aided simulation and digital twin technology in smart manufacturing. Advances in Mathematics for Industry 4.0, 2021:335-359.Search in Google Scholar

[3] Karl Hribernik, Giacomo Cabri, Federica Mandreoli, Gregoris Mentzas. Autonomous context-aware adaptive Digital Twins-State of the art and roadmap. Computers in Industry, 133(2021), 103508.Search in Google Scholar

[4] Chao Liu, Ziwei Su, Xun Xu, Yuqian Lu. Service-oriented industrial internet of things gateway for cloud manufacturing. Robotics and Computer-Integrated Manufacturing, 73(2022), 102217.Search in Google Scholar

[5] Chanbeom Bak, Abhishek Ghosh Roy, Hungsun Son. Quality prediction for aluminium diecasting process based on shallow neural network and data feature selection technique. CIRP Journal of Manufacturing Science and Technology, 33(2021):327-338.Search in Google Scholar

[6] Chen Lu. Study on prediction of surface quality in machining process. Journal of Materials Processing Technology, 205(2008):439-450.Search in Google Scholar

[7] Wang Hongxiang, Dong Shen, Li Dan, Liang Feng. Effects of Optimum Controlled Vibration on Ultraprecision Machined Surface Quality. Chinese journal of mechanics, 11(4):452-455. (in Chinese)Search in Google Scholar

[8] Hong Quan, Wang Guicheng. The status and development of machined surface integrity in precision machining. Modern Manufacturing Engineering, 2004(8):12-15.Search in Google Scholar

[9] Peter Michalik, Jozef Zajac, Michal Hatala, Dusan Mital, Veronika Fecova. Monitoring surface roughness of thin-walled components from steel C45 machining down and up milling. Measurement, 58(2014):416-428.Search in Google Scholar

[10] Ki Yong Lee, Myeong Chang Kang, Yung Ho Jeong, Deuk Woo Lee, Jeong Suk Kim. Simulation of surface roughness and profile in high-speed end milling. Journal of Materials Processing Tech., 2001, 113(1):410-415.Search in Google Scholar

[11] Y. Mizugaki, M. Hao, K. Kikkawa, T. Nakagawa. Geometric Generating Mechanism of Machined Surface by Ball-nosed End Milling. CIRP Annals – Manufacturing Technology, 2001, 50(1):69-72.Search in Google Scholar

[12] B.H. Kim, C.N. Chu. Texture prediction of milled surfaces using texture superposition method. Computer-Aided Design, 1999, 31(8):485-494.Search in Google Scholar

[13] Dilbag Singh, P. Venkateswara Rao. A surface roughness prediction model for hard turning process. The International Journal of Advanced Manufacturing Technology, 2007, 32(11-12):1115-1124.Search in Google Scholar

[14] Y. Quinsat, L. Sabourin, C. Lartigue. Surface topography in ball end milling process: Description of a 3D surface roughness parameter. Journal of Materials Processing Tech., 2008, 195(1-3):135-143.Search in Google Scholar

[15] Azlan Mohd Zain, Habibollah Haron, Safian Sharif. Application of GA to optimize cutting conditions for minimizing surface roughness in end milling machining process. Expert Systems With Applications, 2010, 37(6):4650-4659.Search in Google Scholar

[16] Jean Philippe Costes, Vincent Moreau. Surface roughness prediction in milling based on tool displacements. Journal of Manufacturing Processes, 2011, 13(2):133-140.Search in Google Scholar

[17] Sahith Reddy Madara, Swaroop Ramaswamy Pillai, Chithirai Pon Selvan M, Jens Van heirle. Modelling of surface roughness in abrasive waterjet cutting of Kevlar 49 composite using artificial neural network. Materials Today: Proceedings, 2020(46), 1:1-8.Search in Google Scholar

[18] Girish Kant, Kuldip Singh Sangwan. Predictive Modelling and Optimization of Machining Parameters to Minimize Surface Roughness using Artificial Neural Network Coupled with Genetic Algorithm. Procedia CIRP, 2015, 31:453-458.Search in Google Scholar

[19] Tao Li, Zhenting Zhang, Hua Chen. Predicting the combustion state of rotary kilns using a Convolutional Recurrent Neural Network. Journal of Process Control, 2019, 84:207-214.Search in Google Scholar

[20] Xin Zhang, Jian wei Ma, Zhen yuan Jia, De ning Song. Machining parameter optimisation for aviation aluminium-alloy thin-walled parts in high-speed milling. International Journal of Machining and Machinability of Materials, 2018.Search in Google Scholar

eISSN:
2444-8656
Lingua:
Inglese
Frequenza di pubblicazione:
Volume Open
Argomenti della rivista:
Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics