Author:
Li Zhe,Qin Xiaobo,Fu Xiangfu,Jiang Bin,Cong Weiqi,Zhang Quanjian
Funder
Fundamental Research Fundation for Universities of Heilongjiang Province
National Nature Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Industrial and Manufacturing Engineering,Computer Science Applications,Mechanical Engineering,Software,Control and Systems Engineering
Reference18 articles.
1. Mackenzie A (2015) The production of prediction: what does machine learning want? Eur J Cult Stud 18:429–445
2. Chan TC, Lin HH, Reddy SVVS (2022) Prediction model of machining surface roughness for five-axis machine tool based on machine-tool structure performance. Int J Adv Manuf Tech 120:237–249
3. Yang A, Han Y, Pan Y, Xing H, Li J (2017) Optimum surface roughness prediction for titanium alloy by adopting response surface methodology. Results Phys 7:1046–1050
4. Kong D, Zhu J, Duan C, Lu L, Chen D (2020) Bayesian linear regression for surface roughness prediction. Mech Syst Signal Pr 142:106770
5. Vahabli E, Rahmati S (2016) Application of an RBF neural network for FDM parts’ surface roughness prediction for enhancing surface quality. Int J Adv Manuf Tech 17:1589–1603