Journal of Northeastern University(Natural Science) ›› 2025, Vol. 46 ›› Issue (6): 66-75.DOI: 10.12068/j.issn.1005-3026.2025.20240096

• Mechanical Engineering • Previous Articles     Next Articles

Transfer Learning-Based Robotic Belt Grinding Process for NiCo-FGM

Bo XIN, Hong-liang LI, Wen-xin SUN, Ming-jun LIU   

  1. School of Mechanical Engineering & Automation,Northeastern University,Shenyang 110819,China. Corresponding author: XIN Bo,E-mail: xinbo@me. neu. edu. cn
  • Received:2023-08-27 Online:2025-06-15 Published:2025-09-01

Abstract:

In order to improve the removal depth consistency of nickel-cobalt functional gradient materials (NiCo-FGM), an adaptive grinding force control system was constructed to carry out constant force zonal grinding experiments on five types of NiCo-FGM with different mass fractions of IN718 to investigate the trend and extent of the influence of the process parameters on the removal depth and surface roughness of the materials. The feasibility of transfer learning was then analyzed and the accuracy of the removal depth modelling was compared with that of empirical formulas. Finally, comparing the removal depth prediction results of constant force and variable force grinding. The results showed that the normal force has the most significant effect on the removal depth and surface roughness of the materials. The average error in the prediction of transfer learning is reduced by 4.07%, and the efficiency is higher. The maximum difference in removal depth between the remaining content of IN718 and 50%IN718 under constant force grinding is 8.955 μm, and the maximum removal depth difference between 100%IN718 and 0%IN718 is 15.619 μm, whereas the removal depth consistency can be improved by variable force grinding.

Key words: nickel-cobalt functional gradient material (NiCo-FGM), transfer learning, removal depth consistency, robotic belt grinding, adaptive grinding force control system

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