Effect of Process Modes on Tangential Component of Cutting Force during Belt Rotary Grinding of Aluminium Alloy Blanks D 16

These days, in the manufacture of units and mechanisms of ships, aircraft and other technological machines, industrial robots, long-sized products from D 16 (Standart GOST-R) aluminum alloy are used, for the processing of which a complex for belt rotary grinding has been developed. The outcome measures of the rotary belt grinding process depend on the cutting forces generated during the processing process. According to cutting forces, process stability is diagnosed, values of surface roughness indices, temperatures and cutting modes are calculated according to displacement balance equation. The article is devoted to obtaining a mathematical model establishing the relationship between the tangential component of the cutting force and cutting modes during belt rotary grinding of D 16 aluminum alloy.

Prediction of Abrasive Belt Wear Based on BP Neural Network

Abrasive belt grinding is the key technology in high-end precision manufacturing field, but the working condition of abrasive particles on the surface of the belt will directly affect the quality and efficiency during processing. Aiming at the problem of the inability to monitor the wearing status of abrasive belt in real-time during the grinding process, and the challenge of time-consuming control while shutdown for detection, this paper proposes a method for predicating the wear of abrasive belt while the grinding process based on back-propagation (BP) neural network. First, experiments are carried out based on ultra-depth-of-field detection technology, and different parameter combinations are used to measure the degree of abrasive belt wear. Then the effects of different grinding speeds, different contact pressures, and different work piece materials on the abrasive belt wear rate are obtained. It can be concluded that the abrasive belt wear rate gradually increases as the grinding speed of the abrasive belt increases. With the increase of steel grade, the hardness of the steel structure increases, which intensifies the abrasive belt wear. As the contact pressure increases, the pressure on a single abrasive particle increases, which ultimately leads to increased wear. With the increase of contact pressure, the increase of the wear rate of materials with higher hardness is greater. By utilizing the artificial intelligence BP neural network method, 18 sets of experiment data are used for training BP neural network while 9 sets of data are used for verification, and the nonlinear mapping relationship between various process parameter combinations such as grinding speed, contact pressure, workpiece material, and wear rate is established to predict the wear degree of abrasive belt. Finally, the results of verification by examples show that the method proposed in this paper can fulfill the purpose of quickly and accurately predicting the degree of abrasive belt wear, which can be used for guiding the manufacturing processing, and greatly improving the processing efficiency.

Experimental And Numerical Study On Surface Generated Mechanism of Robotic Belt Grinding Process Considering The Dynamic Deformation of Elastic Contact Wheel

In the robotic belt grinding process, the elastic contact condition between the flexible tool and the workpiece is a critical issue which extremely influences the surface quality of the manufactured part. The existing analysis of elastic removal mechanism is based on the statistic contact condition but ignoring the dynamic removal phenomenon. In this paper, we discussed the dynamic contact pressure distribution caused by the non-unique removal depth in the grinding process. Based on the analysis of the equivalent removal depth of a single grit and the trajectories of grits in manufacturing procedure, an elastic grinding surface topography model was established with the consideration of the dynamic contact condition in the removing process. Robotic belt grinding experiments were accomplished to validate the precision of this model, while the result showed that the surface roughness prediction error could be confined to 11.6%, which meant this model provided higher accuracy than the traditional predicting methods.

Prediction of Surface Roughness of Abrasive Belt Grinding of Superalloy Material Based on RLSOM-RBF

It is difficult to accurately predict the surface roughness of belt grinding with superalloy materials due to the uneven material distribution and complex material processing. In this paper, a radial basis neural network is proposed to predict surface roughness. Firstly, the grinding system of the superalloy belt is introduced. The effects of the material removal process and grinding parameters on the surface roughness in belt grinding were analyzed. Secondly, an RBF neural network is trained by reinforcement learning of a self-organizing mapping method. Finally, the prediction accuracy and simulation results of the proposed method and the traditional prediction method are analyzed using the ten-fold cross method. The results show that the relative error of the improved RLSOM-RBF neural network prediction model is 1.72%, and the R-value of the RLSOM-RBF fitting result is 0.996.