Pneumatic polishing tools are commonly used in traditional robot mold polishing systems, but they have problems with the stable control of mold surface roughness due to low precision and poor adaptability in polishing force adjustment. The integration of an adaptive hydraulic polishing (AHP) tool and robot system effectively solves the above problems, providing a robust solution for the high-precision polishing of various molds. This study systematically investigates the robotic polishing of NAK80 mold steel using an AHP-equipped robotic platform with 3M abrasive discs of progressively refined grit sizes (P180, P400, P800). Through single-factor experiments and response surface methodology, the effects of polishing force, rotational speed, and feeding speed on surface roughness were quantitatively analyzed. The relationship between surface roughness and the polishing parameters was derived to elucidate the roughness evolution before and after over-polishing. Orthogonal experiments combined with range analysis identified optimal parameter combinations for P180 (20 N polishing force, 5000 RPM rotational speed, and 5 mm·s⁻¹ feeding speed) and P400 abrasives (10 N polishing force, 4000 RPM rotational speed, and 5 mm·s⁻¹ feeding speed), achieving minimum surface roughness values of 0.08 µm and 0.044 µm, respectively. For P800 abrasives, a central composite design was used to develop a roughness prediction model with a ≤7.14% relative error, and the optimal parameters are a 20 N polishing force, a 5000 RPM rotational speed, and a 5 mm·s⁻¹ feeding speed. The sequential application of the optimized parameters across all the grit sizes can reduce the surface roughness from an initial 0.4 µm to a final 0.017 µm, representing a 95.75% improvement in the surface finish.

Robotic grinding serves as a pivotal embodiment and key technological support of Industry 4.0. Elucidating the influence of robotic grinding parameters on the material removal depth (MRD) of 42CrMo steel and optimizing these parameters are critical to enhancing grinding efficiency and quality. In this study, the influences of revolution speed, feed speed, grinding force, and grit designation on MRD and surface Vickers hardness of 42CrMo steel were investigated by using an adaptive electro-hydraulic-actuated triangular abrasive belt in robot grinding. A predictive model for MRD of 42CrMo steel has been established using the orthogonal central composite design method. The results indicated that as the revolution speed or grinding increases, both MRD and surface hardness increase. However, as the revolution speed surpasses 4000 RPM or the grinding force exceeds 60 N, the increase of MRD becomes slower due to the increase in surface hardness. Both the MRD and surface hardness decrease continuously as the feed speed increases, and once it exceeds 15 mm·s⁻¹, the decrease of the MRD becomes slow. The rise in grit designation of the abrasive belt makes the MRD reduce gradually while the surface hardness rises slightly. The correlation coefficient of the predictive model is 0.9387, and the relative error between the predicted and experimental MRD is within 10%, indicating a relatively high accuracy. At the optimal grinding parameters (grinding force of 81 N, revolution speed of 4739 RPM, and feed speed of 7.6 mm·s⁻¹), the maximum MRD of 42CrMo steel achieved by an abrasive belt of 60 grit designation is 0.934 mm. This work provides a basis for high-precision robot abrasive belt grinding of 42CrMo steel.

The removal of primer from aircraft skin epoxy resin primer plates was investigated by using an adaptive hydraulically controlled polishing tool in conjunction with an industrial robot. This study examined the effects of several key process parameters—grinding force, rotational speed, feed speed, and abrasive grit size—on primer removal depth and surface roughness. Through both single-factor analysis and response surface methodology (RSM), the variation patterns of removal depth and surface roughness with respect to these parameters were elucidated. RSM was employed to develop regression models for the primer removal depth and removal rate. The relative errors of these regression models were found to be within 8%, while the maximum relative error of the backpropagation neural network prediction model for surface roughness Ra is 9.5%. These models exhibit high accuracy in predicting the material removal depth, material removal depth rate, and surface roughness of the primer plates. The optimal parameters for the adaptive hydraulically controlled polishing tool were identified as flows: a polishing force of 20 N, a feed speed of 40 mm·s⁻¹, a rotational speed of 2000 rpm, and 80-grit sandpaper. Under these conditions, the maximum removal depth reaches 27.5 µm, the highest removal rate is 5.501 µm·s⁻¹, and the surface roughness Ra is 1.897 µm.