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.

To address high-precision robotic abrasive belt grinding of 42CrMo steel, this study adopted the orthogonal central composite design with grinding force, feed rate, and rotational speed as key parameters, establishing and verifying regression models for material removal depth (MRD) and surface roughness (Ra). Results showed that the models’ relative errors are within 6% (MRD) and 10% (Ra). Grinding force and feed rate exert a strong coupling effect on MRD, while feed rate–rotational speed and grinding force–feed rate interactions significantly influence Ra, with “saddle-shaped” response surfaces. Grey relational analysis determined the optimal parameters: 75 N grinding force, 22.4 mm·s⁻¹ feed rate, and 3261 rpm rotational speed, achieving a maximum MRD of 1.975 mm and a minimum Ra of 3.506 μm. The tolerance range of the optimal parameters is F = 70–80 N, v_f = 20–24 mm·s⁻¹, and ω = 3000–3500 rpm. This research provides robust support for process parameter prediction and optimization in high-precision robotic abrasive belt grinding of 42CrMo steel.