Seamless flatness rollers are the core equipment for cold-rolled strip flatness detection. The change in wear resistance of the roller surface-strengthened layer has important guiding significance for the prediction of the roller surface life. The experimental results of the five rounds of friction and wear testing show that the volume–wear rate of the original roller surface is only 1.85 × 10⁻⁶ mm³ (N·M)⁻¹. After the first grinding, the wear resistance of the roller surface decreased and the wear rate was approximately 2.76 × 10⁻⁶ mm³ (N·M)⁻¹. After the second grinding, the wear morphology, wear rate and wear resistance of the samples in each round showed little difference. The average wear rate in the last three rounds of the test is 3.56 × 10⁻⁶ mm³ (N·M)⁻¹ and the wear resistance is significantly lower than that of the previous two rounds of the test. The calculation shows that the original surface wear resistance is 32.92% higher than that of the surface after the first grinding, and the average wear resistance of the surface after the first grinding is 22.59% higher than that of the surface after the second, third and fourth grinding. With long-term use and repeated grinding repair, the roller surface will show a trend of wear resistance decline. When the wear resistance decreases to a certain degree, it will reach a stage of stability. These phenomena provide regular and quantitative references for the study of the life prediction of flatness meter rollers.

The seamless flatness roll is a critical inspection device in cold-rolled strip flatness control systems. Prolonged service causes cracks and scratches on the roll surface, while repeated grinding gradually removes the hardened layer, potentially rendering the roll unusable. To address the risk of thermal damage to internal sensors during the laser cladding repair of seamless flatness rolls, this study proposes a process optimization strategy using the Finite Element Method (FEM) and Response Surface Methodology (RSM). Focusing on an 820 mm roll, a regression prediction model for laser spot and internal component temperatures was constructed using a Box–Behnken design (BBD) based on an experimentally calibrated FEM model. Multi-objective optimization determined the optimal process parameters: laser power of 1.43 kW, laser radius of 3.73 mm, scanning speed of 23.45 mm/s, and overlap rate of 50.40%. Under these conditions, the average error between the predicted and experimental results was only 4.14%. The results confirm that the optimized process ensures the formation of a molten pool while maintaining internal components within safety thresholds, validating the feasibility of this non-destructive repair method.