Model-based optimization of micromilling AISI H11 tool steel: A comprehensive study of wear and its impact on surface quality

Micromilling is characterized by its ability to machine complex shapes from hardened tool steels while achieving superior surface quality. However, suitable process design is crucial to prevent critical tool loads that can accelerate tool wear, depending on the removal rate and cutting edge engagement. Various wear mechanisms can impair tool performance, particularly when machining difficult-to-cut materials. A deep understanding of process load, wear development, and its impact on surface quality is essential for optimizing machining performance and efficiency. This experimental study investigates the machinability of AISI H11 hot-work tool steel (50.9 HRC) using a micromilling process with PVD-TiAlN coated micro-end milling cutters d = 1 mm. A statistical design of experiments varied depth of cut, width of cut, and feed per tooth. Cutting forces were measured to analyze tool load, while surface topography and microhardness of the surface layer were evaluated. Tool wear was monitored, and empirical models focused on flank wear were developed. The results demonstrated that under finishing process conditions, especially with a depth of cut ( a p ) and feed per tooth ( f z ) below 15 μm, cutting force values were comparatively low ( F R = 1.96 N). However, as tool wear progressed, increased ploughing effects raised specific cutting forces, resulting in an unsuitable process load. In contrast, highly productive process parameters ( a p = 95 μm), ( f z = 65 μm) resulted in higher cutting forces (up to 23.7 N), which, when increased by wear, led to overload-induced tool failure. Optimized settings promoted continuous abrasion as the dominant wear mechanism, significantly extending tool life. Tool tests revealed that wear-induced modifications impacted machining outcomes, improving surface roughness and increasing passive forces while enhancing microhardness. Overall, these findings highlight the effectiveness of model-based parameter optimization in enhancing micromilling performance and tool longevity.