Abstract

This work presents a systematic and comprehensive investigation of the protective effect of built-up layer (BUL) in dry cutting of stainless steel SUS304. A detailed examination of BUL and built-up edge (BUE) formation conditions, their formation mechanisms, and their protective effect was carried out at different cutting speeds (5–140 m/min), and different feed rates (0.02–0.1 mm/rev). The uncoated cemented carbide tool was used as a cutting tool. The dimensions of BUL/BUE and tool wear were measured by scanning electron microscope (SEM) and laser confocal microscopy (LCM). The protective effect of BUL/BUE was characterized using flank wear progression, as well as crater wear progression, cutting force analysis, and surface roughness analysis. As a result, it was found that BUE forms around the cutting edge at low cutting speeds (5–20 m/min), and BUL, which resembles a water drop, forms on the tool rake face at cutting speeds equal to or above 40 m/min. And a thin layer of flank built-up (FBU) can form on the tool flank face as the cutting speed increases from 40 m/min to 140 m/min. The BUL/BUE formation mechanism was also confirmed. It was revealed that BUL can be considered as a protective layer, which can not only prevent the tool rake face from wear but also decrease the tool flank wear, but BUE can only prevent the crater wear; and to a certain extent, the thin layer of FBU can also work as a protecting layer on the worn tool flank face in dry cutting of SUS304. It was also revealed that the height of BUL plays a very important role in its protective effect. Meanwhile, it was found that BUL and the thin layer of FBU have no or few influences on the amplitude variation of cutting forces and on the surface roughness. These results indicated that BUL can be used to realize the self-protective tool (SPT) in cutting of difficult-to-cut material such as SUS034. In addition, the research also proved that it is necessary to take the influences of BUL, BUE, and FBU formations on tool wear into account in the tool wear model in order to achieve high-precision prediction.

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