Grain erosion wear properties and grinding performance of porous aggregated cubic boron nitride abrasive wheels

Cubic boron nitride(cBN) superabrasive grinding wheels exhibit unique advantages in the grinding of difficult-to-cut materials with high strength and toughness,such as titanium alloys and superalloys.However,grinding with multilayered metallic cBN superabrasive wheels faces problems in terms of grain wear resistance,the chip storage capability of the working layers and the stability and controllability of the dressing process.Therefore,in this work,novel metallic cBN superabrasive wheels with aggregated cBN(AcBN) grains and open pore structures were fabricated to improve machining efficiency and surface quality.Prior to the grinding trials,the airborne abrasive blasting process was conducted and the abrasive blasting parameters were optimized in view of wear properties of cBN grains and metallic matrix materials.Subsequently,the comparative experiments were performed and then the variations in grinding force and force ratio,grinding temperature,tool wear morphology and ground surface quality of the multilayered AcBN grinding wheels were investigated during machining Ti-6Al-4V alloys.In consideration of the variations of grain erosion wear volume and material removal rate per unit of pure metallic matrix materials as the abrasive blasting parameters changes,the optimal abrasive blasting parameters were identified as the SiC abrasive mesh size of 60# and the abrasive blasting distance and time of 60 mm and 15 s,respectively.The as-developed AcBN grains exhibited better fracture toughness and impact resistance than monocrystalline cBN(McBN) grains because of the existence of metal-bonded materials amongst multiple cBN particles that decreased crack propagation inside whole grains.The metallic porous AcBN wheels had lower grinding forces and temperature and better ground surface quality than vitrified McBN wheels due to the constant layer-by-layer exposure of cBN particles in the working layer of AcBN wheels.