IDR - IIT Kharagpur

High Efficiency Deep Grinding of Bearing Steel and Modelling for Specific Energy Requirement

High Efficiency Deep Grinding of Bearing Steel and Modelling for Specific Energy Requirement

 

Grinding is a machining process where material is removed by the micro milling action of abrasive grits having irregular geometry. Generally grinding is essentially carried out for finishing to desired dimensional and form accuracy and surface finish. Grinding is also being often used for bulk material removal. Initially for bulk material removal by grinding, the table speed or work speed was kept very low and it was named as creep feed grinding. Recently another grinding technique has been introduced where grinding velocity, (Vc), is kept high and the table or work speed,(vw), as well as infeed, (a) are all kept moderately high to achieve high productivity. This is called High Efficiency Deep Grinding (HEDG) which provides good surface integrity along with high productivity. High Efficiency Deep Grinding is carried out using superabrasive wheels at grinding velocity above 120 m/s in highly rigid and preferably CNC controlled machine tools. Such sophisticated and expensive grinding for high accuracy, finish and productivity essentially necessitates its proper use with appropriate wheels like cBN and diamond, values of speed and feed and environment. Proper utilization of HEDG for desired benefits need modelling of grinding forces, specific energy, grinding temperature and surface integrity. This thesis presents in detail the experimental observations related to grinding forces, specific energy requirement and chips formed during HEDG of bearing steel having hardness, RC 60, RC 40 and RC 32. An analytical model of specific energy and validation of the model has been also done for grinding bearing steel of different hardness with single layer cBN wheels at a grinding velocity of 80 m/s to 160 m/s, table speed of 0.5 m/min to 1 m/min and downfeed (infeed) of 0.25 mm to 0.75 mm. The grinding surface temperature was estimated using an analytical modelling method and the results are systematically represented in this thesis.

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