Finding dimensional stability considering deflection effects in cylindrical plunge grinding

Abstract

In spite of the existence of several models to effectively design grinding cycles, they are not frequently adopted in production lines, due to the lack of knowledge, difficulties in determining some input parameters and discrepancies observed in the obtained results during the production of a batch of parts. This paper proposes a contribution to the modeling and simulation of cylindrical plunge grinding cycles in terms of dimensional stability of the process, prediction of the workpiece diameter along the grinding cycle and the minimum required spark-out time, incorporating into the model grinding tolerance limits according to the chosen capability scenario. The prediction of the part diameter and the spark-out time in a cylindrical plunge grinding operation is the key parameter to design optimized grinding cycles. The proposed model covers typical fast grinding cycles in industry, in which the spark-in time is not long enough to guarantee that at its end, the real workpiece radius reduction speed is very close to the programmed feed rate. In the proposed model, the remaining radial stock to be removed during the spark-out stage is designed to be equal to or lower than the tolerance limit for the grinding operation, but considering different scenarios of capability index: barely capable process (C p = 1); capable (C p = 1.3); and six-sigma quality level (C p = 2.0). The minimum spark-out time necessary to reduce the total radial stock to the specified capability limit is calculated by the simulation. Experimental plunge cylindrical grinding tests were performed in which the reduction in the workpiece diameter was measured using an in-process diameter gauge, varying the in-feed velocity and the spark-out time. As a result, a reasonable compromise between the proposed theoretical model and the experimental data was achieved. The dependency of the "time constant" and the in-feed speed was demonstrated for fast spark-in cycles. It was also observed that longer spark-out times will be needed to achieve tighter capability indexes for a given in-feed speed and "time constant" value.

Parameter Identification of Cutting Forces in Crankshaft Grinding Using Artificial Neural Networks

“…Analysis of many scientists’ research results has shown that much attention is paid to the intensification of cylindrical infeed grinding processes and control of cutting forces in this machining method. In particular, S. da Silva et al [ 21 ] noted that despite the existence of several models for effective grinding cycle design, they are not frequently adopted in production lines due to the lack of knowledge, difficulties in determining some input parameters, and discrepancies observed in the obtained results during the production of a batch of parts. That is why this difficult and time-consuming process requires careful preparation for implementing the machining of out-of-repair machine parts even at the stage of pre-production engineering.…”