Roundness prediction in centreless grinding using physics-enhanced machine learning techniques

Abstract

This work proposes a model for suggesting optimal process configuration in plunge centreless grinding operations. Seven different approaches were implemented and compared: first principles model, neural network model with one hidden layer, support vector regression model with polynomial kernel function, Gaussian process regression model and hybrid versions of those three models. The first approach is based on an enhancement of the well-known numerical process simulation of geometrical instability. The model takes into account raw workpiece profile and possible wheel-workpiece loss of contact, which introduces an inherent limitation on the resulting profile waviness. Physical models, because of epistemic errors due to neglected or oversimplified functional relationships, can be too approximated for being considered in industrial applications. Moreover, in deterministic models, uncertainties affecting the various parameters are not explicitly considered. Complexity in centreless grinding models arises from phenomena like contact length dependency on local compliance, contact force and grinding wheel roughness, unpredicted material properties of the grinding wheel and workpiece, precision of the manual setup done by the operator, wheel wear and nature of wheel wear. In order to improve the overall model prediction accuracy and allow automated continuous learning, several machine learning techniques have been investigated: a Bayesian regularized neural network, an SVR model and a GPR model. To exploit the a priori knowledge embedded in physical models, hybrid models are proposed, where neural network, SVR and GPR models are fed by the nominal process parameters enriched with the roundness predicted by the first principle model. Those hybrid models result in an improved prediction capability.

Continuous multi-angle variation (CMAV) for faster roundness correction in centreless grinding

“…In this section a series of roundness simulations are obtained base on a model proposed in [16]. The obtained process model is represented by the block diagram of Figure 7.…”

Estimation method for geometric stability during shoe-type centerless grinding of rolling bearing parts

“…However, the development of computer modeling has contributed to the formation of new research methods and models in the last 20 years. For example, in the articles of Safarzadeh et al (2021), Galleg (2007), Chang et al (2017) and Zhang et al (2022) pose the problem of optimal setting of the grinding machine according to one or more criteria.…”

Profile Evolution and Cross-Process Collaboration Strategy of Bearing Raceway by Centerless Grinding and Electrochemical Mechanical Machining

“…The rounding mechanism of centerless positioning processing determines its unique roundness profile formation and genetic characteristics. How to eliminate the roundness error of centerless positioning has become one of the important issues in the field of high-precision rotary parts manufacturing [ 10 , 11 , 12 ]. Stancekova et al [ 13 ] analyzed the influences of revolution of the feed roll, grinding wheel speed and height of the guide bar parameters on cylindricity deviation during centerless grinding of 50CrMo4 steel shaft.…”