Research on surface roughness prediction in turning Inconel 718 based on Gaussian process regression

Abstract

Nickel-based alloy Inconel 718 is widely used in aircraft engine industry because of its good mechanical properties. Inconel 718 is a typical difficult-to-machine material and its price is relatively expensive. Therefore, accurate prediction of Inconel 718 machined surface roughness with small sample space can improve machining efficiency, optimize process parameters and reduce machining cost. In this paper, a method is proposed to characterize the influence of cutting parameters on roughness by stablishing the corresponding relationship between the proportional hyperparameters in the multivariate kernel function and the cutting speed, cutting deep, feed rate and the rake angle of the tool. A multi input single output (MISO) multivariate Gaussian process regression（GPR）surface roughness prediction model with cutting speed, cutting depth, feed rate and tool rake angle as input variables and surface roughness as output variables is established. The model can not only output the predicted value of surface roughness, but also give the reliability of the predicted value. Experimental results show that the proportional hyperparameter has an independent adjustment function, and the influence of the process parameters characterized by the proportional hyperparameter on the surface roughness is consistent with the experimental results. The experimental results show that the average relative error of MISO multivariate GPR surface roughness prediction model proposed in this paper is 1.5%, which can accurately predict the surface roughness in small sample space.

Study on interfacial leakage characteristics of rubber sealing under temperature cycle conditions in PEM fuel cell

“…Considering the strong dependence between contact state and interfacial leakage, a microporous structure consisting of composite rough and smooth surfaces in contact is used as a geometric model for interfacial leakage. A numerical technique based on the fast Fourier transform, widely used in mechanical engineering [40][41][42], is used to create the composite rough surface. The roughness of the surfaces is assumed to follow a Gaussian distribution [43].…”

The Modelling of Surface Roughness After the Turning of Inconel 601 by Using Artificial Neural Network

“…The results achieved to demonstrate the effectiveness of three methods that have led to similar results represented by the optimal parameters. Hao et al [20] established a multi input single output (MISO) multivariate Gaussian process regression (GPR) surface roughness prediction model with cutting speed, depth of cut, feed and rake angle as input variables and surface roughness as output variable. The experimental results showed that the average relative error of MISO multivariate GPR surface roughness prediction model was 1.5%.…”