Surface roughness prediction and optimization in the REMF process using an integrated DBN-GA approach

Abstract

Surface roughness is a crucial factor affecting the surface quality of workpieces in the manufacturing industries. Thus, it is important to provide an accurate performance of surface roughness prediction and optimal parameters to reduce the burden of time and costs during the process. In this study, two predict models, namely multiple linear regression and deep belief network(DBN) models, were performed to accurately predict change in surface roughness in the rotational-electro magnetic finishing(REMF). Compared to the statistical-based model, the data-driven model based on the DBN architecture was a significantly considerable effect on surface roughness prediction in the REMF process. Among the considered DBN models, DBN5 architecture as [7,14,14,1] showed effective features of the non-linear relationship between process parameters and response with the highest determination coefficient(R 2 ) of 0.9340 and the lowest mean squared error(MSE) of 1.3037×10 -3 in the testing datasets. In addition, a genetic algorithm(GA) as a heuristic optimization technique was adopted to optimize the input parameters of the best derived DBN model. As a result, it proved that the DBN model integrated GA was able to be adopted for the accurate prediction of surface roughness and process optimization.

Prediction of surface roughness based on fused features and ISSA-DBN in milling of die steel P20

“…DBN is a probabilistic generative network consisting of a series of Restricted Boltzmann Machine (RBM) and BP neural networks 30 . As shown in Fig.…”