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

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Abstract

Surface roughness is a crucial factor affecting the surface quality of workpieces in 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 electromagnetic 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 nonlinear relationship between process parameters and response with the highest determination coefficient (R2) of 0.9340 and the lowest mean squared error (MSE) of 1.3037 \(\times\) 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. It showed that the maximum change in surface roughness was 0.530 at particle length of 3 mm, particle diameter of 0.7 mm, particle weight of 1.3 kg, liquid water quantity of 1.0 l, a rotational speed of 1323 rpm, working time of 35 min, and initial surface roughness of 2.5478 m\(\upmu\).

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Authors and Affiliations

  1. Department of Mechanical Engineering, Pukyong National University, Busan, 48547, Korea

    Jung-Hee Lee, Yun-Su Seo & Jae-Seob Kwak

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  1. Jung-Hee Lee
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  2. Yun-Su Seo
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  3. Jae-Seob Kwak
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LJH and SYS performed experiments and data analysis. In addition, they contributed to writing the paper. KJS supervised the project and reviewed the paper.

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Correspondence to Jae-Seob Kwak.

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Lee, JH., Seo, YS. & Kwak, JS. Surface roughness prediction and optimization in the REMF process using an integrated DBN-GA approach. Int J Adv Manuf Technol 121, 5931–5942 (2022). https://doi.org/10.1007/s00170-022-09652-9

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  • DOI: https://doi.org/10.1007/s00170-022-09652-9

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