Surface roughness is primarily used to evaluate the surface finish of a workpiece, which significantly affects the quality performance of the finished components. Effective surface roughness prediction can reduce the time of trial-and-test, thus increasing productivity and reducing costs. Although earlier studies have proposed many different prediction approaches for different machining methods, few studies have been done to develop a generic approach for surface roughness prediction. Therefore, this paper presents a generic evolutionary ensemble learning (GEEL) framework for surface roughness prediction of different kinds of manufacturing process. The GEEL framework for surface roughness prediction consists of three modules, including pre-processing module, multi-algorithm regression module, and GA-based ensemble learning module. Validation experiments were conducted on fluid jet polishing (FJP) of 3D-printed Cobalt Chrome (CoCr) alloy and other seven cases of different machining methods. The results indicate that the mean square error (MSE) and mean absolute error (MAE), obtained by the proposed method were reduced to a certain extent than typical methods in predicting the surface roughness, exhibiting excellent prediction performance and robustness. At the same time, the method has good extensibility and is expected to become a generic framework for surface roughness prediction in manufacturing.
|Number of pages
|International Journal of Computer Integrated Manufacturing
|Early online date
|26 Apr 2023
|Published - 1 Nov 2023
Bibliographical noteFunding Information:
The work described in this paper was mainly supported by the Natural Science Foundation of Fujian Province of China (Project No: 2020J01697) and the Natural Science Foundation of Guangdong Province of China (Project No: 2019A1515012015). The research was partially supported by the National Natural Science Foundation of China (Project No. 52105534). The work was also supported by the Research and Innovation Office of the Hong Kong Polytechnic University (Project No: BBXL, BD9B and TA34).