Surface roughness online prediction using parallel ensemble learning in robotic side milling for aluminum alloy

Ci Song, Zhibing Liu, Xibin Wang, Tianyang Qiu*, Zhiqiang Liang, Wenhua Shen, Yuhang Gao, Senjie Ma

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Robotic machining has the advantages of large workspace and high flexibility, and the acquisition of high surface quality parts has become the research focus. To realize surface roughness online prediction in robotic side milling for aluminum alloy, an intelligent model driven by cutting vibration was adopted. Combined with variational modal decomposition (VMD) and fast fourier transformation (FFT), a rough and fine two-layer decomposition strategy of stable cutting vibration signals was proposed, which can effectively avoid the adverse effects of modal aliasing and endpoint action on the extraction of high-sensitivity features. Based on support vector machine (SVM), random forest (RF) and extreme learning machine (ELM), weighted reconstruction and voting selection were introduced to form a parallel ensemble learning model. Considering the posture influence in the sample construction, the machining system vibration state within common range was fully reflected by orthogonal cutting experiments at the corresponding postures of envelope zone vertices. For the setting process parameters, the obtained surface roughness was within 1.62–2.90 μm. Experiments under the testing postures showed the root mean square error (RMSE) and mean absolute error (MAE) of the model were 0.087 μm and 0.076 μm. The average relative error of 3.57 % and maximum relative error of 8.65 % had shown that the model obtained better surface roughness prediction ability. Finally, the influence significance and the factor action trend between cutting parameters and surface roughness were explored by range analysis, which has been proved by experiments that they can provide guidance for the dynamic regulation of cutting parameters.

Original languageEnglish
Article number112932
JournalMechanical Systems and Signal Processing
Volume235
DOIs
Publication statusPublished - 15 Jul 2025
Externally publishedYes

Keywords

  • Ensemble learning
  • Robotic side milling
  • Surface roughness prediction

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