AUTHOR=Qiao Huaying , Ramli Rizauddin , Zheng Liancheng , Zhao Jiutao 
  
TITLE=Hybrid fuzzy-SVM collaborative reasoning framework for intelligent CNC turning process planning
  
JOURNAL=Frontiers in Mechanical Engineering
  
VOLUME=Volume 12 - 2026
  
YEAR=2026
  
URL=https://www.frontiersin.org/journals/mechanical-engineering/articles/10.3389/fmech.2026.1750884
  
DOI=10.3389/fmech.2026.1750884
  
ISSN=2297-3079
  
ABSTRACT=IntroductionThe optimization of machining process decision-making remains a major challenge in intelligent manufacturing due to the uncertainty of process information, incompleteness of rule bases, and the tendency of traditional algorithms to converge to local optima. Therefore, enhancing the adaptability and robustness of decision-making systems is a crucial task for achieving efficient and reliable computer numerical control (CNC) process planning.MethodsThis study proposes a hybrid decision-making approach that integrates fuzzy theory with support vector machines (SVM) to address uncertainty and incomplete knowledge representation in CNC turning. An Analytic Hierarchy Process (AHP) is used to determine the relative importance of influencing factors, and trapezoidal membership functions are designed to determine the credibility of fuzzy reasoning rules. When the credibility value falls below a defined threshold, a linear-kernel SVM model is activated to provide alternative decisions which formed a fuzzy-SVM collaborative reasoning mechanism.ResultsExperimental validation demonstrates that the proposed hybrid fuzzy-SVM collaborative method achieves remarkable classification accuracy on the test dataset. The system maintains stable performance even under low-credibility or incomplete rule conditions. The SVM module effectively compensates for the limitations of the fuzzy reasoning process, thereby improving the robustness of decision inference compared to single-model approaches.ConclusionThe proposed fuzzy-SVM collaborative reasoning framework enhances the adaptability, stability, and interpretability of CNC machining process decision-making. These findings offer a practical and scalable solution for intelligent process planning in complex and uncertain manufacturing environments.