Analysis and prediction of contact fault detection in automatic flexible polishing for complex surfaces based on WOA-SVM

Purpose On account of the flexibility, large working space and system openness, manipulators are often adopted in automatic grinding and polishing operations. In the flexible roboticized polishing process for complex surfaces with narrow spatial structures, such as aero-engine blades, the contact mode between the tool and the workpiece changes with the transformation of the manipulator’s end posture and the alternation of the workpiece curvature, which often leads to processing contact faults. These faults result in the obsolescence of expensive aerospace components and reduced efficiency. The purpose of this study is to collect vibration signals during the machining process and extract fault characteristic parameters for monitoring and diagnosis for diagnosing faults in automated flexible polishing to protect the workpiece. Design/methodology/approach This paper proposes a whale optimization algorithm (WAO)-support vector machine model based on the support vector machine and WAO. From the original grinding and polishing vibration signal, 11 time-domain features that can reflect the fluctuation of the vibration signal are extracted as detection features. Findings Experimental results indicate that this method effectively reflects the relationship between contact faults and diagnostic results, demonstrating good real-time performance and diagnostic capability. Originality/value This method provides a crucial theoretical basis for real-time fault diagnosis and monitoring in automatic flexible machining, ensuring reliable automatic flexible polishing processes.