Machining feature recognition is an essential way to realize the integration of CAD/CAM

and is significant to improve the quality and efficiency of CNC machining of the aircraft structural parts

which contains plenty of complex interacting features with complex structure. Existing machining feature recognition methods require predefinition of feature interacting patterns before recognizing interacting features

which is difficult to meet the recognition requirements of complex interacting features of the aircraft structural parts. Therefore

a data-driven method for machining feature recognition for aircraft structural parts is proposed

which transforms the problem of machining feature recognition into a graph learning problem

and adaptively learns feature recognition rules from historical process data by constructing a graph neural network model. The proposed method breaks the limits of the pre-defined feature interacting patterns. With typical aircraft structural parts as test parts

the correct recognition rates of isolated features

interacting features and total machining features reach 98.11%

94.62% and 96.18%

respectively

which verifies the effectiveness of the proposed method.