Quality prediction of multistage machining processes based on assigned error propagation network

It is the key issue to predict the machining quality in real time for machining quality control in multistage machining processes(MMPs). For aircraft manufacturing, the characteristics of special and large space size, hard machining materials, and small batch processing always lead to insufficient sample data and difficult monitoring of machining error. Considering the above issue, a quality prediction method is proposed based on assigned error propagation network(AEPN) in MMPs. Quality features(QFs) are introduced into a machining error propagation network(MEPN) for describing the influence relation between each node in machining process, and an AEPN is constructed too. Based on key QF nodes, a single process predict model(SPPM) is established by employing the support vector regression machine(SVRM), which is optimized by the particle swarm optimization(PSO) algorithm. Based on this, the SPPM is merged based on the topology structure of the AEPN, and a multi-processes predict model(MPPM) is further constructed, A software platform for machining quality prediction in MMPs is developed, and a landing gear part is used to verify the applicability of the above method. The result shows that these methods can effectively predict machining error and provide foundation for the machining process control of special parts from the perspective of MMPs.