A Data-Driven Machining Error Analysis Method for Finish Machining of Assembly Interfaces of Large-Scale Components

To guarantee the final assembly quality of the large-scale components, the assembly interfaces of large components need to be finish-machined on site. Such assembly interfaces are often in low-stiffness structure and made of difficult-to-cut materials, which makes it hard to fulfill machining tolerance. To solve this issue, a data-driven adaptive machining error analysis and compensation method is proposed based on on-machine measurement. Within this context, an initial definite plane is fitted via an improved robust iterating least-squares plane-fitting method based on the spatial statistical analysis result of machining errors of the key measurement points. Then, the parameters of the definite plane are solved by a simulated annealing-particle swarm optimization (SA-PSO) algorithm to determine the optimal definite plane; it effectively decomposes the machining error into systematic error and process error. To reduce these errors, compensation methods, tool-path adjustment method, and an optimized group of cutting parameters are proposed. The proposed method is validated by a set of cutting tests of an assembly interface of a large-scale aircraft vertical tail. The results indicate that the machining errors are successfully separated, and each type of error has been reduced by the proposed method. A 0.017 mm machining accuracy of the wall-thickness of the assembly interface has been achieved, well fulfilling the requirement of 0.05 mm tolerance.