Task Automation for modelling deflection prediction on machining thin-wall part with Catia V5

Accuracy of machined components is one of the most critical considerations for many manufacturers especially in aerospace industry where most of the part used a thin-walled monolithic structure. However, because of the poor stiffness of thin-wall part, deformation is more likely to occur in the machining process which resulting a dimensional form errors and has been largely ignored by CAD/CAM software. This paper proposed task automation for predicting the surface errors when machining a thin-wall low rigidity component. The proposed model would be an efficient means for analysing the root cause of errors induced during machining of thin-wall parts and provide an input for downstream decision making on error compensation. A set of machining tests have been done in order to validate the accuracy of the model and the results between simulation and experiment were in good agreement.