A real time machining error compensation method based on dynamic features for cutting force induced elastic deformation in flank milling

ABSTRACT

During the machining process, cutting forces cause deformation of thin-walled parts and cutting tools because of their low rigidity. Such deformation can lead to undercut and may result in defective parts. Since there are various unexpected factors that affect cutting forces during the machining process, the error compensation of cutting force induced deformation is deemed to be a very difficult issue. In order to address this challenge, this article proposes a novel real time deformation error compensation method based on dynamic features. A dynamic feature model is established for the evaluation of feature rigidity as well as the association between geometric information and real time cutting force information. Then the deformations are calculated based on the dynamic feature model. Eventually, the machining error compensation for elastic deformation is realized based on Function Blocks. A thin-walled feature is used as an example to validate the proposed approach. Machining experiment results show that the errors of calculated deformation with the monitored deformation is less than 10%, and the thickness errors were between −0.05 mm and +0.06 mm, which can well satisfy the accuracy requirement of structural parts by NC (Numerical Control) machining.

KEYWORDS:

• Cutting force
• dynamic feature
• elastic deformation
• machining error compensation
• real time

Nomenclature list

DF	=	Dynamic Feature;
CIFG	=	Continuous Interim Feature Geometry;
AR	=	Association Relationship;
CP	=	the point on the contact curve of the tool path with respect to the interim feature;
PI	=	the parameter information of each individual point;
K	=	Flexural rigidity;
DFFB	=	Dynamic Feature Function Block;
SFB	=	Service Function Block;
E	=	Young's modulus of the material;
Iz	=	the moment of inertia of the beam section relative to the Z axis.

Additional information

Funding

The research work presented in this article was primarily supported by the National Natural Science Foundation of China–China Aerospace Science and Technology Corporation on Advance Manufacturing (Ref: U1537209), National Natural Science Foundation of China (Ref: 51375239), and Jiangsu Province Outstanding Youth Fund (Ref: BK20140036). It was also partially supported by the AUTO21 Network of Centres of Excellence, Canada (F508-FWS).