1. Accurate prediction of machining feedrate and cycle times considering interpolator dynamics
- Author
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Burak Sencer, Rob Ward, Erdem Ozturk, and Bryn Jones
- Subjects
0209 industrial biotechnology ,Finite impulse response ,Computer science ,Mechanical Engineering ,02 engineering and technology ,Kinematics ,Linear interpolation ,Industrial and Manufacturing Engineering ,Computer Science Applications ,020303 mechanical engineering & transports ,020901 industrial engineering & automation ,0203 mechanical engineering ,Machining ,Control and Systems Engineering ,Control theory ,Path (graph theory) ,Numerical control ,Trajectory ,Software ,Interpolation - Abstract
This paper presents an accurate machining feedrate prediction technique by modelling the trajectory generation behaviour of modern CNC machine tools. Typically, CAM systems simulate machines’ motion based on the commanded feedrate and the path geometry. Such approach does not consider the feed planning and interpolation strategy of the machine’s numerical control (NC) system. In this study, trajectory generation behaviour of the NC system is modelled and accurate cycle time prediction for complex machining toolpaths is realised. NC system’s linear interpolation dynamics and commanded axis kinematic profiles are predicted by using finite impulse response (FIR)–based low-pass filters. The corner blending behaviour during non-stop interpolation of linear segments is modelled, and for the first time, the minimum cornering feedrate that satisfies both the tolerance and machining constraints has been calculated analytically for 3-axis toolpaths of any geometry. The proposed method is applied to 4 different case studies including complex machining toolpaths. Experimental validations show actual cycle times can be estimated with > 90% accuracy, greatly outperforming CAM-based predictions. It is expected that the proposed approach will help improve the accuracy of virtual machining models and support businesses’ decision-making when costing machining processes.
- Published
- 2021