Abstract
The profile of flutes has a great influence on the stiffness and chip-removal capacity of end-mills. Generally, the accuracy of flute parameters is determined by the computer numerical control (CNC) grinding machine through setting the wheel’s location and orientation. In this work, a novel algorithm was proposed to optimize the wheel’s location and orientation for the flute-grinding to achieve higher accuracy and efficiency. Based on the geometrical constraint that the grinding wheel should always intersect with the bar-stock while grinding the flutes, the grinding wheel and bar-stock were simplified as an ellipse and circle via projecting in the cross-section. In light of this, we re-formulated the wheel’s determination model and analyzed the geometrical constraints for interference, over-cut and undercut in a unified framework. Then, the projection model and geometrical constraints were integrated with the evolution algorithm (i.e., particle swarm optimization (PSO), genetic algorithm (GA) for the population initialization and local search operator so as to optimize the wheel’s location and orientation. Numerical examples were given to confirm the validity and efficiency of the proposed approach. Compared with the existing approaches, the present approach improves the flute-grinding accuracy and robustness with a wide range of applications for various flute sizes. The proposed algorithm could be used to facilitate the general flute-grinding operations. In the future, this method could be extended to more complex grinding operations with the requirement of high accuracy, such as various-section cutting-edge resharpening.
Highlights
Flutes, as the major structure of end-mills, play an important role in the cutting performance [1,2,3,4].A flute can be defined by the following three parameters: core radius, flute angle and rake angle [5,6,7].The rake angle influences the cutting force, while the core radius and the helix angle determine the stiffness and chip-removal capacity of the cutters
In view of the above survey, it can be seen that the current study has addressed the modeling of flute-grinding well, but fast and stable algorithms are still required for further study
An initial points generation algorithm was proposed in Algorithm 1, which could be used for the population initialization for GA or particle swarm optimization (PSO)
Summary
As the major structure of end-mills, play an important role in the cutting performance [1,2,3,4]. Kim et al [11] developed a simulation method with Boolean operations to construct the helix motion and developed an iterative process to compute the wheel geometry and location data This method could be used for virtual cutting tests in the CAM system, it is time-consuming to achieve a high machining accuracy. To improve the precision of generated flutes, Li [12] established a novel algorithm to calculate the numerical data of flutes based on the enveloping theory, in which the flute profile was interpolated with appropriate discrete points using the cubic polynomial expression This method might be invalid for grinding with a bevel-type wheel. The desired flute profile can be viewed as an optimization problem with regards to the wheel’s shape and configuration To solve this problem, Chen et al [16] proposed an iteration algorithm to determine the wheel location and orientation.
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