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The optimal feedrate planning on five-axis parametric tool path with geometric and kinematic constraints for CNC machine tools

Author

Listed:
  • Huan Liu
  • Qiang Liu
  • Pengpeng Sun
  • Qitong Liu
  • Songmei Yuan

Abstract

The optimal feedrate planning on five-axis parametric tool path with multi-constraints remains challenging due to the variable curvature of tool path curves and the nonlinear relationships between the Cartesian space and joint space. The methods for solving this problem are very limited at present. The optimal feedrate associated with a programmed tool path is crucial for high speed and high accuracy machining. This paper presents a novel feedrate optimisation method for feedrate planning on five-axis parametric tool paths with preset multi-constraints including chord error constraint, tangential kinematic constraints and axis kinematic constraints. The proposed method first derives a linear objective function for feedrate optimisation by using a discrete format of primitive continuous objective function. Then, the preset multi-constraints are converted to nonlinear constraint conditions on the decision variables in the linear objective function and are then linearised with an approximation strategy. A linear model for feedrate optimisation with preset multiple constraints is then constructed, which can be solved by well-developed linear programming algorithms. Finally, the optimal feedrate can be obtained from the optimal solution and fitted to the smooth spline curve as the ultimate feedrate profile. Experiments are conducted on two parametric tool paths to verify the feasibility and applicability of the proposed method that show both the planning results and computing efficiency are satisfactory when the number of sampling positions is appropriately determined.

Suggested Citation

  • Huan Liu & Qiang Liu & Pengpeng Sun & Qitong Liu & Songmei Yuan, 2017. "The optimal feedrate planning on five-axis parametric tool path with geometric and kinematic constraints for CNC machine tools," International Journal of Production Research, Taylor & Francis Journals, vol. 55(13), pages 3715-3731, July.
  • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:13:p:3715-3731
    DOI: 10.1080/00207543.2016.1254357
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