Study of Optimal Cutting Paths for Steel Plates Based on Enumeration and Nonlinear Programming

Authors

  • Yongheng Shen

DOI:

https://doi.org/10.54097/b18yqz13

Keywords:

Plate cutting path optimisation; Shortest path for air travel; Enumeration; Non-linear Planning Models; Mould Processing Efficiency.

Abstract

With the development of society and economy, mould processing enterprises have an increasingly strong need to reduce costs and increase economic efficiency. Improving the efficiency of the cutting process is one of the key links to achieve this goal, and the planning of the cutting path occupies a major position in it. Under the condition that the actual cutting distance is fixed, the shortest air distance becomes the goal pursued by enterprises. In this paper, the optimal cutting path problem for steel plates is studied, and the optimal paths under different cutting tasks are found by enumeration method and nonlinear planning model.

Downloads

Download data is not yet available.

References

[1] Chentsov A G, Chentsov P A, Petunin A A, et al. Model of megalopolises in the tool path optimisation for CNC plate cutting machines[J]. International Journal of Production Research, 2018, 56(14): 4819-4830.

[2] Abbas A T, Aly M F, Hamza K. Optimum drilling path planning for a rectangular matrix of holes using ant colony optimisation[J]. International Journal of Production Research, 2011, 49(19): 5877-5891.

[3] Fountas N A. A virus-evolutionary, multi-objective intelligent tool path optimisation methodology for sculptured surface CNC machining[D]. Kingston University, 2019.

[4] Lu F, Zhou G, Zhang C, et al. Integrated optimisation of multi-pass cutting parameters and tool path with hierarchical reinforcement learning towards green manufacturing[J]. Robotics and Computer-Integrated Manufacturing, 2025, 91: 102824.

[5] Hong S, Lee S U, Huang X, et al. An anytime algorithm for chance constrained stochastic shortest path problems and its application to aircraft routing[C]//2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021: 475-481.

Downloads

Published

10-10-2024

Issue

Section

Articles