CNC Milling Toolpath Generation Using Genetic Algorithms

  • Wesley Essink

Student thesis: Doctoral ThesisPhD


The prevalence of digital manufacturing in creating increasingly complex products with small batch sizes, requires effective methods for production process planning. Toolpath generation is one of the challenges for manufacturing technologies that function based on the controlled movement of an end effector against a workpiece. The current approaches for determining suitable tool paths are highly dependent on machine structure, manufacturing technology and product geometry. This dependence can be very expensive in a volatile production environment where the products and the resources change quickly. In this research, a novel approach for the flexible generation of toolpaths using a mathematical formulation of the desired objective is proposed. The approach, based on optimisation techniques, is developed by discretising the product space into a number of grid points and determining the optimal sequence of the tool tip visiting these points. To demonstrate the effectiveness of the approach, the context of milling machining has been chosen and a genetic algorithm has been developed to solve the optimisation problem. The results show that with meta-heuristic methods, flexible tool paths can indeed be generated for industrially relevant parts using existing computational power. Future computing platforms, including quantum computers, could extend the applicability of the proposed approach to much more complex domains for instantaneous optimisation of the detailed manufacturing process plan.
Date of Award11 Jan 2017
Original languageEnglish
Awarding Institution
  • University of Bath
SupervisorAydin Nassehi (Supervisor) & Stephen Newman (Supervisor)


  • Toolpath Generation
  • Optimisation
  • Genetic Algorithms
  • Manufacturing
  • CNC Milling

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