Optimal tool path planning for 3D printing with spatio-temporal and thermal constraints
This addresses path planning for 3D printing, but it appears incremental as it applies an existing MILP method to encode constraints without major breakthroughs.
The paper tackled the problem of synthesizing optimal path plans for 3D printing under spatio-temporal and thermal constraints by reducing it to a Mixed Integer Linear Programming (MILP) problem, and experimental analysis demonstrated feasibility in generating optimal plans.
In this paper, we address the problem of synthesizing optimal path plans in a 2D subject to spatio-temporal and thermal constraints. Our solution consists of reducing the path planning problem to a Mixed Integer Linear Programming (MILP) problem. The challenge is in encoding the implication constraints in the path planning problem using only conjunctions that are permitted by the MILP formulation. Our experimental analysis using an implementation of the encoding in a Python toolbox demonstrates the feasibility of our approach in generating the optimal plans.