Optimal tool path planning for 3D printing with spatio-temporal and thermal constraints

TL;DR

This paper presents a method to generate optimal 2D tool paths for 3D printing considering spatio-temporal and thermal constraints by formulating the problem as a MILP, enabling efficient and feasible planning.

Contribution

It introduces a novel MILP-based approach to encode complex constraints for optimal 3D printing tool path planning.

Findings

The MILP formulation successfully encodes spatio-temporal and thermal constraints.

Experimental results demonstrate the approach's feasibility and optimality.

The Python toolbox implementation validates the method's practicality.

Abstract

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.