Infill toolpath optimization for fused deposition modeling

Level of Education of Students Involved

Undergraduate

Faculty Sponsor

Jesse Sestito

College

Engineering

Discipline(s)

Mechanical Engineering

ORCID Identifier(s)

0000-0002-4489-7054

Presentation Type

Poster Presentation

Symposium Date

Spring 4-25-2024

Abstract

Additive manufacturing (AM) can be a highly effective process in various industries. It is a process for creating highly specialized, light-weight parts. AM parts created using fused deposition modeling (FDM) are generally not created as solid parts. Instead, the insides of the parts are filled using a sparse printing pattern to generate what is called infill. There are various standard infills that are available to FDM, with each one yielding varying mechanical properties with a wide variety of non-extrusion jumps. Each of these non-extrusion jumps adds in additional printing time, slowing down the entire process. We are looking to develop a differential growth algorithm to create infills for any type of part that optimizes material properties with zero non-extrusion jumps. The algorithm starts with a specified number of nodes on a line that then enact forces on each other, causing that line to grow and fill a non-uniform space. The forces are modeled after the Lennard-Jones energy equation for atoms, and the points slowly fill the space using Newton’s second law. Using this, the toolpath is of one, continuous line for the extruder to follow. With this algorithm, the variation of just the forces can adjust the percentage infill. It also means that different nodes could experience different amounts of force, and we would have the ability to create an adaptive infill to prioritize critical areas of our part. The proposed differential growth toolpath could decrease print time and allow for greater control of mechanical properties in AM.

Biographical Information about Author(s)

Sarah Iselin is a third year Mechanical Engineering student, and this is her third semester doing undergraduate research.

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