Toolpath planning for multi-gantry additive manufacturing

Abstract

Additive Manufacturing (AM), specifically Fused Filament Fabrication (FFF) is revolutionizing the production of many products. FFF is one of the most popular AM processes because it is inexpensive, requires little maintenance, and has high material utilization. Unfortunately, long cycle times are a significant drawback that prevents FFF from being more widely implemented, especially for large-scale components. In response to this, printers that employ multiple independent FFF printheads simultaneously working on the same part have been developed, and multi-gantry configurations are now commercially available; however, there is a dearth of formal research on multi-gantry path planning, and current practices do not maximize printhead utilization or as-built mechanical properties. This article proposes a novel methodology for generating collision-free toolpaths for multi-gantry printers that yields shorter print times and superior mechanical properties compared with the state of the art. In this, a metaheuristic approach is used to seek near-optimal segmentation and scheduling of each layer while a collision checking and resolution algorithm enforces kinematic constraints to ensure collision-free solutions. Simulation is used to show the resulting makespan reduction for various layers, and the proposed methodology is physically implemented and verified. Tensile testing on samples printed via the current and proposed methods confirm that the proposed methodology results in superior mechanical properties.

Keywords:

• Fused filament fabrication
• toolpath planning
• parallel processing
• multi-gantry 3D printer

Additional information

Notes on contributors

Hieu Bui

Hieu Bui earned his BS and MS degrees in industrial engineering from the University of Arkansas – Fayetteville in 2015 and 2019, respectively. He is currently pursuing a PhD degree at the same university. His research interests include (i) additive manufacturing, (ii) robotics, and (iii) transportation logistics.

Harry A. Pierson

Dr. Harry Pierson is an assistant professor in the Department of Industrial Engineering at the University of Arkansas and directs the AT&T Manufacturing Automation Laboratory. He received his PhD from The Ohio State University in 2012 and has over 20 years of combined academic and industrial experience. He also holds an MS in engineering management and a BS in mechanical engineering from Missouri S&T. He studies collaborative robotics, additive manufacturing, and advanced manufacturing, and his research has been supported by the Army Research Office, Air Force Research Laboratory, and the Army Material Command.

Sarah Nurre Pinkley

Dr. Sarah Nurre Pinkley is an assistant professor in the Department of Industrial Engineering at the University of Arkansas. She received her PD, MS, and BS from Rensselaer Polytechnic Institute. Her research interests include using network optimization, scheduling, and optimization algorithms for restoring interdependent infrastructure systems, operating electric vehicle and drone battery swap stations, understanding last-mile delivery, and optimizing complex systems.

Kelly M. Sullivan

Dr. Kelly M. Sullivan is an associate professor of industrial engineering at the University of Arkansas, Fayetteville, AR. His research focuses on advancing computational methodology for designing, maintaining, and securing complex systems in order to limit the potential for failure. He holds a PhD in industrial and systems engineering from the University of Florida and a MS in industrial engineering from the University of Arkansas. Dr. Sullivan received a National Science Foundation CAREER Award in 2018 and was awarded the 2014 Glover-Klingman Prize for the best paper published in Networks. He is currently a member of IISE and INFORMS and serves as an associate editor for Operations Research Letters and INFORMS Journal on Computing.