Improved co-scheduling of multi-layer printing path scanning for collaborative additive manufacturing

Abstract

Additive manufacturing processes, especially those based on fused filament fabrication mechanism, have a low productivity. One solution to this problem is to adopt a collaborative additive manufacturing system that employs multiple printers/extruders working simultaneously to improve productivity by reducing the process makespan. However, very limited research is available to address the major challenges in the co-scheduling of printing path scanning for different extruders. Existing studies lack: (i) a consideration of the impact of sub-path partitions and simultaneous printing of multiple layers on the multi-extruder printing makespan; and (ii) efficient algorithms to deal with the multiple decision-making involved. This article develops an improved method by first breaking down printing paths on different printing layers into sub-paths and assigning these generated sub-paths to different extruders. A mathematical model is formulated for the co-scheduling problem, and a hybrid algorithm with sequential solution procedures integrating an evolutionary algorithm and a heuristic is customized to multiple decision-making in the co-scheduling for collaborative printing. The performance was compared with the most recent research, and the results demonstrated further makespan reduction when sub-path partition or the simultaneous printing of multiple layers is considered. This article discusses the impacts of process setups on makespan reduction, providing a quantitative tool for guiding process development.

Keywords:

• Collaborative additive manufacturing
• process planning systems
• co-scheduling
• hybrid heuristic algorithm
• manufacturing systems

Acknowledgments

The authors thank Dr. Tarik Dickens' group for motivating this research at the High-Performance Materials Institute at the Florida A&M University-Florida State University College of Engineering. His comments on the preliminary results in the early stage of this research, which were presented in an ASME conference MSEC MSEC2019-3005, V001T02A042 are also gratefully acknowledged.

Additional information

Funding

This research is partially supported by two NSF grants CMMI-1901109 and HRD-1646897.

Notes on contributors

Zhengqian Jiang

Zhengqian Jiang received his PhD in industrial engineering from Florida State University in 2020. He is a research scientist at Amazon, Bellevue, WA, USA starting from 2020. He received a BS in mechanical engineering manufacture and automatization from the China University of Petroleum in 2010, MS in aerospace engineering from Shanghai JiaoTong University in 2013, and MS in industrial engineering from Florida State University in 2015.

Hui Wang

Hui Wang is a faculty member in industrial engineering at the Florida A&M University-Florida State University College of Engineering. His research has been focused on data analytics and optimization to support quality control and manufacturing system design. He received his PhD in industrial engineering from the University of South Florida.

Yanshuo Sun

Yanshuo Sun is a faculty member in industrial engineering at the Florida A&M University-Florida State University College of Engineering. His research interests are in mathematical programming and big data analytics with applications primarily in transportation and logistics systems. He has a PhD in transportation engineering from the University of Maryland.