Session: 03-01-07: Annual Conference-Wide Symposium on Additive Manufacturing

Paper Number: 145736

145736 - Productivity Analysis of Automated 3d Printing System 

Automated in-house systems are essential in today's manufacturing and prototyping. They provide benefits such as minimizing the need for manual work and decreasing human error while enhancing productivity, efficiency, and uniformity. Automated technologies in additive manufacturing allow for quick printing iteration and testing, which speeds up the product development cycle and reduces time to market. 

The existing bottleneck in the productivity of 3D printing operations is due to the sequential process; there is a delay in the next printing job due to prolonged waiting time for the completion of the ongoing print operation and subsequent manual removal of completed 3D printed parts. The printing process needs to be continuously monitored, and a worker must be assigned to remove the printed part and start printing the next job. 

In this research, we first built an automated 3D printing system by integrating a commercial robotic arm and commercial 3D printers with our developed triggering circuit and scripts. The system aims to automate the 3D printing process. Second, we utilized the system to investigate the productivity comparison of automatic vs. manual scenarios. 

The following is a detailed methodology for integrating the automated 3D printing setup. The setup described entails the integration of two Prusa printers with two Raspberry Pi devices via serial communication facilitated by specialized Raspbian software (OctoPi containing Octoprint). Each Raspberry Pi communicates with a single printer, interconnected wirelessly within a subnet. Furthermore, a workstation connected to the network orchestrates system start-up and monitoring tasks. Two triggering circuits, each comprising opto-couplers, bridge the UR5 robotic arm controller's digital input/output pins with the Raspberry Pi GPIOs. The robot operates in two states: IDLE, where digital output pins are off, and BUSY, where all digital output pins are activated, signaling motion in progress. Upon print completion, the scripts await the robot's IDLE state before initiating the next print job, ensuring synchronization between printing and post-print handling. The robotic arm grips the bed plate containing the printed parts and places it inside the collecting container; then, it picks the new bed plate for the next print job.

The following is the methodology for the survey study. We compare the performance metrics of two scenarios: “Automated 3D Printing System” and “Manual Worker Operation.” The roles are End-user, Manual worker, and Automated 3D printer. The end-user sends the print jobs containing multiple parts (e.g., numerous pieces per job) to be 3D printed in both automated and manual scenarios. The print jobs, slicer setting, and number of parts in the bed plate are constant in both scenarios. We then log the activities of both scenarios with the actual time needed:
t1= duration between “print job submitted” and “sending gcode to 3D printer”, 
t2= duration between “sending gcode to 3D printer” and “3D printer’s bed plate heating completed & extrusion started”, 
t3= duration between “3D printer’s extrusion started” and “3D printing extrusion completed”,
t4= duration between “3D printing extrusion completed” and “3D printer’s bed plate cooling completed”,
t5= duration between “3D printer’s bed plate cooling completed” and “existing bed plate removed,”
t6= duration between “existing bed plate removed” and "new bed plate placed,”
t7= duration between "new bed plate placed” and “next print job submitted.”

We hypothesize that the manual scenario has variability in productivity due to workers' different skills and experiences. We also hypothesize that the automated system scenario has increased productivity due to alleviating the existing bottleneck of removing the finished 3D-printed parts, preparing the next build plate for the next printing, and automatically starting the following 3D printing job. 

In conclusion, incorporating advanced technologies like robotic, automated 3D Printing systems maximizes productivity. This paper provides the system architecture and the systematic survey study with quantitative results, which are helpful as a reference for users interested in increasing the productivity of additive manufacturing operations.

Presenting Author: Nawal Aljasmi Dubai Electricity & Water Authority (DEWA)

Presenting Author Biography: Bachelor of Science in Mechanical Engineering - University of Sharjah, UAE.
Joined Dubai Electricity & Water Authority (DEWA) since April 2018.
I participated as a presenter at the INTERNATIONAL MECHANICAL ENGINEERING CONGRESS & EXPOSITION (IMECE) - 2020-2023
Poster Presentation At IEEE-IROS International Conference On Intelligent Robotics.
Macau, CHINA, 2019
International Mechanical Engineering Congress & Exposition (Imece)
Utah, USA, 2019
Winning an internal award in DEWA (Distinguished Employee Award) and an International award Ideas America Award 2020, The overall idea of the year & gold in breakthrough innovation

Authors:

Nawal Aljasmi Dubai Electricity & Water Authority (DEWA)
Khuloud Almaeeni Dubai Electricity & Water Authority (DEWA)
Mozah Alyammahi Dubai Electricity & Water Authority (DEWA)
Amrut Sekhar Panda Dubai Electricity & Water Authority (DEWA)
Ahmad Al Mheiri Dubai Electricity & Water Authority (DEWA)
Arun Joy Dubai Electricity & Water Authority (DEWA)
Rahmat Agung Susantyoko Dubai Electricity & Water Authority (DEWA)
Vinod Subramanian Dubai Electricity & Water Authority (DEWA)