Energy-efficient and quality-aware part placement in robotic additive manufacturing

Suyog Ghungrad, Abdullah Mohammed, Azadeh Haghighi

Research output: Contribution to journalArticlepeer-review


The advancements in autonomous robots for additive manufacturing (AM) are opening new horizons in the manufacturing industry, especially in aerospace and construction applications. The use of multiple robots and collaborative work in AM has rapidly gained attention in the industry and research community. Addressing the process planning challenges for single-robotic AM is foundational in addressing more advanced challenges at the collaborative multi-robotic level for AM. Among these challenges include the part placement problem which explores the optimal positioning of the part within the robot’s reach volume. The majority of the existing part placement algorithms take into account the part accuracy and manufacturing time for decision-making, while neglecting the implications of such decisions on energy efficiency and environmental sustainability. To address this gap, this paper presents a methodology for energy-efficient, high-quality part placement (EEHQPP) in robotic additive manufacturing. An energy-quality map is formulated and established to characterize the energy and quality variations across the robot’s workspace to inform the decision-making process. Two case studies (a container and a spur gear) are considered, and the performance of the proposed approach compared to the benchmark (i.e., default part placement by the 3D printing software) are evaluated. The proposed algorithm reduces both the energy consumption and maximum deviation error of the container (6.5% and 19.4%, respectively) and spur gear (1.4% and 32.7%, respectively) geometries manufactured by the robotic additive manufacturing system.
Original languageEnglish
Pages (from-to)644-650
Number of pages7
JournalJournal of Manufacturing Systems
Early online date31 May 2023
Publication statusPublished - Jun 2023

Bibliographical note

Copyright © 2023 Society of Manufacturing Engineers (SME). Published by Elsevier Ltd. All rights reserved. This is an open access article under the CC BY-NC-ND license (


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