2026-09-18T19:20:54Z https://api.figshare.com/v2/oai

oai:figshare.com:article/32995121 2026-05-01T00:00:00Z portal_693 item_type_8 month_year_05_2026

Analytics-Driven Cooperative Multi-Robot Additive Manufacturing: Advancing Efficiency and Quality Suyog Ghungrad (24400076) Engineering, Industrial Engineering, Mechanical Computer Science Robotic additive manufacturing (AM) leverages the unbounded work envelopes and high degrees of freedom of articulated manipulators to revolutionize large-scale fabrication; however, the transition from single robot to cooperative multi-robot and multi-head systems introduces complex, coupled decisions in decomposition, placement, and path planning under time, energy, quality, strength, and thermal constraints. This dissertation establishes a comprehensive analytics framework that links robot kinematics and dynamics, process parameters, and cooperation strategies to the resulting process–structure–property relationships in multi-robot AM. First, it characterizes the dimensional accuracy and energy consumption of single-robot AM through analytically grounded and experimentally validated model and use these models to construct three-dimensional energy–quality (EQ) maps that relate workspace location to manufacturing performance and guide part placement within a manipulator’s workspace. Second, to address multi-robot scenarios, these concepts of EQ maps are extended into inverse EQ maps for multi- robot scenarios, enabling the optimal positioning of multiple robot bases around decomposed sub-parts while satisfying reachability, collision, and tolerance constraints to enable cooperative printing. Third, to accelerate decision-making and address the computational limitations of traditional analytical models, the thesis introduces a Kinematics-Guided Multi-Task learning architecture that jointly predicts path feasibility and energy consumption. By embedding inverse kinematics into a shared backbone, KG-MT internalizes reachability and joint limits, achieves orders-of-magnitude speedups over simulation, and generalizes across robot platforms to support cross-platform, energy-aware planning. Finally, the thesis addresses the critical process planning challenges of strength- and thermal-aware decomposition and path planning respectively, : for multi-head extrusion-based systems, where decomposition creates weak interfaces, thesis proposes a Synchronous Multi-Layer Printing strategy that recovers the joint strength of single-head deposition while preserving the throughput of multi-head printing; for multi-laser systems, the thesis establishes a framework that links decomposed multi-laser trajectories to thermal histories and uses clustering-based selection of path-planning strategies to stabilize thermal distributions and promote uniform temperature fields, validated experimentally and via a physics-informed neural network. Collectively, these contributions unify robot-, process-, structure-, and property-level analytics into a coherent decision-making foundation for cooperative multi-robot AM, supporting energy-efficient, high- quality, and property-aware decision-making across placement, decomposition, and path planning. 2026-05-01T00:00:00Z Text Thesis 10.25417/uic.32995121.v1 https://figshare.com/articles/thesis/Analytics-Driven_Cooperative_Multi-Robot_Additive_Manufacturing_Advancing_Efficiency_and_Quality/32995121 In Copyright Open Access after 2028-05-01