About this Abstract |
| Meeting |
2025 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2025)
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| Symposium
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2025 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2025)
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| Presentation Title |
AI Agent Framework-Driven Process Planning for Multi-Material and Multi-DOF Manipulator-Based Support-Free Additive Manufacturing |
| Author(s) |
Bharat Dwivedi, Arun Skanda Rebbapragada, Arka Rebbapragada, Rajeev Dwivedi |
| On-Site Speaker (Planned) |
Bharat Dwivedi |
| Abstract Scope |
Multi-degree-of-freedom (DOF) robotic manipulators offer a transformative approach by allowing substrate reorientation in conjunction with material deposition, thereby eliminating the need for support structures. This paper presents an AI-driven framework for process planning that effectively replicates material addition intent using a spatial manipulator with multiple DOFs.
Proposed framework overcomes limitations such as finite size of the deposition end-effector and leverages AI-based optimization to dynamically adapt toolpath planning and deposition strategies, ensuring high-fidelity replication of desired geometries. We use 'maxel' framework for multi-material additive manufacturing, enabling the fabrication of functionally graded materials (FGMs) and heterogeneous structures. As a proof of concept, we demonstrate the effectiveness of our AI-driven process planning through pilot test cases involving functionally graded materials with varying compositions.
By reducing material waste, optimizing deposition strategies, and enabling complex multi-material structures, our AI-based framework paves the way for next-generation AM applications in aerospace, biomedical engineering, and custom manufacturing |
| Proceedings Inclusion? |
Planned: Post-meeting proceedings |