AI-POWERED ROBOTIC FABRICATION OF SCULPTURES USING MULTI-MATERIAL 3D PRINTING
DOI:
https://doi.org/10.29121/shodhkosh.v6.i2s.2025.6686Keywords:
AI-Driven Fabrication, Multi-Material 3D Printing, Robotic Sculpture Design, Generative Art, Digital Fabrication Systems, Adaptive ManufacturingAbstract [English]
The current paper includes an original strategy of developing an AI-assisted robotic fabrication of sculptures using multi-material 3D printing, by combining creativity, computation, and automation in the process of modern artwork creation. The paper discusses how we can combine artificial intelligence (AI) with robotic production processes to allow autonomous design production and manipulation of materials. The system is able to increase the artistic freedom by using machine learning algorithms to design and optimize via generative design and optimization and make sure the structures and aesthetics are accurate. An extensive approach was created to combine an AI-based conceptual system with a robotic arm and multi-material print head that was adaptive. The AI model learns on data population of massive information on artistic forms and material behavior, which allows dynamic decision making when fabricating. The suggested pipeline, which includes a digital idea up to a solid sculpture, will comprise real-time sensor input and reinforcement learning to regulate adaptively the print parameters, including layer thickness, deposition speed, and combining materials. Practical work has shown that the system could create complex sculptures of both rigid and flexible materials with new textual and structural variations that could not be produced by traditional sculpture. The study emphasizes how AI-based design intelligence is used to create new forms of human-machine collaboration in the creation of art using robot precision. The findings indicate the possible great impact on the future of the computational aesthetics, digital craftsmanship, and self-fabrication systems, between the creative will and material manifestation.
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Copyright (c) 2025 Deepali M Ujalambkar, Ram B Ghogare, Manjushree V. Gaikwad, Jyoti Yogesh Deshmukh, Vinod Chandrakant Todkari, Nilesh P. Sable
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