Research Projects

Advancements in Additive Manufacturing (AM) are essential to the success of India's "Make in India" and "Atmanirbhar Bharat" initiatives. To foster growth in the Indian AM sector, the Government of India has introduced a "National Strategy for Additive Manufacturing," in 2022 which highlights the need for faster and more precise large-format AM machines. Achieving this objective requires the development of high-throughput positioning systems for AM machines. Cable-driven parallel robots (CDPRs) present a promising solution for high-throughput positioning systems, due to their low inertia of moving parts. However, challenges such as motion command-induced (MCI) vibrations and cable slack can interfere with control. Current CDPR controllers are typically limited to simple trajectories, and real-time control of more complex paths remains difficult. Even if better controllers are developed, CDPR design constraints can limit throughput, leading to conservative path planning. The novelty of this research lies in developing a fundamental understanding of how CDPR design parameters influence the maximum achievable acceleration. This insight will be used to optimise path planning, trajectory generation, and control for higher throughput. Another innovative aspect of the research is the development of a control strategy based on learning control and B-splines, enabling real-time control of CDPRs while mitigating MCI vibrations and preventing cable loss of control. The intellectual merit of this work is the creation of a framework that, given a specific CDPR design and a set of part designs, can generate optimal paths, trajectories, and control signals to maximize throughput. The framework will be validated through an experimental setup developed as part of the project. The technological impact will be the creation of high-throughput positioning systems for AM machines. In the future, the CDPR positioning system will be integrated with pellet-based extrusion. Additionally, in future, the proposed framework could be adapted to support reconfigurable CDPR designs suited to different families of parts, enhancing throughput across a wide range of part designs.