2023 ASEE Annual Conference & Exposition

Machine-Learning Driven Robot-Motion Design: Introducing a Web-Based Mechanism Design Software

Presented at Mechanical Engineering Division (MECH) Technical Session 15: Automation and Machine Learning

This paper presents a novel machine-learning-driven web-based software, which enables the design and simulation of planar N-bar single and multi-degree-of-freedom linkage mechanisms for robotics and mechatronics applications.
The software is developed using research methodologies to create a new computational framework for simultaneous type and dimensional synthesis of mechanisms for motion generation problems. The existing paradigm of selecting the type of a mechanism and then computing the dimension is shown to be inadequate in meeting the requirements of designers. Therefore, a new data-driven approach is proposed in which both the type and dimensions of a mechanism are computed directly from the user input, i.e., motion or path. While a formal assessment of the software in a classroom setting is pending, this paper outlines its broad applicability to support the learning outcomes of several mechanical engineering classes, including freshman engineering design, engineering dynamics, kinematics of machinery, computer-aided mechanism design, robotics, and mechatronics. The software is suitable for a wide range of engineering levels, from freshman engineering to advanced kinematics and robotics classes, and has been adopted by numerous universities and organizations for their mechanical engineering programs.

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