Purwar, A. (2023, June), Machine-Learning Driven Robot-Motion Design: Introducing a Web-Based Mechanism Design Software  Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--43510

\bibitem{asee_peer_43510}
  Purwar, A. (2023, June), \emph{Machine-Learning Driven Robot-Motion Design: Introducing a Web-Based Mechanism Design Software}
  Paper presented at 2023 ASEE Annual Conference \& Exposition, Baltimore , Maryland.
  10.18260/1-2--43510

Anurag Purwar.  "Machine-Learning Driven Robot-Motion Design: Introducing a Web-Based Mechanism Design Software".  2023 ASEE Annual Conference & Exposition, Baltimore , Maryland, 2023, June.  ASEE Conferences, 2023.  https://strategy.asee.org/43510 Internet.  06 Jul, 2024

\bibitem{asee_peer_43510}
  Anurag Purwar.
  "Machine-Learning Driven Robot-Motion Design: Introducing a Web-Based Mechanism Design Software".
  \emph{2023 ASEE Annual Conference \& Exposition, Baltimore , Maryland, 2023, June}.
  ASEE Conferences, 2023.
  https://strategy.asee.org/43510 Internet.  06 Jul, 2024

@INPROCEEDINGS{asee_peer_43510
author = "Anurag Purwar"
title = "Machine-Learning Driven Robot-Motion Design: Introducing a Web-Based Mechanism Design Software"
booktitle = "2023 ASEE Annual Conference \& Exposition"
year = "2023"
month = "June"
address = "Baltimore , Maryland"
publisher = "ASEE Conferences"
note = {https://strategy.asee.org/43510}
number = {10.18260/1-2--43510}
}

TY  - CPAPER
AB  - 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. 
AU  - Anurag Purwar
CY  - Baltimore , Maryland
DA  - 2023/06/25
PB  - ASEE Conferences
TI  - Machine-Learning Driven Robot-Motion Design: Introducing a Web-Based Mechanism Design Software
UR  - https://strategy.asee.org/43510
DO  - 10.18260/1-2--43510
ER  -