Trevas, D. A., & Tester, J. T. (2019, April), Improving Machine Design Instruction by Developing Computational Design Tools  Paper presented at 2019 Pacific Southwest Section Meeting, California State University, Los Angeles , California. 10.18260/1-2--31831

\bibitem{asee_peer_31831}
  Trevas, D. A., \& Tester, J. T. (2019, April), \emph{Improving Machine Design Instruction by Developing Computational Design Tools}
  Paper presented at 2019 Pacific Southwest Section Meeting, California State University, Los Angeles , California.
  10.18260/1-2--31831

David A Trevas and John T. Tester.  "Improving Machine Design Instruction by Developing Computational Design Tools".  2019 Pacific Southwest Section Meeting, California State University, Los Angeles , California, 2019, April.  ASEE Conferences, 2019.  https://peer.asee.org/31831 Internet.  22 Apr, 2024

\bibitem{asee_peer_31831}
  David A Trevas and John T. Tester.
  "Improving Machine Design Instruction by Developing Computational Design Tools".
  \emph{2019 Pacific Southwest Section Meeting, California State University, Los Angeles , California, 2019, April}.
  ASEE Conferences, 2019.
  https://peer.asee.org/31831 Internet.  22 Apr, 2024

@INPROCEEDINGS{asee_peer_31831
author = "David A Trevas and John T. Tester"
title = "Improving Machine Design Instruction by Developing Computational Design Tools"
booktitle = "2019 Pacific Southwest Section Meeting"
year = "2019"
month = "April"
address = "California State University, Los Angeles , California"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/31831}
number = {10.18260/1-2--31831}
}

TY  - CPAPER
AB  - In a traditional Machine Design course, the analysis of machine components and systems involve lengthy, multi-step processes that often include retrieving data from graphs or tables. Automating these processes is quite feasible with computational tools that are ubiquitous in the marketplace, and is routinely part of engineering work in industry.  The ability to synthesize a solution algorithm from the reference equations and data obtained from tables and graphs shows that students understand the processes used in machine design. Once student master the fundamental machine design theory, students can create the computational tools and then proceed to engage the topics in machine design at a much higher level than with only use of pencil, paper and calculator. They can quickly vary input parameters into their computational tools and immediately see the influence on the output values of interest. In other words, they can actually design machine components and systems, instead of merely analyzing them given the input quantities for equations.

This work extends these previous findings of those using Microsoft Excel in Engineering classrooms by teaching students how to build successful Machine Design-dedicated tools.   Teaching students how to build these tools involves 4 basic steps:  1) introducing the machine design topic traditionally, 2) developing techniques for data input, 3) Algorithm development of the theory-based equations, and 4) validation processes required to insure future output accuracy.  

The primary result of this approach is that students successfully answer questions on tests which are at a much higher level than those that were presented on previous tests. Students who have taken the course when they learned the techniques of developing computational tools have reported that they were able to put these skills to use at work or in their subsequent courses. This approach has the benefit of teaching marketable skills that also enhance the students' understanding of the concepts of machine design.

AU  - David A Trevas
AU  - John T. Tester
CY  - California State University, Los Angeles , California
DA  - 2019/04/04
PB  - ASEE Conferences
TI  - Improving Machine Design Instruction by Developing Computational Design Tools
UR  - https://peer.asee.org/31831
DO  - 10.18260/1-2--31831
ER  -