Effective Integration Of Mathematical And Cae Tools In Engineering
Author(s) -
Raghu Echempati,
Enayat Mahajerin,
Anca Sala
Publication year - 2020
Publication title -
papers on engineering education repository (american society for engineering education)
Language(s) - English
Resource type - Conference proceedings
DOI - 10.18260/1-2--3449
Subject(s) - matlab , curriculum , computer science , software engineering , software , course (navigation) , engineering drawing , mathematics education , engineering management , engineering , programming language , mathematics , psychology , pedagogy , aerospace engineering
Today, more than ever, engineers are challenged to use efficient computational tools in the simulation and design processes. Math software tools such as MATLAB ® , MathCAD ® and Excel ® in recent years have achieved wide spread acceptance throughout the educational and industrial communities. Moreover, CAE tools such as Solid Edge, Unigraphics ® , I-DEAS ® , ANSYS ® , etc., are used to perform parametric design and finite element analysis of individual components and simple mechanical assemblies. Integration of such tools into the engineering curriculum enhances students understanding of, and appreciation for the iterative and openendedness nature of design problems. This paper describes the teaching and learning experiences of including such tools in few example courses in mechanical engineering. One of them is a Computational and Experimental course (“Course 1” taught at Saginaw Valley State University (SVSU) using MATLAB), the second one is a Computational course (“Course 2”, taught at Baker College (BC)), and the third course is Machine Design (“Course 3”, taught at Kettering University (KU) using Excel and other CAE/FEA tools). The first and the third courses are 4credit and junior level subjects that include workshop sessions and laboratory assignments, while the second one is a 4-credit, senior level theoretical course. These example courses have both individual and collaborative assignments, which include conduction of experiments in order to generate data. Experience from all these courses taught at these universities shows that when students generate data on their own using good engineering judgment, they can easily process the data, develop and interpret any mathematical and statistical models for the experiment.
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