Digital Design and Manufacturing Lab: Teaching

Teaching

DDML students are encouraged to take courses in Mechanical Design, CAD, FEA, Optimization, Numerical Methods and Failure Analysis. Students may also take courses in Programming, AI, Data Science, Machine Learning and Statistics.

Coursework and other requirements for MS and PhD can be found at:

https://mae.osu.edu/graduate/mechanical-engineering

https://mae.osu.edu/graduate/aerospace-engineering

Here is a list of courses typical DDML graduate students enroll in:

Mechanical Design Credits Syllabus

ME 5682 Product Design

ME 5683 (lab)

3

1

 Lecture covering the fundamentals of the product design process, from concept creation to final implementation, including product architecture and design for manufacture and assembly. An optional, corresponding project-based lab course (ME/ISE 5683) offers practical application of this material.
ME 5680 CAD/CAM 4

Design of machine components, surfaces, and assemblies using parametric and feature-based design principles and advanced design tools in SolidWorks. Labs involving CNC machining, injection molding, 3D printing.

Prereq: 3670 (561), or Grad standing in Engineering, or permission of instructor. Not open to students with credit for 621 or 683.
ME 5670 Advanced CAD 3 Advanced techniques for solid, surface and assembly modeling using CATIA workbenches. Covers not only construction methods, but also how geometric modelers work internally: constraint solving, geometric DoFs, history roll forward-rollback, BRep data structure, Boolean ops, math representations of curves and surfaces. Teaches effective strategies for modeling, parametrization and robust histories (Details)
ME 7751 Kinematics 3 Kinematic design and analysis of mechanisms. The focus is on kinematic representations of rigid transformations in space, derivation and solution of the kinematic constraint equations. Computer projects involve Solidworks and Matlab/Mathematica.
ME 5194 Smart Product Design 3 Introduction to the concepts and process of embedded product design through an application based, structured design process and practice their application in the design of smart and interconnected products
Analysis and Simulation Credits Syllabus
ME 5139 Applied FEM 3

Introduction to finite element modeling; hands-on lab oriented class that uses Ansys finite element software. (Details)
ME 7760 Form Synthesis 3 Applied stress analysis for mechanical design
ME 6661 Advanced FEM/CAE (Ansys & Dyna) 4

Advanced FE modeling of nonlinear structural problems; min. weight design and topology optimization. Includes boundary, geometric and material nonlinearaties, both rate independent and dependent. The emphasis is on modeling decision making and validation. This is not a traditional FE theory class but a hands-on, software-intensive modeling and simulation class with a significant lab component. Prereq: 5139; or Grad standing in AeroEng, CivilEn, MatScEn, or MechEn. (Details)
ME 7761 Optimization in Mechanical Design 3 Application of optimization techniques to mechanical systems and structures. The structures considered will typically be high performance structures such as in aircraft and spacecraft
ME 5144 Fracture Mechanics 3 Fracture and fatigue of solids; stress intensity factors; stability of cracks; compliance and energy methods; plane stress, plane strain effects; crack propagation and arrest criteria.

Mathematics/Statistics

Credits

Syllabus

MATH 4568 Linear Algebra

3

https://math.osu.edu/courses/math-4568

STAT 6301 Probability & Statistical Inference

3

https://stat.osu.edu/courses/stat-6301

MATH 6601 Numerical Methods

4

https://people.math.osu.edu/overman.2/math6601/intro.pdf

MATH 4551 Vector Analysis

3

https://math.osu.edu/courses/math-4551

Data Science

Credits

Syllabus
CSE 5052 Survey of AI for non-majors 3 Survey of the basic concepts and techniques in artificial intelligence, including problem solving, knowledge representation, and machine learning.

ME 5194 Machine Learning for Engineers

3

The course covers the foundations of various machine learning methods briefly and provide an applied hands-on introduction to machine learning as relevant to MAE and robotics. Applications in MAE and robotics will be explored in homework and project assignments, with subject-specific datasets and problems.

CSE 5523 Machine Learning

3

https://www.asc.ohio-state.edu/schuler.77/courses/5523/

 

CSE 5526 Introduction to Neural Networks

3

https://web.cse.ohio-state.edu/~wang.77/teaching/cse5526/Syllabus.pdf