**UNDERGRADUATE COURSES**

**AUTOMATED MANUFACTURING**

**Course Objectives:**

1. Introduce principles, methods, and hardware/software tools used in modern
computerized design and manufacturing of discrete parts.

2. Acquire practical experience in computer-aided design and manufacturing
through a series of laboratory exercises (see ADMS Laboratory)

3. Understand the main components involved in automated manufacturing, so as to
be able to select an area of academic and professional specialization.

**Course Outline:**

· Introduction to geometric modeling and related techniques for
computer-aided design, process planning, and manufacturing: approximating
curves and surfaces, motion planning.

· Introduction to basic concepts of probability theory for Statistical Process
Control (SPC): quality control, control charts, Taguchi methods

· Discrete-event control of manufacturing systems: Programmable Logic Control
(PLC)

· Introduction to systems and control theory: modeling and control of dynamic
processes, applications to manufacturing process control and robotics.

· Supervisory control of manufacturing systems: discrete-event models,
inventory control, scheduling.

**LABORATORY SESSIONS:
**1. Designing a part to be manufactured.

2. Computer-Aided Design (CAD): designing a part using the SmartCAM software

3. CNC machining: introduction and basic operation of a CNC milling machine

4. Computer-Aided Process Planning (CAPP): creating a process model for turning using the SmartCAM software

5. Process planning and CNC machining of a rotational part: manufacturing a part on a lathe

6. Programmable Logic Controllers (PLC): introduction, basic operation, and programming of a PLC

7. Robotics: introduction and basic operation of a robotic manipulator

8. Robot control: controlling a robotic manipulator through the ACL language

9. Vision systems for inspection of parts

10. Statistical Process Control (SPC): introduction to basic techniques for inspection and quality control

11. Computer Integrated Manufacturing (CIM): introduction to communication networking in a manufacturing environment

12. Computer Simulation and supervisory control of manufacturing systems: task sequencing and scheduling

**STATISTICS AND QUALITY
ENGINEERING**

**Course Objectives:**

1. Introduce principles of probability and statistics including events, Bayes
theorem, random variables, functions of random variables, sampling
distributions, and parameter estimation.

2. Learn about the main concepts of quality engineering: Acceptance Sampling, Real Time Quality Control, and the Taguchi method for product quality improvement.

**Course Outline:**

· Probabilistic modeling; statistical/probabilistic thinking, reasoning
and decision making; data analysis, communicating statistical information.

· Treatment of Data: Frequency distributions, Descriptive statistics

· Probability Theory basics

· Probability Distribution and Density: Random variables and probability distributions, special distribution and density functions, mean and variance, functions of random variables, vectors of random variables and joint density and distribution

· Sampling Distributions

· Statistical Inference: Estimation and confidence intervals, hypothesis testing

· Statistical methods for Quality Control and Improvement: Acceptance sampling, Control charts, Design of Experiments

**PROBABILITY THEORY IN ELECTRICAL
AND COMPUTER ENGINEERING**

**Course Objectives:**

1. Develop a solid foundation in the
concepts of probability theory.

2. Learn fundamental probabilistic modeling and analysis techniques so
you can use them in Electrical and Computer Engineering applications.

3. Learn basic techniques for managing and processing data and apply
them to basic estimation and hypothesis testing problems.

**Course Outline:**

- Foundations
of Probability Theory

- Discrete and continuous random variables

- Pairs of random variables: joint cumulative
distribution and density functions, marginal probability density functions, functions
of random variables, expectation, covariance, correlation, conditioning

- Sums of random variables: sample averages,
moment generating functions, inequalities, Central Limit Theorem

- Parameter estimations: point estimation,
interval estimation

- Hypothesis testing