**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