### Automated Manufacturing

**Course Objectives:**

- Introduce principles, methods, and hardware/software tools used in modern computerized design and manufacturing of discrete parts.
- Acquire practical experience in computer-aided design and manufacturing through a series of laboratory exercises (see ADMS Laboratory)
- 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.

### ADMS Laboratory

- Designing a part to be manufactured.
- Computer-Aided Design (CAD): designing a part using the SmartCAM software
- CNC machining: introduction and basic operation of a CNC milling machine
- Computer-Aided Process Planning (CAPP): creating a process model for turning using the SmartCAM software
- Process planning and CNC machining of a rotational part: manufacturing a part on a lathe
- Programmable Logic Controllers (PLC): introduction, basic operation, and programming of a PLC
- Robotics: introduction and basic operation of a robotic manipulator
- Robot control: controlling a robotic manipulator through the ACL language
- Vision systems for inspection of parts
- Statistical Process Control (SPC): introduction to basic techniques for inspection and quality control
- Computer Integrated Manufacturing (CIM): introduction to communication networking in a manufacturing environment
- Computer Simulation and supervisory control of manufacturing systems: task sequencing and scheduling

### Statistics and Quality Engineering

**Course Objectives:**

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

- Develop a solid foundation in the concepts of probability theory.
- Learn fundamental probabilistic modeling and analysis techniques so you can use them in Electrical and Computer Engineering applications.
- 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