IE495 OFF-LINE QUALITY CONTROL
| Course Code: | 5680495 |
| METU Credit (Theoretical-Laboratory hours/week): | 3 (3.00 - 0.00) |
| ECTS Credit: | 5.0 |
| Department: | Industrial Engineering |
| Language of Instruction: | English |
| Level of Study: | Undergraduate |
| Course Coordinator: | Prof.Dr. GÜLSER KÖKSAL |
| Offered Semester: | Once in several years. |
Course Objectives
1. To develop a basic understanding of quality control at the product and process design stages of a product’s life cycle.
2. To develop an ability to formulate and solve typical design optimization problems.
3. To develop an understanding of contemporary issues in quality engineering and solution apporoaches
Course Content
The total quality concept. Continuous quality improvement. Quality control activities (off-line, on-line). Quality loss. System design. Parameter design. Taguchi method. Tolerance design. Quality function deployment. Quality standards. Quality awards.
Course Learning Outcomes
- Demonstrates a basic understanding of the design process from concept to prototyping.
- Demonstrates a basic understanding of quality control activities at the design stage.
- Understands the basic principle of robust design, and impact of robust design on the producers, customers and the society.
- Identifies control and noise factors, and objectives of a robust design problem correctly, chooses appropriate performance measures.
- Understands different robust design methods (Taguchi, response surface).
- Understands different variance approximation techniques (numeric integration, Taylor series, Monte Carlo simulation, statistical experimentation), and uses the statistical experimentation method effectively.
- Designs appropriate robust design experiments (standard and modified orthogonal arrays, full and fractional factorial designs), and analyzes and interprets data (ANOVA, marginal average plots, confidence intervals) to solve robust design problems.
- Uses appropriate statistical package programs effectively to solve robust design problems.
- Demonstrates the ability to apply robust design principles and methodology already learned to new problems, and practical problems.
- Understands the importance, timing, and methodology of tolerance design in product development.
- Understands the meaning of total quality.
- Understands the meaning of total quality management, its basic principles and approaches.
Program Outcomes Matrix
| Contribution | |||||
| # | Program Outcomes | No | Yes | ||
| 1 | An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics | ✔ | |||
| 2 | An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors | ✔ | |||
| 3 | An ability to communicate effectively with a range of audiences | ✔ | |||
| 4 | An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts | ✔ | |||
| 5 | An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives | ✔ | |||
| 6 | An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions | ✔ | |||
| 7 | An ability to acquire and apply new knowledge as needed, using appropriate learning strategies | ✔ | |||
| 8 | An ability to design, analyze, operate, and improve integrated systems that produce and/or supply products and/or services in an effective, efficient, sustainable, and socially responsible manner | ✔ | |||
| 9 | An ability to apply critical reason and systems thinking in problem solving and systems design | ✔ | |||
| 10 | An ability to use scientific methods and tools (such as mathematical models, statistical methods and techniques) necessary for industrial engineering practice | ✔ | |||