ME315 Mechanical Experimental Lab IIstanbul Okan UniversityDegree Programs Automotive Engineering (English)General Information For StudentsDiploma SupplementErasmus Policy StatementNational Qualifications
Automotive Engineering (English)
Bachelor TR-NQF-HE: Level 6 QF-EHEA: First Cycle EQF-LLL: Level 6

General course introduction information

Course Code: ME315
Course Name: Mechanical Experimental Lab I
Course Semester: Spring
Course Credits:
Theoretical Practical Credit ECTS
2 2 3 5
Language of instruction: EN
Course Requisites:
Does the Course Require Work Experience?: No
Type of course:
Course Level:
Bachelor TR-NQF-HE:6. Master`s Degree QF-EHEA:First Cycle EQF-LLL:6. Master`s Degree
Mode of Delivery: Face to face
Course Coordinator : Dr.Öğr.Üyesi ALPER TEZCAN
Course Lecturer(s): Dr.Öğr.Üyesi HAYRETTİN KARCI
Course Assistants:

Course Objective and Content

Course Objectives: This is the first of a two course sequence. The purpose of the Mechanical Experimental Lab I course is to give students the fundamental knowledge of measurement systems, statistical methods and the components of a measurement system that has a wide range of usage in mechanical/automotive engineering.
Course Content: General Characteristics of Measurement Systems.
Statistical Analysis of Experimental Data.
Measurement Systems with Electrical Signals.
Computerized Data Acquisition Systems.
Discrete Sampling and Analysis of Time Varying Signals.

Learning Outcomes

The students who have succeeded in this course;
Learning Outcomes
1 - Knowledge
Theoretical - Conceptual
1) Students will the general characteristics of a measurement system.
2) Students will learn how to do statistical analysis of experimental data.
3) Students will learn measurement systems with electrical signals
4) Students will learn computerized data acquisition systems.
5) Students will learn discrete sampling and analysis of time varying signals
2 - Skills
Cognitive - Practical
3 - Competences
Communication and Social Competence
Learning Competence
Field Specific Competence
Competence to Work Independently and Take Responsibility

Lesson Plan

Week Subject Related Preparation
1) • Describe the course. • Measurement in Engineering Experimentation • Measurement in Operational Systems • Dimensions and Units -
1)
2) • Define measurement systems. • Generalized measurement system • Validity of measurement • Measurement error and related definitions • Calibration of measurement systems • Dynamics measurement -
3) • Define electrical signals. • General characteristics of signal amplification • Signal attenuation • General aspects of signal filtering • Indicating and recording devices • Digital voltmeters and multimeters • Oscilloscopes • Data acquisition systems -
4) • Define electrical signals. • Electrical transmission of signals between components • Low and high level analog voltage signal transmission • Current loop analog signal transmission • Digital signal transmission -
5) • Define statistical analysis. • Statistical analysis of experimental data • General concepts and definitions • Measures of general tendency • Measures of dispersion • Probability • Probability distribution functions • Some probability distribution functions with engineering applications -
6) • Define statistical analysis. • Parameter estimation • Interval estimation of the population mean • Interval estimation of the population variance • Criterion for rejecting questionable data points -
7) • Define statistical analysis methods. • Correlation of experimental data • Correlation coefficient • Least-squares linear fit -
8) • Outliers in x-y data sets • Linear regression using data transformation • Multiple and polynomial regression • Linear functions of random -
9) Midterm -
10) • Define data acquisition systems. • Computer systems • Computer systems for data acquisition • Components of computer systems • Representing numbers in computer systems -
11) • Data acquisition components • Multiplexers • Basics of Analog to Digital converters • Basics of Digital to Analog converters • Simultaneous Sample-and-Hold Subsystems -
12) • Define data acquisition systems. • Configuration of Data-Acquisition systems • Internal single board plug-in systems • External systems • Digital connectivity • Virtual instruments • Digital storage oscilloscopes • Data loggers • Software for data-acquisition systems • Commercial software packages -
13) • Define sampling theorem. • Discrete sampling and analysis of time-varying signals • Discrete sampling and analysis of time varying signals. -
14) • Sampling-rate theorem • Spectral analysis of time-varying signals -
15) Final -

Sources

Course Notes / Textbooks: A.J. Wheeler and A.R. Ganji, Introduction to Engineering Experimentation, 3rd Edition, Pearson Education, 2010.
References: R.S. Figliola, and D.E. Beasley, Theory and Design for Mechanical Measurements, 5th Edition, Wiley, 2011.
E.O. Doebelin, Measurement Systems - Application and Design, 5th Edition, McGraw-Hill, 2004.
J.P. Holman, Experimental Methods for Engineers, 7th Edition, McGraw-Hill, 2010.
P.F. Dunn, Measurement and Data Analysis for Engineering and Science, 2nd Edition, Taylor&Francis/CRC Press, 2010.

Course-Program Learning Outcome Relationship

Learning Outcomes

1

2

3

4

5

Program Outcomes
1) Sufficient knowledge in mathematics, science and engineering related to their branches; and the ability to apply theoretical and practical knowledge in these areas to model and solve engineering problems.
2) The ability to identify, formulate, and solve complex engineering problems; selecting and applying appropriate analysis and modeling methods for this purpose.
3) The ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; the ability to apply modern design methods for this purpose. (Realistic constraints and conditions include such issues as economy, environmental issues, sustainability, manufacturability, ethics, health, safety, social and political issues, according to the nature of design.)
4) Ability to develop, select and use modern techniques and tools necessary for engineering applications; ability to use information technologies effectively.
5) Ability to design experiments, conduct experiments, collect data, analyze and interpret results to examine engineering problems or discipline-specific research topics.
6) The ability to work effectively in disciplinary and multidisciplinary teams; individual work skill.
7) Effective communication skills in Turkish oral and written communication; at least one foreign language knowledge; ability to write effective reports and understand written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8) Awareness of the need for lifelong learning; access to knowledge, ability to follow developments in science and technology, and constant self-renewal.
9) Conform to ethical principles, and standards of professional and ethical responsibility; be informed about the standards used in engineering applications.
10) Awareness of applications in business, such as project management, risk management and change management; awareness of entrepreneurship, and innovation; information about sustainable development.
11) Information about the universal and social health, environmental and safety effects of engineering applications and the ways in which contemporary problems are reflected in the engineering field; awareness of the legal consequences of engineering solutions.
12) Knowledge on advanced calculus, including differential equations applicable to automotive engineering; familiarity with statistics and linear algebra; knowledge on chemistry, calculus-based physics, dynamics, structural mechanics, structure and properties of materials, fluid dynamics, heat transfer, manufacturing processes, electronics and control, design of vehicle elements, vehicle dynamics, vehicle power train systems, automotive related regulations and vehicle validation/verification tests; ability to integrate and apply this knowledge to solve multidisciplinary automotive problems; ability to apply theoretical, experimental and simulation methods and, computer aided design techniques in the field of automotive engineering; ability to work in the field of vehicle design and manufacturing.

Course - Learning Outcome Relationship

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution
1) Sufficient knowledge in mathematics, science and engineering related to their branches; and the ability to apply theoretical and practical knowledge in these areas to model and solve engineering problems. 4
2) The ability to identify, formulate, and solve complex engineering problems; selecting and applying appropriate analysis and modeling methods for this purpose. 3
3) The ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; the ability to apply modern design methods for this purpose. (Realistic constraints and conditions include such issues as economy, environmental issues, sustainability, manufacturability, ethics, health, safety, social and political issues, according to the nature of design.) 3
4) Ability to develop, select and use modern techniques and tools necessary for engineering applications; ability to use information technologies effectively.
5) Ability to design experiments, conduct experiments, collect data, analyze and interpret results to examine engineering problems or discipline-specific research topics.
6) The ability to work effectively in disciplinary and multidisciplinary teams; individual work skill.
7) Effective communication skills in Turkish oral and written communication; at least one foreign language knowledge; ability to write effective reports and understand written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8) Awareness of the need for lifelong learning; access to knowledge, ability to follow developments in science and technology, and constant self-renewal.
9) Conform to ethical principles, and standards of professional and ethical responsibility; be informed about the standards used in engineering applications.
10) Awareness of applications in business, such as project management, risk management and change management; awareness of entrepreneurship, and innovation; information about sustainable development.
11) Information about the universal and social health, environmental and safety effects of engineering applications and the ways in which contemporary problems are reflected in the engineering field; awareness of the legal consequences of engineering solutions.
12) Knowledge on advanced calculus, including differential equations applicable to automotive engineering; familiarity with statistics and linear algebra; knowledge on chemistry, calculus-based physics, dynamics, structural mechanics, structure and properties of materials, fluid dynamics, heat transfer, manufacturing processes, electronics and control, design of vehicle elements, vehicle dynamics, vehicle power train systems, automotive related regulations and vehicle validation/verification tests; ability to integrate and apply this knowledge to solve multidisciplinary automotive problems; ability to apply theoretical, experimental and simulation methods and, computer aided design techniques in the field of automotive engineering; ability to work in the field of vehicle design and manufacturing. 2

Learning Activity and Teaching Methods

Expression
Individual study and homework
Lesson
Reading
Homework
Problem Solving
Report Writing
Q&A / Discussion

Assessment & Grading Methods and Criteria

Written Exam (Open-ended questions, multiple choice, true-false, matching, fill in the blanks, sequencing)
Homework

Assessment & Grading

Semester Requirements Number of Activities Level of Contribution
Homework Assignments 1 % 10
Midterms 1 % 40
Final 1 % 50
total % 100
PERCENTAGE OF SEMESTER WORK % 50
PERCENTAGE OF FINAL WORK % 50
total % 100

Workload and ECTS Credit Grading

Activities Number of Activities Duration (Hours) Workload
Course Hours 14 4 56
Midterms 1 39 39
Final 1 40 40
Total Workload 135