Special Topics in IE: Digital Manufacturing (IE 48005)

2019 Spring
Faculty of Engineering and Natural Sciences
Industrial Engineering(IE)
3
6.00 / 6.00 ECTS (for students admitted in the 2013-14 Academic Year or following years)
Emre Özlü emreozlu@sabanciuniv.edu,
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English
Undergraduate
MS309 IE309
Interactive lecture,Studio work/practice
Interactive,Learner centered,Simulation
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CONTENT

This course is designed for students who are interested in digitalization in manufacturing environment. It provides a background starting from the digitalization at shop floor level to the analysis of the gathered data to support decision making process. In the first lectures, the methods and hardware for data collection are covered. Labview is used for practical demonstrations and applications. The necessary calculations for conversion of the analog signal to digital signal are taught. After these basics, the evolution of the manufacturing science is reviewed and the link with the digitization is defined. As a next step, several digitalization methods are covered under the Digital Twin topic. Finally, digital transformation is reviewed from a more general perspective. The topics covered during the course will be applied through a term project. The students will make two presentations; one for reporting their progress and one for their final report they will submit.

OBJECTIVE

The overall objective of this course is to introduce the basics of digitalization and applications in manufacturing. The students will gain knowledge on the basics of analog to digital conversion, sensors and data acquisition, digital twins and data integration in manufacturing environment. They will also gain ability to collect and analyse data and further use it to predict several decision parameters at the most basic level.

LEARNING OUTCOME

Students will understand the basic of digitization, the differences of digitalization and applications in manufacturing environment.
Students will demonstrate the ability to evaluate and integrate the digitalization approaches with the manufacturing system and process models through digital twin approaches.
Students will gain ability to evaluate and apply basic sensor application, data acquisition techniques and their relationship with the manufacturing environment.
Students will gain ability to discuss and further investigate the impact of digital transformation, smartness, cognition, digital twin and data acquisition on the manufacturing systems.
Students will gain ability to use Labview coding and applications on how to create, analyze and optimize a manufacturing facility.

PROGRAMME OUTCOMES


1. Understand the world, their country, their society, as well as themselves and have awareness of ethical problems, social rights, values and responsibility to the self and to others. 1

2. Understand different disciplines from natural and social sciences to mathematics and art, and develop interdisciplinary approaches in thinking and practice. 5

3. Think critically, follow innovations and developments in science and technology, demonstrate personal and organizational entrepreneurship and engage in life-long learning in various subjects. 5

4. Communicate effectively in Turkish and English by oral, written, graphical and technological means. 3

5. Take individual and team responsibility, function effectively and respectively as an individual and a member or a leader of a team; and have the skills to work effectively in multi-disciplinary teams. 3


1. Possess sufficient knowledge of mathematics, science and program-specific engineering topics; use theoretical and applied knowledge of these areas in complex engineering problems. 5

2. Identify, define, formulate and solve complex engineering problems; choose and apply suitable analysis and modeling methods for this purpose. 4

3. Develop, choose and use modern techniques and tools that are needed for analysis and solution of complex problems faced in engineering applications; possess knowledge of standards used in engineering applications; use information technologies effectively. 5

4. Ability to design a complex system, process, instrument or a product under realistic constraints and conditions, with the goal of fulfilling specified needs; apply modern design techniques for this purpose. 4

5. Design and conduct experiments, collect data, analyze and interpret the results to investigate complex engineering problems or program-specific research areas. 3

6. Knowledge of business practices such as project management, risk management and change management; awareness on innovation; knowledge of sustainable development. 3

7. Knowledge of impact of engineering solutions in a global, economic, environmental, health and societal context; knowledge of contemporary issues; awareness on legal outcomes of engineering solutions; understanding of professional and ethical responsibility. 2


1. Use mathematics (including derivative and integral calculations, probability and statistics), basic sciences, computer and programming, and electronics engineering knowledge to design and analyze complex electronic circuits, instruments, software and electronics systems with hardware/software.

2. Analyze and design communication networks and systems, signal processing algorithms or software using advanced knowledge on differential equations, linear algebra, complex variables and discrete mathematics.


1. Comprehend key concepts in biology and physiology, with emphasis on molecular genetics, biochemistry and molecular and cell biology as well as advanced mathematics and statistics.

2. Develop conceptual background for interfacing of biology with engineering for a professional awareness of contemporary biological research questions and the experimental and theoretical methods used to address them.


1. Formulate and analyze problems in complex manufacturing and service systems by comprehending and applying the basic tools of industrial engineering such as modeling and optimization, stochastics, statistics. 5

2. Design and develop appropriate analytical solution strategies for problems in integrated production and service systems involving human capital, materials, information, equipment, and energy. 5

3. Implement solution strategies on a computer platform for decision-support purposes by employing effective computational and experimental tools. 5


1. Applying fundamental and advanced knowledge of natural sciences as well as engineering principles to develop and design new materials and establish the relation between internal structure and physical properties using experimental, computational and theoretical tools.

2. Merging the existing knowledge on physical properties, design limits and fabrication methods in materials selection for a particular application or to resolve material performance related problems.

3. Predicting and understanding the behavior of a material under use in a specific environment knowing the internal structure or vice versa.


1. Familiarity with concepts in statistics and optimization, knowledge in basic differential and integral calculus, linear algebra, differential equations, complex variables, multi-variable calculus, as well as physics and computer science, and ability to use this knowledge in modeling, design and analysis of complex dynamical systems containing hardware and software components. 4

2. Ability to work in design, implementation and integration of engineering applications, such as electronic, mechanical, electromechanical, control and computer systems that contain software and hardware components, including sensors, actuators and controllers. 4

ASSESSMENT METHODS and CRITERIA

  Percentage (%)
Final 40
Exam 30
Presentation 30

RECOMENDED or REQUIRED READINGS

Readings

Several journal papers that will be supplied through out the semester.