Course unit title Level of course unit Course unit code Type of course unit Semester of course unit Local credit ECTS credit Syllabus
PRODUCTIVE DESIGN AND APPLICATIONS Second cycle ETM 545 1 7.50 7.50 Print
   
Description of course unit
Prerequisites and course requisities
Language of instruction Turkish
Coordinator Assoc. Prof. Dr. Bülent Kaya
Lecturer(s)
Teaching assitant(s)
Mode of delivery Face to face and supervised on project based on digital classroom
Course objective Generative design tools and software have become increasingly sophisticated, empowering designers to leverage computational power for more efficient and creative problem-solving in the design process. The course covers, here''''s how generative design typically works: Define Parameters and Constraints: The designer specifies the design parameters, goals, and constraints that the algorithm should consider. Parameters could include dimensions, materials, performance criteria, and more.
Course description Generative design is a design approach that involves using algorithms and computational methods to generate multiple design iterations based on a set of predefined parameters, goals, and constraints. This approach is particularly prevalent in fields such as architecture, engineering, product design, and manufacturing. The goal of generative design is to explore a wide range of design possibilities, optimize designs for specific criteria, and ultimately arrive at innovative and efficient solutions.

Course contents
1 Introduction to FEM, sample project...Warm up
2 Running a case with FEM, sample project and result reporting.
3 Introduction to topology optimization. A case implemantation with topology optimization.
4 A case implemantation with topology optimization.
5 Introduction to generative design. Introduction of the tool: Fusion 360.
6 Generative design, Define Parameters and Constraints: The designer specifies the design parameters, goals, and constraints that the algorithm should consider. Parameters could include dimensions, materials, performance criteria, and more.
7 Generative design,Algorithmic Exploration: The generative design algorithm explores a vast design space by generating multiple variations based on the specified parameters. This can involve mathematical algorithms, evolutionary algorithms, or other computational methods.
8 Generative design,Algorithmic Exploration: The generative design algorithm explores a vast design space by generating multiple variations based on the specified parameters. This can involve mathematical algorithms, evolutionary algorithms, or other computational methods.
9 Generative design,Evaluate and Optimize: Each generated design is evaluated against the defined goals and constraints. The algorithm then optimizes the designs based on the performance metrics, iteratively refining the solutions.
10 Generate Iterations: The process continues, generating numerous iterations of designs. These iterations can vary significantly, allowing the exploration of unconventional and innovative solutions.
11 Generate Iterations: The process continues, generating numerous iterations of designs. These iterations can vary significantly, allowing the exploration of unconventional and innovative solutions.
12 Generative design, A case study, with Human Input: While the algorithm plays a crucial role, human designers are still involved in the process. They provide creative input, make decisions based on aesthetic preferences, and guide the algorithm by adjusting parameters or selecting promising designs.
13 Generative design, A case study, with Human Input: While the algorithm plays a crucial role, human designers are still involved in the process. They provide creative input, make decisions based on aesthetic preferences, and guide the algorithm by adjusting parameters or selecting promising designs.
14 Generative design, A case study, with Human Input: While the algorithm plays a crucial role, human designers are still involved in the process. They provide creative input, make decisions based on aesthetic preferences, and guide the algorithm by adjusting parameters or selecting promising designs.
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Learning outcomes of the course unit
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*Contribution level of the course unit to the key learning outcomes
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Number of stars refer to level of contribution from 1 (the least) to 5 (the most)

Planned learning activities, teaching methods and ECTS work load
  Quantity Time (hour) Quantity*Time (hour)
Lectures (face to face teaching) 12 3 36
Study hours out of classroom (study before and after the class) 10 10 100
Homework 0 0 0
Presentation / seminar 2 10 20
Quiz 0 0 0
Preparation for midterm exams 1 10 10
Midterm exams 1 4 4
Project (term paper) 0 0 0
Laboratuar 0 0 0
Field study 0 0 0
Preparation for final exam 1 10 10
Final exam 1 2 2
Research 0 0 0
Total work load     182
ECTS     7.50

Assessment methods and criteria
Evaluation during semester Quantity Percentage
Midterm exam 0 0
Quiz 0 0
Homework 0 0
Semester total   0
Contribution ratio of evaluation during semester to success   0
Contribution ratio of final exam to success   0
General total   0

Recommended and required reading
Textbook
Additional references

Files related to the course unit