TÜRKÇE
ENGLISH
Information
| Unit | FACULTY OF ENGINEERING |
| FOOD ENGINEERING PR. | |
| Code | IG117 |
| Name | Computer Aided Engineering |
| Term | 2026-2027 Academic Year |
| Semester | 1. Semester |
| Duration (T+A) | 2-2 (T-A) (17 Week) |
| ECTS | 3 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Belirsiz |
| Type | Normal |
| Label | C Compulsory |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Dr. Öğr. Üyesi Sinan KEYİNCİ |
| Course Instructor |
Dr. Öğr. Üyesi Sinan KEYİNCİ
(Güz)
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
The aim of this course is to develop students' ability to use computer aided engineering tools for solving food engineering problems. Students are expected to gain basic skills in algorithmic thinking, numerical calculation, data analysis, visualization, process simulation and engineering interpretation. The course also aims to introduce artificial intelligence supported engineering approaches and to develop awareness about ethical, reliable and responsible use of computational and AI tools in engineering practice.
Course Content
This course introduces computer aided engineering tools for solving engineering problems in food science and technology. The course covers basic computational thinking, data processing, numerical calculations, visualization, process modeling and simulation through MATLAB and related engineering software environments. Food engineering examples such as heat transfer, fluid flow, mixing, drying, energy balance, quality control and process optimization are used throughout the course. Within the scope of the course, students will also be introduced to artificial intelligence assisted engineering applications. Basic machine learning concepts, data driven prediction, image based quality assessment, AI supported coding and responsible use of AI tools in engineering practice will be discussed.
Course Precondition
None
Resources
1. "MATLAB Programming for Engineers" by Stephen J. Chapman (latest edition) 2. Supplementary material: Lecture notes 3. Introductory materials on data analysis, machine learning and image processing for engineering applications.
Notes
Students must install MATLAB. Toolboxes for Data Analysis, Image Processing and Curve Fitting are recommended. A student license is recommended.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Explains the role and importance of computer aided engineering tools in food engineering applications. |
| LO02 | Uses basic algorithmic thinking and computational problem solving approaches for engineering problems. |
| LO03 | Performs numerical calculations, data analysis and visualization using MATLAB or similar computational tools. |
| LO04 | Organizes, analyzes and interprets experimental and process data related to food engineering. |
| LO05 | Solves basic food engineering problems such as heat transfer, fluid flow, mixing, drying and energy balance using computer aided methods. |
| LO06 | Uses curve fitting, regression and basic optimization methods in food engineering applications. 7 |
| LO07 | Explains the use of artificial intelligence and machine learning approaches for data prediction, process monitoring and quality control in food engineering. |
| LO08 | Evaluates the reliability, limitations and ethical use of computer aided and AI supported engineering results. |
| LO09 | Reports and presents computational results using graphs, tables and technical explanations. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Have sufficient knowledge in the fields of basic sciences (mathematics / science) and food engineering and the ability to use theoretical and applied knowledge in these areas in complex engineering problems. | |
| PLO01 | - | ||
| PLO02 | Bilgi - Kuramsal, Olgusal | Identifies, defines and solves complex engineering problems in applications in the fields of food engineering and technology. | 5 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Gains the ability to apply a complex system or process related to food products and production components using modern design methods under certain constraints and conditions. | |
| PLO04 | Bilgi - Kuramsal, Olgusal | Choosing and using modern technical tools necessary for analysis and solution of complex problems encountered in food engineering and technology applications; For this purpose, he/she uses information technologies. | 5 |
| PLO05 | Bilgi - Kuramsal, Olgusal | Gaining laboratory skills for the analysis and solution of complex problems in the field of food engineering, designing an experiment, conducting an experiment, collecting data, analyzing and interpreting the results. | 4 |
| PLO06 | Bilgi - Kuramsal, Olgusal | Takes responsibility individually and as a team member to solve problems encountered in food engineering applications. | |
| PLO07 | Bilgi - Kuramsal, Olgusal | Gains the ability to communicate verbally and in writing in Turkish / English related to the field of food engineering, to write reports, to prepare design and production reports, to present effectively and to use communication technologies. | |
| PLO08 | Bilgi - Kuramsal, Olgusal | Recognizing the necessity of lifelong learning and constantly improving himself/herself in the field of food engineering. | 4 |
| PLO09 | Bilgi - Kuramsal, Olgusal | Gains the awareness of food legislation and management systems and professional ethics. | |
| PLO10 | Bilgi - Kuramsal, Olgusal | Using the knowledge of project design and management, he/she attempts to develop and realize new ideas about food engineering applications; have information about sustainability. | |
| PLO11 | Bilgi - Kuramsal, Olgusal | Has awareness about the effects and legal consequences of engineering practices related to food safety and quality on consumer health and environmental safety within the framework of national and international legal regulations. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to Computer Aided Engineering in Food Engineering. Basic concepts, engineering problem solving and computational thinking. | Course syllabus and basic concepts are reviewed. | Öğretim Yöntemleri: Anlatım |
| 2 | Introduction to MATLAB and computational environments. Interface, variables, operators and basic calculations. | MATLAB interface and basic commands are reviewed. | Öğretim Yöntemleri: Anlatım |
| 3 | Vectors, matrices and arrays. Data organization and basic matrix operations for engineering problems. | Vectors, matrices and basic operations are reviewed. | Öğretim Yöntemleri: Anlatım |
| 4 | Programming fundamentals. Scripts, functions, loops and conditional statements. | Scripts, functions, loops and conditional statements are reviewed. | Öğretim Yöntemleri: Gösterip Yaptırma |
| 5 | Data import, export and preprocessing. Working with experimental and process data in food engineering. | Sample data files and data import operations are reviewed. | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 6 | Data visualization. Plotting experimental data, customized graphs, comparison of process variables and technical interpretation. | Plotting and data visualization notes are reviewed. | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 7 | Numerical solution of engineering problems. Solving nonlinear equations and basic numerical applications in food processes. | Numerical solution examples are reviewed. | Öğretim Yöntemleri: Proje Temelli Öğrenme |
| 8 | Mid-Term Exam | Previous topics are reviewed for the mid-term exam. | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Curve fitting, regression and parameter estimation. Application examples from drying, heating, cooling and quality data. | Curve fitting and regression notes are reviewed. | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 10 | Optimization in food engineering. Process parameter optimization and area under curve calculations. | Optimization examples are reviewed. | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 11 | Heat transfer and energy balance applications. Computer aided solution of basic food process problems. | Heat transfer and energy balance topics are reviewed. | Öğretim Yöntemleri: Anlatım |
| 12 | Fluid flow, mixing and pressure loss applications. Basic computer aided calculations and engineering interpretation. | Fluid flow and pressure loss notes are reviewed. | Öğretim Yöntemleri: Anlatım |
| 13 | Introduction to artificial intelligence in food engineering. Basic machine learning concepts, data driven prediction and process monitoring. | Introductory AI and machine learning notes are reviewed. | Öğretim Yöntemleri: Gösterip Yaptırma |
| 14 | AI assisted quality control and image-based applications. Examples from food classification, defect detection and visual inspection. | Image processing and quality control examples are reviewed. | Öğretim Yöntemleri: Anlatım |
| 15 | Responsible use of AI and final application study. Reliability, ethics, limitations, reporting and presentation of computer aided engineering results. | Notes on ethical use of AI and reporting are reviewed. | Öğretim Yöntemleri: Gösterip Yaptırma |
| 16 | Term Exams | All topics are reviewed for the term exam. | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Application examples are reviewed for the term exam. | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 4 | 56 |
| Out of Class Study (Preliminary Work, Practice) | 8 | 1 | 8 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 2 | 3 | 6 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 2 | 2 |
| Final Exam | 1 | 3 | 3 |
| Total Workload (Hour) | 75 | ||
| Total Workload / 25 (h) | 3,00 | ||
| ECTS | 3 ECTS | ||