教學大綱與進度

課程基本資料：

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
115-1	363529	工程最佳化與應用	1	3.0	3	★	柯俊宏	機電所 機械自動化外生專班	2	0	機電所和機電外生所合開，英文授課

教學大綱與進度：

教師姓名	柯俊宏
Email	Maning_98@yahoo.com
最後更新時間	2026-06-03 21:45:26
課程大綱	Course Description: This course provides a comprehensive and hands-on introduction to engineering optimization methodologies, with a particular emphasis on the integration of Computer-Aided Engineering (CAE) tools and simulation-driven design. Using ANSYS as the primary computational platform, students will learn how optimization techniques can be applied to solve complex engineering problems in product design, structural analysis, and operational performance. The course focuses on continuous-variable optimization problems involving real-valued objective functions and engineering constraints. Students will develop both theoretical understanding and practical skills in formulating optimization problems, selecting appropriate solution strategies, and interpreting results within realistic engineering contexts. Special attention is given to the interaction between numerical optimization algorithms and finite element analysis (FEA), enabling students to perform simulation-based design optimization. Through lectures, case studies, and project-based learning, participants will gain experience in applying optimization methods to multidisciplinary engineering applications, including mechanical, biomedical, and dental engineering systems. Learning Objectives: Upon successful completion of this course, students will be able to: * Understand the fundamental principles and mathematical foundations of engineering optimization. * Formulate engineering design problems using objective functions, design variables, and constraints. * Apply optimization algorithms within the ANSYS environment. * Integrate finite element analysis with optimization workflows for simulation-driven design. * Evaluate and interpret optimization results to support engineering decision-making. * Develop practical optimization models for real-world engineering applications. Key Topics: * Fundamentals of Engineering Optimization * Mathematical Formulation of Optimization Problems * Design Variables, Objective Functions, and Constraint Handling * Numerical Optimization Algorithms * Finite Element Analysis and Optimization Integration * Parametric Modeling and Design Exploration in ANSYS * Simulation-Based Design Optimization * Case Studies in Mechanical, Biomedical, and Dental Engineering * Sensitivity Analysis and Design Validation * Practical Applications and Industry-Oriented Projects Course Project: As a capstone project, students will develop and analyze a simulation-based optimization model using ANSYS. Representative projects may include the optimal design of a dental implant under biomechanical and anatomical constraints, balancing structural performance, durability, and clinical requirements through advanced engineering optimization techniques.
課程進度	Week Date (2026–2027) Topic 1 Sep 10, 2026 Course Introduction & Overview of Engineering Optimization 2 Sep 17, 2026 Introduction to ANSYS and CAE Tools 3 Sep 24, 2026 Optimization Techniques: Single-Variable Functions 4 Oct 1, 2026 Optimization Techniques: Multi-Variable Functions 5 Oct 8, 2026 Fundamentals of Finite Element Analysis (FEA) Using ANSYS 6 Oct 15, 2026 Introduction to Linear Programming and Its Engineering Applications 7 Oct 22, 2026 ANSYS Case Study: Dental Implant Modeling 8 Oct 29, 2026 Midterm Examination 9 Nov 5, 2026 Constrained Optimization and Optimality Conditions 10 Nov 12, 2026 ANSYS Application: Stress Analysis of Dental Implants 11 Nov 19, 2026 Transformation Methods in Constrained Optimization 12 Nov 26, 2026 Optimal Design of Dental Implants with ANSYS – Part I 13 Dec 3, 2026 Constrained Direct Search and Evolutionary Algorithms 14 Dec 10, 2026 Optimal Design of Dental Implants with ANSYS – Part II 15 Dec 17, 2026 Optimal Design of Dental Implants with ANSYS – Part III 16 Dec 24, 2026 Optimal Design of Dental Implants with ANSYS – Part IV 17 Dec 31, 2026 Final Project Presentation I 18 Jan 7, 2027 Final Project Presentation II
評量方式與標準	Grading Criteria: Homework Presentations – 30% Optimization Project Design – 30% Final Project Presentation – 30% Attendance and Participation – 10%
使用教材、參考書目或其他	【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】 使用外文原文書：是 Engineering Optimization Methods and Applications G.V. Reklaitis A. Ravindran K.M. Ragsdell
課程諮詢管道	Students can ask questions in the LINE Group or email directly to me: Professor Jyuhn Ke Email: jyuhnk@gmial.com Cell: 0935421518
延伸教學與資源	● 混成學習 (Blended Learning) 課程結合實體授課與線上學習（同步視訊／非同步活動）。 Combines face-to-face instruction with online learning (including synchronous video sessions or asynchronous activities). ● 參與式創新教學 (Participatory & Innovative Teaching) 強調學生參與與產出，導入翻轉、PBL/專題、設計思考或遊戲化等策略。 Emphasizes student engagement and outcomes by adopting strategies such as Flipped Classroom, PBL (Problem/Project- Based Learning), Design Thinking, or Gamification. ● 數位教材資源應用 (Application of Digital Resources) 採用影音多媒體或具開放授權之資源（如OCW、開放教科書）作為主要或輔助教材。 Adopts multimedia or open-licensed resources (e.g., OCW, Open Textbooks) as primary or supplementary teaching materials.
課程對應SDGs指標	SDG3：良好健康和福祉（Good Health and Well-being） SDG4：優質教育（Quality Education） SDG5：性別平等（Gender Equality） SDG7：可負擔的潔淨能源（Affordable and Clean Energy） SDG8：尊嚴就業與經濟發展（Decent Work and Economic Growth） SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
課程是否導入AI	● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools） ● 簡單介紹AI的基本概念（Briefly introduce the basic concept of AI）
備註	1. It is recommended that instructors use multiple evaluation methods (such as participation in discussions, online tests or quizzes, submission of reports and assignments, adjustment of grading criteria, etc.) to evaluate students’ learning performance for semester grades.