關於機械系-師資
林錦德 Lin, Chin-Te
助理教授
Office：E4-266
Tel：03-4267379
Campus Extension：34379
Fax：03-4254501
E-mail：chintelin@ncu.edu.tw
Lab：E2-218
Campus Extension：34363
學經歷
Education & Experiences
Ph.D., National Taiwan University, Taiwan, 2012
MS National Taiwan University, Taiwan, 2005
BS National Taiwan University, Taiwan, 2002
Deputy Manager, Intelligent Machine Technology Center, Industrial Technology Research Institute, 201701-201902
Engineer, Intelligent Machine Technology Center, Industrial Technology Research Institute, 201209-201612
Visiting Scholar, Laboratoire Interdisciplinaire de Physique, Université Joseph Fourier, 201105-201112
教授課程
Courses Taught
ME6036- Optimal Design (EMI, English as a Medium of Instruction)
ME5205-Intelligent manufacturing in practice (I)
ME5203- Cyber-Physical Integration Technology)
ME6108- Special Seminar in Smart Manufacturing and Management
ME2025- Department Computer Network and Programming
ME2037ME2038- Mechanical Drawing
ME3099- Programming and Its Applications
ME5204- Manufacturing Network Technologies
研究專長
Research Areas
Manufacturing system engineering
Optimization
Interdisciplinary integration
Finite Element Analysis
Computer-aided Design
實驗室
Laboratory
E2-218 IoT-enabled Manufacturing Lab
Fundamental Training
Industrial Control and PLC Programming: Centered on Structured Text (ST) under the IEC 61131-3 standard, focusing on industrial control logic design and practical implementation.
System Programming: Using general-purpose programming languages such as Python, JavaScript, C/C++, and C# to develop capabilities in data processing, control interfacing, and application system development.
Computer-Aided Engineering and Manufacturing: Covering CAD / CAE / CAM to build foundational skills in mechanical design, engineering analysis, and manufacturing processes.
Artificial Intelligence and Data Analysis: Learning machine learning and AI methods as fundamental tools for engineering modeling and analysis.
Optimization Design and Design of Experiments: Establishing systematic design and analysis capabilities to support engineering parameter design and performance improvement.
Research Focus
Smart Manufacturing and Automation System Integration and Evolution: Integrating industrial control, system programming, and engineering tools to develop deployable smart manufacturing system architectures.
Data-Driven Engineering Modeling and Decision-Making: Combining experimental data, sensor data, and AI methods for system modeling, prediction, and decision support.
Optimization-Oriented System Design Methods: Focusing on optimization and design of experiments to study performance enhancement and design strategies for complex engineering systems.
Cyber–Physical Integration and Digital Twin Applications: Integrating physical systems with virtual models to develop digital twin applications for system verification, analysis, and control.