Mechanical Engineering

The latest industrial revolution is named as Industry 4.0, which is defined as the combination of smart manufacturing systems and developed information technologies. The success model of Industry 4.0 is enabled by a group of tools such as cloud computing, machine learning, big data, internet of things, and cyber physical systems. This course provides a study of Industry 4.0 and its revolutionary implications to smart manufacturing, smart products/services, and smart cities. The implementation, opportunities and challenges of Industry 4.0 are also discussed. The powerful change in production techniques will require the extensive use of digital intelligence in the entire production process. As one of the important manufacturing methods of Industry 4.0, additive manufacturing (AM) or three-dimensional (3D) printing is introduced in the second part of course. 3D printing offers numerous benefits to a smart factory, such as high production efficiency, time and material saving, rapid prototyping, and decentralized production methods. This course provides a comprehensive study on the liquid, solid and powder-based 3D printing methods. It also offers insights on the applications and future trend of 3D printing.

This course examines topics on kinematic analysis of mechanisms (position, velocity, and acceleration analysis of solid bodies and mechanisms) and design/synthesis of mechanisms (how to determine the geometry of a mechanism to achieve kinematic
goals). 

This course critically examines the technology of energy systems that will be acceptable in a world faced with global warming, local pollution, and declining supplies of oil. It covers conventional fossil fuel energy systems, renewable energy systems (wind, solar, ocean), and non-carbon emitting energy systems.

This course examines irrotational flow, circulation, 2D airfoils, thin airfoil theory, 3D wings, lifting line theory, boundary layers, turbulence, supersonic flow, shock waves, expansion fans, transonic flow, and swept wings.

This course teaches students to use finite element programs in a practical way to solve problems in linear elastic stress analysis. Upon completion of the course, students are able, in a later industrial setting, to undertake the analysis of real problems with a fair understanding of sensible modelling procedures. In support of this, the course is split into two stages: the theoretical study of the finite element method, with emphasis on understanding what goes on inside a typical, modern, commercial program; and practical experience in analysis using an industry-standard, interactive, finite element program. 

This course examines the aerodynamics and thermodynamics of aircraft gas turbines and rockets and provide the tools to design and evaluate the performance of jet engines. It will also present the current environmental impacts of aviation and paths for more sustainable aviation.

This course provides research training for exchange students. Students work on a research project under the guidance of assigned faculty members. Through a full-time commitment, students improve their research skills by participating in the different phases of research, including development of research plans, proposals, data analysis, and presentation of research results. A pass/no pass grade is assigned based a progress report, self-evaluation, midterm report, presentation, and final report.

This course provides individual research training for students in the Junior Year Engineering Program through the experience of belonging to a specific laboratory at Tohoku University. Students are assigned to a laboratory with the consent of the faculty member in charge. They participate in various group activities, including seminars, for the purposes of training in research methods and developing teamwork skills. The specific topic studied depends on the instructor in charge of the laboratory to which each student is assigned. The methods of assessment vary with the student's project and laboratory instructor. Students submit an abstract concerning the results of their individual research each semester and present the results near the end of the program.

This course studies Machine Learning, an important sub-area of Artificial Intelligence. It is designed to help students understand some key technologies such as linear classifiers, support vector machines, decision trees and neural networks, through the process of applying them to actual data and developing Python programs. 

This course aims to instruct students on machine learning and basic skills on data processing. Students will be expected to read and write Python programs and modules by the end of the course.

This special lab course nurtures international students' creative competency by offering them opportunities for learning in communities of research practice. The student's supervisor arranges the research topic. Students give three oral presentations during the study period. In the presentations, students integrate ideas and analyses on laboratory results into creative and academically coherent work. FrontierLab program coordinators and supervisors attend and evaluate the final oral presentation.