This course is aimed at providing the undergraduate and graduate students in mechanical and manufacturing engineering basic knowledge and understanding of the major manufacturing processes for modeling, monitoring and control of these processes through a series of analytical and experimental techniques and tools, including group work for assignments and experiments.

This course will provide a strong foundation in the concepts, theories and applications of design engineering methodologies for effective manufacture of high quality products at low costs and high productivity. In addition to the lectures, the assembly and design analysis of "product based assembly kits" will be used to apply and help learn the tools presented in class. The final project includes the application of these tools to re-design a given product from a manufacturing and assembly perspective.

This course will provide a strong foundation in the concepts, theories and applications of design engineering methodologies for effective manufacture of high quality products at low costs and high productivity. In addition to the lectures, the assembly and design analysis of "product based assembly kits" will be used to apply and help learn the tools presented in class. The final project includes the application of these tools to re-design a given product from a manufacturing and assembly perspective.

This course introduces students to fundamentals of design, planning and control of manufacturing systems aided by computers. Concepts of control hardware, NC programming languages, software aspects related to NC manufacturing, programmable controllers, performance modeling of automated manufacturing systems, group technology and flexible manufacturing systems, etc. will be adressed.

Systems Engineering is a discipline necessary for cost-effective development of complex multi-disciplinary systems. Optimal design of modern systems for defense, transportation, telecommunications and energy, among other industries, requires a different perspective than the design of subsystems operating within them. This course presents principles and the practice of Systems Engineering, along with its origins in the aerospace and software industries, historical perspective and case studies of current interest.

Consideration of the mass, energy and force balances applied to compressible fluids. Isentropic flow, diabatic flow, flow with friction, wave phenomena and one-dimensional gas dynamics. Applications to duct flows and to jet and rocket propulsion engines.

Unsymmetrical bending of beams, thin plates, stress analysis of thick-walled cylinders, and rotating discs. Theory of elastic energy, curved beams, stress concentration, and fatique.

Modern powerplants for electric power generation and cogeneration. Thermodynamic analysis of different concepts of powerplants. Design studies of specific powerplants.

This course will include two major parts: machine learning theories and applications. Machine learning theories will cover legacy techniques (e.g., support vector machine, Bayesian inference) and then go deeper into deep learning (convolutional and recurrent neural network). The application part will cover some practical studies on how can we leverage the machine learning techniques to analyze the data collected from factory floors. Also, programming of the machine learning techniques (e.g., Python) will be covered in the class as well.

This course will include two major parts: machine learning theories and applications. Machine learning theories will cover legacy techniques (e.g., support vector machine, Bayesian inference) and then go deeper into deep learning (convolutional and recurrent neural network). The application part will cover some practical studies on how can we leverage the machine learning techniques to analyze the data collected from factory floors. Also, programming of the machine learning techniques (e.g., Python) will be covered in the class as well.