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Course Criteria
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4.00 Credits
Explores development of the mechanical design process and its open-ended nature. Reviews fundamentals of stress and theories of failure including fatigue considerations in the analysis of various machine components. Treatment is given to shafts, springs, screws, connections, lubrications, bearings, gears, and tolerances. Includes team-based design projects that involve modeling and the design process.
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4.00 Credits
Presents the theoretical backgrounds for the analysis and design of simple feedback control systems, differential equations, and Laplace transforms. Treats system modeling, linear approximations, transfer functions, and block diagrams; and transient and frequency response and stability-frequency domain and root locus methods. Other topics may include linear systems with time lag and relay servomechanisms with small nonlinearities.
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4.00 Credits
Introduces theories of thermal energy transport, including conduction, convection, and thermal radiation, and the design of thermal systems. Solution methods are developed for steady-state and transient conduction problems including thermal circuit analogies, internal energy sources and extended surfaces. Convective heat transfer mechanisms are introduced and correlations to evaluate the heat transfer coefficient are discussed. Methodologies for calculating the thermal radiation heat transfer between surfaces are introduced. These theories are integrated with thermodynamics and fluid mechanics in the design of thermal systems, including heat exchangers. Includes an open-ended design project and students are expected to use computational methods throughout the course.
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4.00 Credits
Offers elective credit for courses taken at consortium institutions.
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4.00 Credits
Offers elective credit for courses taken at consortium institutions.
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4.00 Credits
Offers elective credit for courses taken at consortium institutions.
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4.00 Credits
Offers elective credit for courses taken at consortium institutions.
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4.00 Credits
Covers the theory and applications of expert systems and neural networks in engineering. Topics include knowledge representation (semantic networks, frames, production rules, and logic systems), problem-solving methods (heuristic search algorithms, forward and backward chaining, constraint handling, truth, and maintenance), approximate reasoning methods (Bayesian, Dempster-Shafer, fuzzy logic, and certainty factors), and expert system shells. Reviews background material on important neural network architectures such as feed-forward neural networks, Kohonen's feature maps, radial basis function networks, and adaptive resonance theory networks. Discusses neural network applications in several areas including group technology; part family formation; manufacturing systems design, process, and machine tool monitoring and diagnosis; system identification and control; and product inspection.
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4.00 Credits
Designed to give students an understanding of the fundamentals of lean thinking and train them in applying this knowledge to practical problems. Uses case studies from different disciplines to help students learn lean principles and develop skills to implement them in practice. Covers theory and applications of lean six sigma, in which lean focuses on waste reduction while six sigma strives to eliminate defects. A knowledge-driven and customer-focused approach to creating value, lean thinking calls for process changes to eliminate waste, shorten product delivery time, improve product quality, and curtail costs. Key tenants of lean thinking are value, value stream, flow, pull, and perfection. Lean thinking is imperative for organizations aspiring to stay competitive by creating and delivering products in less time while improving customer satisfaction.
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4.00 Credits
Explores the field of mass customization (MC) in which a company provides customers with goods and services that suit their individual needs but does so with the efficiency and cost associated with mass production. MC is important in many sectors including computers, automotive, health care, banking, insurance, and tourism. Provides students with conceptual understanding and implementation strategies of MC, based on principles of industrial engineering, mechanical engineering, management science, and marketing. Topics include typology of mass-customized production systems, manufacturing processes for MC, information needs of MC, customer focus, marketing issues, technology enablers, implementation methods, and case studies. Lectures, case discussions, plant visits, guest lectures, and a term project are used. Cross-disciplinary activities, particularly between engineering and business students, are encouraged wherever possible.
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