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  • 3.00 Credits

    Statics of particles and rigid bodies, free body diagrams, forces and moments of forces, equilibrium of rigid bodies, distributed forces, centroids, and moments of inertia. (Fall, Spring) [Graded (Standard Letter)] Prerequisite(s): MATH 1210 - Prerequisite Min. Grade: D- Prerequisite:    MATH 1210
  • 3.00 Credits

    Mechanical properties and behavior of materials under stress. Analysis of stresses in beams and shafts. Plane stress and strain, principal stresses. Axial, torsional, and flexural loading. Mohr's circle. (Fall, Spring) [Graded (Standard Letter)] Co-requisite(s): ENGR 2145 Prerequisite(s): ENGR 2010 and MATH 1220 - Prerequisite Min. Grade: D- Prerequisite:    ENGR 2010 A MATH 1220 Corequisite:    ENGR 2145
  • 1.00 Credits

    Lab course that accompanies ENGR 2140. Experimental determination of the modulus of elasticity, Poisson's ratio, stress concentrations, principal strains and stresses, the relationship between vertical shear force and bending moment distribution in a beam, and the critical buckling force. (Fall, Spring) [Graded (Standard Letter)] Co-requisite(s): ENGR 2140 Prerequisite(s): ENGL 2010 - Prerequisite Min. Grade: D- Prerequisite:    ENGL 2010 Corequisite:    ENGR 2140
  • 3.00 Credits

    An introduction to computer programming. Topics to be covered include procedural programming in Matlab/Simulink, user interfaces, program syntax and semantics, input/output operations, applications, documentation, and the management of complex programming projects. Problem-solving, case studies, and applications will be emphasized. (Fall, Spring) [Graded (Standard Letter)] Prerequisite(s): MATH 1210 - Prerequisite Min. Grade: D- Prerequisite:    MATH 1210
  • 1.00 Credits

    This lab accompanies ME 3100. (As Needed) [Graded (Standard Letter)] Co-requisite(s): ME 3100 Corequisite:    ENGR 3010
  • 3.00 Credits

    An introduction to technical project management. The course prepares students with basic project-management skills applicable to any sized project. Topics to be covered include teamwork, the engineering design process, planning, engineering economics, systems engineering methods and tools, risk management and mitigation, testing, configuration control and documentation, and technical presentations and documentation. The course focuses on applying the concepts and methods of effective project management in a team environment to prepare students for the senior capstone sequence. (Spring) [Graded (Standard Letter)] Prerequisite(s): ENGR 1010 or ENGR 2145 - Prerequisite Min. Grade: D- Registration Restriction(s): Junior or Senior standing required Prerequisite:    ENGR 1010 O ENGR 2145
  • 3.00 Credits

    Coursework emphasizes creative and critical thinking, planning, design, execution and statistical evaluation of experiments, multidisciplinary team work, and project management. Students will use engineering and project-management principles and concepts learned to-date to propose a project, develop requirements and constraints, complete initial design, and present initial results to a panel of professional reviewers. Prepares students to complete the project in ENGR 4085. (Fall) [Graded (Standard Letter)] Prerequisite(s): ENGR 3030 - Prerequisite Min. Grade: D- Prerequisite:    ENGR 3030
  • 3.00 Credits

    This is a continuation of ENGR 4025. Coursework emphasizes creative and critical thinking, planning, design, execution and statistical evaluation of experiments, multidisciplinary team work, and project management. Students will use engineering principles and concepts learned to-date to propose, design, complete, and formally present a comprehensive project to a panel of professional reviewers. (Spring) [Graded (Standard Letter)] Prerequisite(s): ENGR 4025 - Prerequisite Min. Grade: D- Prerequisite:    ENGR 4025
  • 1.00 - 4.00 Credits

    Description unavailable. (As Needed) [Pass/Fail] Prerequisite(s): Instructor permission Repeatable for Add'l Credit? Yes - Total Credits: 8
  • 3.00 Credits

    The Artificial Intelligence Practicum offers hands-on experience in applying AI and machine learning to real-world engineering challenges. Through collaborative, project-based learning, students work on industry-relevant applications such as machine learning algorithms, computer vision, and predictive maintenance. This course develops practical skills, equipping students to excel in AI-related roles and contribute to technological advancements in engineering. (As Needed) [Graded (Standard Letter)] Prerequisite(s): ME 3600 - Prerequisite Min Grade: D- Can the prerequisite be taken concurrently? Yes Prerequisite:    ME 3600