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At Kettering University, all mechanical engineering students take a two-course sequence in Dynamic Systems; the first course is Dynamic Systems with Vibrations, and the second is Dynamic Systems with Controls. In the past, the first course has been purely a lecture class, while the second has included both lecture and lab time. Both courses were modified, as a result of student experiences and feedback from industry partners, with the lab redesigned for Dynamic Systems with Controls and a lab component involving Matlab/Simulink modeling and simulations added to Dynamic Systems with Vibrations. This paper will focus on the lab addition to the first course, Dynamic Systems with Vibrations. This paper will first present the background of the course and reasons for the addition of the lab, including the desired student outcomes. Next, it will discuss the initial design of the labs and the evolution to the current set of lab experiences. This discussion will include the key topics covered, the learning objectives of the labs, and the practical challenges in implementation. Finally, data from student instructor/course surveys will be presented, and future goals for the lab component of the class will be discussed.

A computational 50th percentile male head and neck complex model was correlated to physical experimental data. The computational model utilizes 15 muscle pairs represented by the Hill Muscle Model with the complete head/neck system modeled using MADYMO. The model was used for analysis and optimization of activation and deactivation of muscle activity in flexion and extension. Sensitivity analysis performed using the model shows that, of the multiple parameters within the Hill Model, activation level and timing prove to have the greatest effect on the system kinematics. In addition, the rate by which an activation level is changed becomes an important factor in the simulation. With the use of numerical optimization techniques, a pattern was determined for the applied activation/deactivation rates and timing of flexors and extensors during flexion and extension of the head. The numerical optimization result correlated to within 9% of measured value during the initial flexion of the head. The optimized activation model reflected an activation onset 90 ms after the start of the impulse load, which agrees with published reaction times of muscles. Activation and deactivation rates for the extensors were found to be 1.7 and 0.29%, respectively. While the onset of activation of the flexor muscles occurred before rebound, it was found that muscles, at near the mid-plane, were triggered by the optimized model to abate the flexion. Rates of activation and deactivation of the flexors were found to be 0.9 and 0.3%, respectively. Both the extensors as well as the flexors were found to activate only up to 70% before deactivating. Therefore, it was evident from this study that using the Hill Muscle Model with the activation parameter modeled as binary, 0 or 100%, may lead to inaccurate simulation results.