An Experiential Learning in Electrical Machines Laboratory Course

Abstract

This work illustrates the use of Design of Experiments (DOE) as an experiential learning in Electrical Machines laboratory. DOE is a systematic method to determine the relationship between factors affecting a process and the output of that process. This information is needed to manage process inputs in order to optimize the output. The objective of this work is to provide an exposure to students on experimentation and simulation environment as a part of structured enquiry problem in the course. A common problem statement was given to the class of eighty students. A batch of five students worked as a team in order to accomplish the task. The problem statement focused on developing the second order response surface methodology based model to predict the speed of a motor by conducting DOE with two control factors and three levels each. Sixteen teams were given common problem statement with varied ranges of control factors. Full Factorial Design (FFD) was selected as a suitable design of experimental layout plan. Each team conducted nine trails for two control factors and three levels. To further validate the model, nine additional experiments with random control factors have been carried out to test the accuracy of the model. Students were introduced to open source simulation tool which was used to predict the response. As per the estimate of laboratory schedule for solving structured enquiry problem, the students were assessed in accordance with the rubrics published well in advance. The competencies addressed for activity are problem analysis, ability to design experiments, individual and team work, simulation. The analysis was concentrated on deviation of predicted and actual measured values of motor speed, regression, and variation of output response with respect to two control factors. This activity helped the students to analyze the results for the proposed problem. As per the assessment, the attainment of the competencies - problem analysis, experimental design planning, individual and team work is around 65.6%, 90.5% and 84.2% respectively. However there is scope for improvement in area of coding as the attainment is comparatively low with 72.5%. As per the students' feedback, their learning is enhanced after having undergone this activity in comparison with classroom learning.