Exploring the Efficacy of Simulation-Based Learning (SBL) for Enhancing Program Outcomes in Mechanical Engineering: A Case Study on Engineering Graphics

This research investigates the effectiveness of Simulation-Based Learning (SBL) in enhancing student comprehension of complex mechanical engineering concepts, such as fluid dynamics, thermodynamics, FEA, and mechanics. The integration of tools like MATLAB, SolidWorks, ANSYS Fluent, AutoCAD, FluidSim, BricsCAD, and GeoGebra allows students to simulate theoretical mechanics and engineering graphics, bridging the gap between theory and practice. Additionally, Machine Learning playgrounds offer hands-on AI experience. A key novelty is the development of an Automatic Engineering Drawing Sheet Evaluation Algorithm, utilizing Python-based image processing to automate the assessment of technical drawings, increasing efficiency and accuracy. GeoGebra further serves as a tool for assessing geometric transformations in engineering graphics. Integrating simulation tools into the first-year Engineering Graphics course has significantly enhanced Course Outcomes (CO) and Program Outcomes (PO), particularly PO5 (Modern Tool Usage), with a 94.29% improvement. Tools like AutoCAD and GeoGebra have enhanced student understanding of Orthographic Projections and Isometric Views, reflected in improved test scores and positive feedback. Case studies showcase how these tools align with various POs, highlighting the critical role of SBL in modern mechanical engineering education