Friedman Chi-Square Test Analysis of Carbon Steel Tensile Stress Data Using Python

This research introduces an innovative method to evaluate data representation in materials science and engineering., utilizing actual tensile strength test results of carbon steel integrated into three-dimensional computer-aided drawing (3D CAD) simulations. Five groups of learners are tasked with manually plotting 100 specific datasets along simulated stress-strain displacement curves. The objective is to enhance understanding of material properties, particularly carbon steel, through practical exercises involving the application of the tensile strength equation. Learners compute force from stress values and determine dimensions from the simulated visual to calculate strain. In addition, this study uses the Friedman Chi-Square Test to analyze and compare the manual plotting results, supported by Python programming for further statistical evaluation. The findings offer valuable insights into comparing different manual plotting techniques, demonstrating their educational relevance for engineering students. Furthermore, this research establishes a method to assess the accuracy of learners' plotting outputs, which means that this exercise does not only facilitate learning, but also provides a basis for grading. It also emphasizes the importance of understanding various points on the stress-strain curve in decision-making during the design process. This approach contributes substantially to materials science education by alternative methodologies in data representation assessment and engineering education.