Exploring feasibility of vision-based automated evaluation during laboratory courses in manufacturing

Manual assessment of laboratory exercises and assignments by human instructors is subjective and time-consuming, introducing errors, inconsistencies, and biases. A preliminary study developing an automated evaluation system utilizing computer vision has been proposed in this article to address these challenges. It utilizes computer vision to assess the accuracy and quality of components machined by students enrolled during the manufacturing laboratory course. The system includes image acquisition hardware, algorithms for objective decisions, and an interface for evaluating student performance. The system is implemented during one of the laboratory classes, and a comparative assessment is carried out with manual evaluations followed by student feedback. It has been shown that the system can address subjectivity concerns, reducing the workload on course instructors and teaching assistants. The research also broadens the utility of computer vision in manufacturing education and creates interest among enrolled students to appreciate the role of newer technologies in a core discipline.