Virtual simulation experiment of the design and manufacture of a beer bottle-defect detection system

Background Machine learning-based beer bottle-defect detection is a complex technology that runs automatically;however,it consumes considerable memory,is expensive,and poses a certain danger when training novice operators.Moreover,some topics are difficult to learn from experimental lectures,such as digital image processing and computer vision.However,virtual simulation experiments have been widely used to good effect within education.A virtual simulation of the design and manufacture of a beer bottle-defect detection system will not only help the students to increase their image-processing knowledge,but also improve their ability to solve complex engineering problems and design complex systems.Methods The hardware models for the experiment(camera,light source,conveyor belt,power supply,manipulator,and computer)were built using the 3DS MAX modeling and animation software.The Unreal Engine 4(UE4)game engine was utilized to build a virtual design room,design the interactive operations,and simulate the system operation.Results The results showed that the virtual-simulation system received much better experimental feedback,which facilitated the design and manufacture of a beer bottle-defect detection system.The specialized functions of the functional modules in the detection system,including a basic experimental operation menu,power switch,image shooting,image processing,and manipulator grasping,allowed students(or virtual designers)to easily build a detection system by retrieving basic models from the model library,and creating the beer-bottle transportation,image shooting,image processing,defect detection,and defective-product removal.The virtual simulation experiment was completed with image processing as the main body.Conclusions By mainly focusing on bottle mouth defect detection,the detection system dedicates more attention to the user and the task.With more detailed tasks available,the virtual system will eventually yield much better results as a training tool for image processing education.In addition,a novel visual perception-thinking pedagogical framework enables better comprehension than the traditional lecture-tutorial style.