The Forming Limit Diagram (FLD) is a criterion used to assess the formability of sheet metal during a manufacturing process. Traditionally, FLDs are obtained through manual measurements using Mylar tape or through the use of automatic deformation measurement systems such as ARMIS and ARGUS. However, the use of Mylar tape is not user-friendly and can result in errors. Additionally, the cost of using automatic measuring equipment is high. To address these challenges, we propose a method that utilizes a low-cost USB digital microscope and the Python-based open-source library, OpenCV, to obtain forming limit diagrams. This approach allows for the measurement of deformation on specimens by analyzing circles printed on them. To evaluate the performance of this method, a circular grid was printed on a sus430 0.3 t specimen and a nakajima test was conducted. The strain data obtained using this system was then compared to the FLD obtained with the ARGUS system. The results confirmed that the formability of sheet metal can be assessed at a lower cost using our proposed method.

This study developed a defect-detecting system for automotive wheel nuts. We proposed an image processing method using OpenCV for efficient defect-detection of automotive wheel nuts. Image processing method focused on noise removal, ratio adjustment, binarization, polar coordinate system formation, and orthogonal coordinate system conversion. Through data collection, preprocessing, object detection model training, and testing, we established a system capable of accurately classifying defects and tracking their positions. There are four defect types. Types 1 and 2 defects are defects of products where the product is completely broken circumferentially. Types 3 and 4 defects are defects are small circumferential dents and scratches in the product. We utilized Faster R-CNN and YOLOv8 models to detect defect types. By employing effective preprocessing and post-processing steps, we enhanced the accuracy. In the case of Fast RCNN, AP values were 0.92, 0.93, 0.76, and 0.49 for types 1, 2, 3, and 4 defects, respectively. The mAP was 0.77. In the case of YOLOv8, AP values were 0.78, 0.96, 0.8, and 0.51 for types for types 1, 2, 3, and 4 defects, respectively. The mAP was 0.76. These results could contribute to defect detection and quality improvement in the automotive manufacturing sector.

Bell’s palsy is a disease that occurs primarily between ages of 15 and 60, especially in middle-aged individuals. Although this disease gradually recovers within weeks to months, recurrence and permanent sequelae are possible. Its causes are diverse and unclear. Appropriate treatment is unknown, threatening lives of patients with this condition. In this study, we measured the degree of facial paralysis in a model of Bell’s palsy patients using OpenCV and the H.B grade measurement method and classified measured values according to H.B grade classification. This enabled prediction of the type and risk of diseases that might occur depending on the degree of facial paralysis. Additionally, we utilized more coordinate data to confirm movement of facial muscles by region to address limitations of the Nottingham system measurement method. We graded the level of this movement to enable intuitive confirmation and confirmed differences between existing Nottingham system and the H.B grade. This simple system could determine the level of paralysis in patients with Bell’s palsy and their corresponding risk level for related diseases. It enables information on causative disease of patients with Bell’s palsy to be quickly obtained, enabling prompt treatment and support.

Tool wear is an essential parameter in determining tool life, machining quality and productivity. Current or power signals from motor drivers in machine have been used to estimate tool wear. However, accuracy of tool wear estimation was not enough to measure the amount of tool wear. In this study, flank wear of a drill tool was measured using vision sensor module which has zoom lens, CCD camera and image processing technique. The vision module was set up in the machine tool. Therefore, the image was acquired without ejecting the tool from the machine. Image processing techniques were used to define the cutting edge shape, tool diameter, and the wear edge on cutting rips with the proposed measuring algorithm. The automatically calculated wear value was compared with a manually measured value. As a result, the difference between the manual and the automatic methods was below 4.7%. The proposed method has an advantage to decrease the measuring time and improve measuring repeatability because the tool is measured holding chuck in a spindle.