Optimization of an Optical Test Bench for Tire Properties Measurement and Tread Defects Characterization

Frustrated Total Internal Reflection Measurement System for Pilot Inceptor Grip Pressure

Sensing the interaction between the pilot and the control inceptors can provide important information about the pilot's activity during flight, potentially enabling the objective measurement of the pilot workload, the application of preventive actions against loss of situational awareness, and the identification of the insurgence of adverse couplings with the vehicle dynamics. This work presents an innovative pressure-sensing device developed to be seamlessly integrated into the grips of conventional aircraft control inceptors. The sensor, based on frustrated total internal reflection of light, is composed of low-cost elements and can be easily manufactured to be applicable to different hand pressure ranges. The characteristics of the sensor are first demonstrated in laboratory calibration tests. Subsequently, applications in flight simulator testing are presented, focusing on the objective representation of the pilot's instantaneous workload.

The defect detection for X-ray images based on a new lightweight semantic segmentation network

The tire factory mainly inspects tire quality through X-ray images. In this paper, an end-to-end lightweight semantic segmentation network is proposed to realize the error detection of bead toe. In the network, firstly, the texture feature of different regions of tire is extracted by an encoder. Then, we introduce a decoder to fuse the output feature of the encoder. As the dimension of the feature maps is reduced, the positions of bead toe in the X-ray image have been recorded. When evaluating the final segmentation effect, we propose a local mIoU(L-mIoU) index. The segmentation accuracy and reasoning speed of the network are verified on the tire X-ray image set. Specifically, for 512 × 512 input images, we achieve 97.1% mIoU and 92.4% L-mIoU. Alternatively, the bead toe coordinates are calculated using only 1.0 s.

Approach to Automated Visual Inspection of Objects Based on Artificial Intelligence

The article discusses the possibility of object detector usage in field of automated visual inspection for objects with specific parameters, specifically various types of defects occurring on the surface of a car tire. Due to the insufficient amount of input data, as well as the need to speed up the development process, the Transfer Learning principle was applied in a designed system. In this approach, the already pre-trained convolutional neural network AlexNet was used, subsequently modified in its last three layers, and again trained on a smaller sample of our own data. The detector used in the designed camera inspection system with the above architecture allowed us to achieve the accuracy and versatility needed to detect elements (defects) whose shape, dimensions and location change with each occurrence. The design of a test facility with the application of a 12-megapixel monochrome camera over the rotational table is briefly described, whose task is to ensure optimal conditions during the scanning process. The evaluation of the proposed control system with the quantification of the recognition capabilities in the individual defects is described at the end of the study. The implementation and verification of such an approach together with the proposed methodology of the visual inspection process of car tires to obtain better classification results for six different defect classes can be considered as the main novel feature of the presented research. Subsequent testing of the designed system on a selected batch of sample images (containing all six types of possible defect) proved the functionality of the entire system while the highest values of successful defect detection certainty were achieved from 85.15% to 99.34%.

Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing

This paper introduces and implements an efficient training method for deep learning–based anomaly area detection in the depth image of a tire. A depth image of 16 bit integer size is used in various fields, such as manufacturing, industry, and medicine. In addition, the advent of the 4th Industrial Revolution and the development of deep learning require deep learning–based problem solving in various fields. Accordingly, various research efforts use deep learning technology to detect errors, such as product defects and diseases, in depth images. However, a depth image expressed in grayscale has limited information, compared with a three-channel image with potential colors, shapes, and brightness. In addition, in the case of tires, despite the same defect, they often have different sizes and shapes, making it difficult to train deep learning. Therefore, in this paper, the four-step process of (1) image input, (2) highlight image generation, (3) image stacking, and (4) image training is applied to a deep learning segmentation model that can detect atypical defect data. Defect detection aims to detect vent spews that occur during tire manufacturing. We compare the training results of applying the process proposed in this paper and the general training result for experiment and evaluation. For evaluation, we use intersection of union (IoU), which compares the pixel area where the actual error is located in the depth image and the pixel area of the error inferred by the deep learning network. The results of the experiment confirmed that the proposed methodology improved the mean IoU by more than 7% and the IoU for the vent spew error by more than 10%, compared to the general method. In addition, the time it takes for the mean IoU to remain stable at 60% is reduced by 80%. The experiments and results prove that the methodology proposed in this paper can train efficiently without losing the information of the original depth data.

Dual optical force plate for time resolved measurement of forces and pressure distributions beneath shoes and feet

Frustrated total internal reflection (FTIR) imaging was used to perform remote optical measurements of the forces/pressures exerted beneath shoes and feet during a number of different training activities including countermovement jumps, jogging and drop jumps. A single camera was used to simultaneously image two acrylic, FTIR waveguide imaging elements from below, at frame rates up to 200 frames per second. The images obtained using the camera were converted into pressure/force maps using a previously developed theory which combines the mechanics of contact of soft objects and the scattering of evanescent waves. The forces obtained from the optical measurements were shown to be in good agreement with measurements obtained from load cells placed beneath the FTIR imaging elements. The ability to produce accurate spatial maps of the force/pressure distribution beneath soft contacting objects such as feet and shoe outsoles at high frame rates has numerous potential applications in sports sciences and medicine.

Sensor-Based Real-Time Detection in Vulcanization Control Using Machine Learning and Pattern Clustering

Recent paradigm shifts in manufacturing have resulted from the need for a smart manufacturing environment. In this study, we developed a model to detect anomalous signs in advance and embedded it in an existing programmable logic controller system. For this, we investigated the innovation process for smart manufacturing in the domain of synthetic rubber and its vulcanization process, as well as a real-time sensing technology. The results indicate that only analysis of the pattern of input variables can lead to significant results without the generation of target variables through manual testing of chemical properties. We have also made a practical contribution to the realization of a smart manufacturing environment by building cloud-based infrastructure and models for the pre-detection of defects.