Grinding is used to improve surface roughness and dimensioning precision in the metal industry. A large amount of heat is released during grinding. Most of this heat is transferred to the workpiece. In this case, a grinding burn may occur on the workpiece. Grinding burn is a significant issue in the production of bearings. If a burn occurs on the workpiece during grinding, the surface quality deteriorates and the internal structure and mechanical qualities of the material are adversely affected. Therefore, detecting grinding burn is critical for bearing manufacturers. In this study, during the grinding of the bearing parts, the machine conditions were changed in accordance with the real grinding scenario, and burnt and non-burned bearing data were obtained with the acoustic emission sensor. Then, time-frequency representations were obtained from these data with the continuous wavelet transform. These images have been classified in the GoogLeNet Network by transfer learning. Combinations of faulty/ normal data processed under different machine settings and combinations of faulty/ normal data processed with the same machine parameters were classified with the proposed method and compared. Faulty bearings processed with the same machine characteristics were detected with 100% accuracy using the proposed method. This percentage gives a reliable result for bearing producers. This study contributes to the literature in three ways: (a) It is based on data collected under real-world grinding situations. (12 different machine settings were employed.) (b) Various machine conditions were compared. (c) Faulty bearings were detected with high accuracy.

This study presents the design, modeling, and prototyping of an external rotor permanent magnet synchronous motor (ER-PMSM) specifically for elevator traction systems. The external rotor design aims to surpass the efficiency of conventional inner rotor gearless elevator traction motors. A commercially available 4 kW inner rotor permanent magnet synchronous motor (IR-PMSM) was selected for comparative analysis. Critical parameters, including stator tooth tip thickness, slot tip radius, slot height, stator yoke height, stator tooth thickness, and the number of turns per phase, were optimized to enhance efficiency. The artificial bee colony (ABC) algorithm was utilized for the first time to determine the optimal configuration of an external rotor PMSM. The prototype was fabricated and subjected to rigorous testing using a dedicated electrical motor test setup. Comparative results demonstrated a significant improvement in efficiency for the ER-PMSM over the IR-PMSM, with the efficiency increasing from 72.5% to 84.67% at nominal operating conditions.

Pes planus is a common postural deformity related to the medial longitudinal arch of the foot. Radiographic examinations are important for reproducibility and objectivity; the most commonly used methods are the calcaneal inclusion angle and Mery angle. However, there may be variations in radiographic measurements due to human error and inexperience. In this study, a deep learning (DL)-based solution is proposed to solve this problem. Lateral radiographs of the right and left foot of 289 patients were taken and saved. The study population is a homogeneous group in terms of age and gender, and does not provide sufficient heterogeneity to represent the general population. These radiography (X-ray) images were measured by 2 different experts and the measurements were recorded. According to these measurements, each X-ray image is labeled as pes planus or non-pes planus. These images were then filtered and resized using Gaussian blurring and median filtering methods. As a result of these processes, 2 separate data sets were created. Generally accepted DL models (AlexNet, GoogleNet, SqueezeNet) were reconstructed to classify these images. The 2-category (pes planus/no pes planus) data in the 2 preprocessed and resized datasets were classified by fine-tuning these reconstructed transfer learning networks. The GoogleNet and SqueezeNet models achieved 100% accuracy, while AlexNet achieved 92.98% accuracy. These results show that the predictions of the models and the measurements of expert radiologists overlap to a large extent. DL-based diagnostic methods can be used as a decision support system in the diagnosis of pes planus. DL algorithms enhance the consistency of the diagnostic process by reducing measurement variations between different observers. DL systems accelerate diagnosis by automatically performing angle measurements from X-ray images, which is particularly beneficial in busy clinical settings by saving time. DL models integrated with smartphone cameras can facilitate the diagnosis of pes planus and serve as a screening tool, especially in regions with limited access to healthcare.