The Split Hopkinson Pressure Bar (SHPB) experiment is commonly employed to assess the dynamic mechanical properties of materials under high strain-rate conditions (10²-10⁴ s-¹) through the propagation of elastic stress waves via pressure bars. The precision and dependability of SHPB measurements are heavily influenced by the alignment of the specimen with the bars. Misalignment can lead to flexural vibrations, causing waveform distortion and undermining the assumption of onedimensional stress waves. While previous research has explored the impact of misalignment on waveform characteristics, pinpointing the specific sources of distortion from measured signals remains a challenge. This study introduces a machine learning-based classification method that extracts features from distorted SHPB waveforms to identify the type of misalignment. Incident wave signals under various misalignment scenarios were simulated using the commercial finite element software LS-DYNA, and the extracted features were utilized to create a training dataset. Several machine learning models, including XGBoost, were trained and evaluated, with XGBoost yielding the highest accuracy and F1-score. The trained model was then applied to experimentally measured distorted waveforms to validate its effectiveness. This proposed approach facilitates the automated diagnosis of distortion sources in SHPB data, reducing the need for manual interpretation and improving analysis efficiency.
In the semiconductor manufacturing industry, efficient operation of wafer transfer robots has a direct impact on productivity and product quality. Ball screw misalignment anomalies are a critical factor affecting precision transport of robots. Early diagnosis of these anomalies is essential to maintaining system efficiency. This study proposed a method to effectively diagnose ball screw misalignment anomalies using 1D-CNN and 2D-CNN models. This method mainly uses binary classification to distinguish between normal and abnormal states. Additionally, explainable artificial intelligence (XAI) technology was applied to interpret diagnostic decisions of the two deep learning models, allowing users to convince prediction results of the AI model. This study was based on data collected through acceleration sensors and torque sensors. It compared accuracies of 1D-CNN and 2D-CNN models. It presents a method to explain the model"s predictions through XAI. Experimental results showed that the proposed method could diagnose ball screw misalignment anomalies with high accuracy. This is expected to contribute to the establishment of reliable abnormality diagnosis and preventive maintenance strategies in industrial sites.
The assembly misalignment of a high maneuver precision guidance missile such as anti-air interceptor could have poor influence on the performance of the system and lead to fatal mission failure. Thus, several methods to minimize the assembly misalignment of missiles have been suggested, including assembly guiding tools and measuring devices. However, previously suggested solutions have disadvantages in versatility and cost. In this study, the low cost and universally applicable solution based on inclinometers for measuring assembly misalignment of a missile is introduced. The comparison between measurement data of the suggested system and a three-dimensional laser tracker for a missile assembly misalignment was conducted. The results show the suggested system can quantify a missile assembly misalignment with comparable precision and accuracy.
This paper presents an analysis of the stiffness and fatigue life of double-row angular contact ball bearings (D-ACBBs). To this end, a comprehensive quasi-static model was developed for D-ACBBs subjected to five degree-of-freedom (DOFs) loading and displacement. The model was verified by a commercial computational program. A rigorous numerical investigation was performed, based on the developed model regarding the effects of external load, rotational speed, axial clearance, bearing arrangement, and angular misalignment on the stiffness and fatigue life of the D-ACBB. The D-ACBB was subjected to negative axial clearance and yielded improved performance in terms of fatigue life and stiffness. The effect of angular misalignment on the bearing fatigue life was found to be significantly dependent on the amount of axial clearance. The moment stiffness of the D-ACBB in a back-to-back arrangement was higher than the face-to-face arrangement owing to the increased effective load center distance, whereas the radial stiffness and fatigue life were almost unchanged for back-to-back and face-to-face arrangements.
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