Yaochi Tang

This study investigated the sound absorption coefficient of micro-perforated panels with multiple design factors, using the Taguchi method for analysis and optimization. The panels, made using stereolithography technology for three-dimensional printing, had four key factors: perforation rate, hole diameter, panel thickness, and wave surface. Their absorption coefficients were measured using an impedance tube and analyzed in two directions: namely, the absorption coefficient across various frequency bands and the frequency corresponding to the maximum absorption coefficient. By utilizing the Taguchi method's orthogonal array, this study calculated the interaction and influence of these factors on sound absorption, completing the optimization and prediction of the experiment. Results showed that perforation rate and panel thickness significantly affected the absorption coefficient across all frequencies, while hole diameter mainly influenced low-frequency absorption. The wavy surface had a minor impact. The study optimized micro-perforated panels with the Taguchi method using less experimental data, and experimental verification was performed, which aligned with the prediction results. This study demonstrated that optimizing micro-perforated panels with the Taguchi method is efficient and accurately identifies the optimal combination, allowing predictions of absorption for the noises in different frequency bands or specific frequencies.

This study proposed a novel machine learning–driven methodology for detecting potential defects in computer numerical control (CNC) spindle manufacturing. The methodology, which analyzes 13 real‐world built‐in spindles, employs t ‐distributed stochastic neighbor embedding ( t ‐SNE) for data visualization and enhances k ‐means++ clustering with the Davies–Bouldin Index (DBI) for the automatic selection of the optimal number of clusters, significantly surpassing traditional inspection methods in identifying subtle yet critical defects. This study utilized the fast Fourier transform (FFT) for precise feature extraction. The integration of these advanced algorithms accurately identified defects and categorized them, thus optimizing manufacturing processes. The inclusion of the DBI in the k ‐means++ clustering algorithm facilitated an objective evaluation of cluster quality, ensuring that the selected number of clusters accurately represents the underlying data patterns. This automated selection of the optimal k value enhanced the stability and reliability of the defect detection process. The proposed methodology substantially reduced the yield of defective spindles by identifying and addressing defects before spindle installation in CNC machines. The proactive defect detection and intervention system rectified potential failures at an early stage and improved the overall quality control processes. This proactive approach enhanced operational efficiency and reliability, reduced rework and warranty claims costs, and aligned with industrial needs while addressing a critical gap in academic research. This study significantly contributes to spindle manufacturing, ensuring high‐quality production outcomes and bridging important gaps in both industrial application and academic research.

Currently, robots on the market have varying operational requirements due to their different functions. The care robots are primarily used to serve people under care. Therefore, reducing the amount of noise they make while moving is important. This study measured their noise in an anechoic room. This study found that the most noise is produced when they move forward and backward at full speed. The robot’s vibration signal was measured and compared with the noise source to propose the most effective way to reduce the noise. Due to the robot’s small internal space, methods for noise reduction are limited. The care robot’s development must adopt quick customizable 3D printing technologies, produce sound absorption/insulation materials independently, determine the best sound absorption/insulation material and structural configuration, use impedance tubes to measure the sound absorption coefficient and sound insulation performance, and select the appropriate materials and implement them into the robot for actual tests. These results indicate that the noise value of the main contributing frequency can be reduced by 15 dB at most. Furthermore, this study also used an acoustic simulation analysis to calculate and simulate the vibration and noise of the care robot. The vibration and noise signals during the robot’s operation were measured and compared to provide a reference for manufacturers in design. Finally, experimental materials were added to the simulation and compared with the noise reduction results. The results show similar trends. Therefore, the model developed in this study may provide a fast, low-cost, and feasible method for the noise reduction of care robots.

Bearings are one of the critical components of any mechanical equipment. They induce most equipment faults, and their health status directly impacts the overall performance of equipment. Therefore, effective bearing fault diagnosis is essential, as it helps maintain the equipment stability, increasing economic benefits through timely maintenance. Currently, most studies focus on extracting fault features, with limited attention to establishing fault thresholds. As a result, these thresholds are challenging to utilize in the automatic monitoring diagnosis of intelligent devices. This study employed the generalized fractal dimensions to effectively extract the feature of time-domain vibration signals of bearings. The optimal fault threshold model was developed using the receiver operating characteristic curve, which served as the baseline of exception judgment. The extracted fault threshold model was verified using two bearing operation experiments. The experimental results revealed different damaged positions and components observed in the two experiments. The same fault threshold model was obtained using the method proposed in this study, and it effectively diagnosed the abnormal states within the signals. This finding confirms the effectiveness of the diagnostic method proposed in this study.

Fault diagnosis is an important method for maintaining the stable and safe running state of mechanical equipment. As most mechanical equipment faults are induced by the bearing assembly, bearing fault diagnosis is of considerable importance. At present, the mainstream intelligent diagnostic techniques include supervised learning and unsupervised learning. Supervised learning requires manual labeling and data classification, which is unfavorable for massive data amounts. Therefore, how to effectively use labeled data to increase the accuracy of diagnosis is critical, especially when the bearing failure cannot be labeled at the very beginning. This paper proposes a time–frequency analysis of the short-time Fourier transform and wavelet transform methods based on unsupervised learning. The time axis was integrated to obtain the marginal frequency of two frequency domains as a diagnostic feature, and then two clustering centroids were established automatically by the K-means of unsupervised learning. The signals were divided into two classes based on the nearest clustering centroid as the criteria for diagnosis. Finally, other bearings in different positions were classified and diagnosed using the nearest clustering centroid in the same experiment to verify the effectiveness of the method proposed in this study.

Acoustical materials are usually used for architectural design, ambient noise, and traffic noise to absorb sound. The sound absorption coefficient (SAC) is often used as the performance index of acoustical materials. It is measured using the reverberation room method (RRM) and the impedance tube method (ITM). The sound source of the RRM is a random incidence in various directions, whereas that of the ITM is a normal incident. The SAC measured by the RRM is more extensive, practical, and costly than ITM. Usually, the SAC measured by RRM is used as the commercially available acoustical materials index. However, RRM requires large-area test samples for measurement and a special sound field of reverberation room for test experiments. Therefore, this study aims to use the ITM to measure SAC. The measured SAC, frequency, thickness, and material density were employed as independent variables and substituted in the obtained multiple regression model to predict SAC obtained by RRM. In addition, the measurement characteristics of impedance tubes provide varisized calibers for different frequency ranges to measure a relatively accurate SAC. Based on the abovementioned characteristics, this study proposed a dual-model for estimating the SAC of the reverberation room method to achieve the best-estimated result. After experimental validation, the dual-model estimated result was compared with SAC from the reverberation room method. It was observed that the maximum absolute value of all errors did not exceed 0.16. Most of the absolute errors were below 0.1, proving the accuracy of this method. Therefore, the method proposed in this study can shorten the acoustical material development schedule for manufacturers and save the cost of the development process.

The bearing is an important part of mechanical equipment. Its condition directly influences the operation of the entire mechanical equipment. Faults in bearings may induce fatal disasters and heavy economic losses, in worst-case scenarios. In fault diagnosis, many studies have only proposed the characteristics of faults; however, without standards for quantifying the faults, automated diagnosis could not be realized. Based on the generalized fractal dimensions (GFDs) and the receiver operating characteristic (ROC) curve, this study proposed a method to characterize initial failure trends and quantize the bearing failure standard. The size of GFDs was calculated using the vibration signal measured during the operation of a bearing. The optimal classification model was determined based on the ROC curve as the criterion of damage. The model trained by the signals of the training group was used in the check analysis of the signals of the validation group. The accuracy of this quantitative analysis was validated by experimental results. Finally, three bearings in different positions were diagnosed by this model under the same test conditions, and the effectiveness of the quantitative diagnosis of damage proposed in this study was validated.

This study established the prognostics and health management system for bearing failure. The vibration signals measured during the bearing operation were used for prognostics. First, the time‐domain signal of vibration was calculated through generalized fractal dimensions, and the relationship diagram of generalized fractal dimensions and time was obtained. Then, the trend of bearing failure was compared by the GFDal results. However, the results can only be used for qualitative feature extraction. The bearing failure at the beginning cannot be determined by qualitative methods. Therefore, this study further converted the calculation results of GFDs into a Gauss distribution curve based on the statistical method under normal operation of the bearing. The Gauss distribution curve of the bearing under normal operation and at different time was overlapped. The overlap rate of the bearing area under different times was calculated. The minimum value was taken as the diagnostic standard, which was the optimal threshold of bearing failure defined in this study and was used as the quantitative basis for bearing failure. Therefore, the comparison of the area overlap rate under the Gauss distribution curve between the normal bearing and the bearing under test could provide diagnosis to the bearing failure. Moreover, the time point of the initial failure of the bearing could also be estimated based on the optimal failure threshold.