The assembly process of Printed Wire Board (PWB) components is becoming increasingly intricate and multifaceted. Due to the constraints imposed by electronic component specifications and manufacturing cost considerations, the coexistence of through-hole technology and surface-mount technology (SMT) is anticipated to persist for an extended period during the transition from traditional assembly methodologies to SMT. Most enterprises adopt a dispensing process, heavily relying on parameters delineated by equipment manufacturers or experiential insights derived from production practices. When modifications occur within an enterprise's production system, there is often a marked increase in defects associated with printing quality throughout the manufacturing process, significantly escalating repair labor requirements and man-hours expended. Furthermore, any occurrences of inspection failures can disrupt normal customer operations, resulting in substantial risks related to product quality assurance. To systematically enhance the issue of surface mount quality and production capacity, reduce investment in SMT dispensing equipment, and boost enterprise competitiveness, this study employs statistical data and applies 6σ and management statistics methods, utilize MINITAB statistical software as the analysis tool, redefine high customer satisfaction and low defect criteria, conduct process analysis, and derive optimal parameters for thick mold equipment. Production practice demonstrates that these new parameters significantly enhance surface mount quality while improving overall enterprise benefits.
The escalator system, which experiences data fluctuations during continuous daily operation, plays a vital role in subway stations. To accurately assess the health status of the escalator, it is crucial to predict its future condition. To mitigate the effects of significant fluctuations in data and leverage the correlations among cross-variables, the Multi-scale Average Pool Transformer (MA-Transformer) is proposed. The model pre-processes the data using average pooling layers with different kernel sizes, segregates the intricate escalator data into trend and residual components, and predicts them separately with two independent networks. This approach enhances the essential data representations and mitigates the adverse effects of frequent data fluctuations. Given the substantial number of variable types in escalator data, this paper introduces two modules that focus on the self-attention mechanism to model the inter-relationships between multiple variable types. Additionally, the trends and residuals output from different average pooling layers are fed into the self-attention layer, which provides a more comprehensive representation of features at the same time point. This enables the model to better capture the overall trend changes and the distribution of anomalies that deviate from the trend. This study collected data from 19 classes from 2 escalators at the engineering site for training and testing. Experimental results demonstrate that the MA-Transformer achieves superior detection accuracy, outperforming evaluated time-series prediction baseline models in terms of both Mean Absolute Error (MAE) and Root Mean Square Error (RMSE).
Traffic flow prediction is a key issue in urban traffic management, providing important support for urban planning and traffic diversion. In traffic flow prediction, LSTM (Long Short-Term Memory networks) and GRU (Gated Recurrent Units) capture temporal information with different focuses, but single-cell candidate states often lack full feature aggregation, limiting model performance. Challenges include long-term dependencies and large-scale data processing. This paper proposes an innovative dual-gating mechanism integrating LSTM and GRU candidate states to enhance accuracy. Experimental results show significant outperformance in Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), improving traffic flow predictions and urban traffic management.
In present work, the failure analysis was conducted on a switching power supply using various methods including macroscopic morphology, circuit analysis, disassembly morphology, X-ray CT, microscopic morphology, and corrosion product analysis. The results indicated that the MOS tube was the location of the failure in the switching power supply. Additionally, the surface solder of the exposed leads was found to have corroded to some extent due to the influence of corrosion media. Combined with the working principle of enhanced NMOS pipe, the cause of the failure is the switching power supply under the high power board device temperature rise, plus the service environment humidity, the pin corrosion, the pin and the packaging interface sealing performance is reduced, the water molecules in the air into the packaging device, and there is a large electric field inside the MOS tube during the working process. The internal metal of the device was affected by comprehensive factors such as temperature, humidity, chlorine ions, and electric field distribution, leading to electrochemical migration and subsequent breakdown between G and D terminals.
Harvesting mechanical energy from low-frequency, multidirectional daily vibrations have been considered as an excellent candidate to replace the conventional batteries in many fields. In order to achieve effectively harvesting the energy in these vibrations, this paper designs a piezoelectric vibration energy harvester with a ball-bowl structure. Benefiting from the structural characteristics, the proposed scheme can capture vibrations in different directions, and the working frequencies show a good consistency and are close to the frequency of daily vibrations. The captured energy is transmitted to the piezoelectric beam by magnetic interaction and converted into electrical energy. The analysis and experimental results have demonstrated that the designed scheme has a good performance in harvesting multidirectional vibration energy.
