In continuous-process systems, failures of rolling-element bearings typically cause accidents, reduced productivity, and production-related financial losses. Therefore, predicting both the lifespan of rolling-element bearings and their replacement time is crucial for preventing machine system failures. Accordingly, numerous studies have reported various machine and deep learning classifiers for predicting the lifespan of bearings. However, these studies did not consider degradation trends of bearings. Thus, this study aimed to develop an algorithm to predict the lifespan of a bearing by considering its degradation trend. A vibration dataset of bearings was obtained at low and high speeds. Using a second-order curve-fitting model, various degradation patterns in the dataset were classified. Appropriate time-domain or frequency-domain feature variables applicable to the design of a classifier were determined according to classified patterns. In addition, the classifier was trained using multiple bidirectional long short-term memories. Finally, the performance of the developed classifier was verified experimentally.
With global warming leading to abnormal weather phenomena and increasing carbon emissions, countries are implementing carbon emission reduction policies. Europe’s Carbon Border Adjustment Mechanism (CBAM) aims to promote environmentally responsible practices while maintaining industrial competitiveness. To avoid potential tariffs in the European market, Korea must vigorously pursue carbon emission reduction. Emphasizing renewable energy adoption is crucial for achieving eco-friendly and sustainable energy production. This study conducted an economic feasibility assessment for constructing small hydroelectric power plants using discharged energy from Goseong Green Power Plant. By evaluating economic viability, decision-makers could assess potential benefits and costs to support effective planning and implementation. Findings of this study could encourage investments in renewable energy projects, fostering a greener and more sustainable energy landscape for the future.
The subtle feature is one of the characteristic lines and represents the most noticeable line in the automotive panel. In this study, we proposed a method to predict the radius of curvature of products according to the material, its thickness, its punch angle, and its punch radius. The radius of curvature was divided into three regions, namely, the non-linear, transition, and linear regions. In the non-linear region, the prediction model for the radius of curvature with different forming conditions was derived using the finite element analysis. In the linear region, the radius of curvature was assumed to be the sum of the punch radius and the thickness of the material. In the transition region, a model connecting two regions (Non-linear and linear region) was developed based on the continuity condition. The prediction model presented a very small RMSE with the value of 0.314 mm. Using the prediction model, the radius of curvature with various forming variables could be predicted and the required radius of punch, to obtain a certain value of the radius of curvature, could be precisely predicted.
Cambolt that has two slot shape in thread, have been widely used to adjust wheel alignment in Hyundai and Kia motors. These slots in thread make stress more concentrated, and lead to yield more easily. This paper describes the optimizing process of the Cambolt figure, to maximize the yield load. Contribution of the Cambolt design factors to yield load was verified, through actual test and finite element analysis. Using the DFSS (Design for Six Sigma) method, we optimized the design factors of Cambolt, and confirmed the yield load was enhanced. This new Cambolt can provide more stable wheel alignment joints, by using a higher range of preload.
In this paper, we propose a method to generate the trajectory of a robotic shoe sole spray system by extracting target points from a 3-D model of a mold sole. Point cloud transformation based on the mold 3-D file format, Z-Axis uppermost point extraction, elimination of unnecessary points, and final target point selection are sequentially performed. The Catmull- Rom algorithm is then applied to plan spline trajectory that allows the robot end effector to spray at a constant speed by following the extracted target points. The proposed algorithm is validated on the test bed of a shoe sole spray system. Through the proposed method, the adhesive can be uniformly dispensed to the sole of the shoe in an atypical shape without the process of extracting the work point using the vision system.
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A smart factory with Big Data analytics is getting attention because of its ability to automate and make the manufacturing environment more intelligent. At the same time, higher reliability is required with a drastic increase in complexity and uncertainty within the current system of manufacturing fields. The pump is considered as one of the most crucial equipment as it can affect the overall manufacturing performance of the manufacturing processes and it needs to be timely diagnosed of its mechanical condition as a top priority. In this research, we propose an operation system of centrifugal pumps and a data-driven fault diagnostic model that is developed by collecting relevant multivariate data from several natures. Proposed machine learning models can be used for detecting and diagnosing pump faults via analytical processes containing signal preprocessing and feature engineering procedures. Simulation and case studies from rotating machinery have demonstrated the effectiveness of the proposed analytical framework not only for attaining quantitative reliability but practical usages in actual manufacturing fields as well.
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This paper examines the stability of the blades that convert the wind kinetic energy into the mechanical energy among the small wind power-generation systems, and proposes the design improvement for blades with a higher rigidity and a lighter weight than the conventional blades. The composite-specimen tensile test and static-load test are conducted to verify the reliability. To design the lightweight blade with the high stiffness, the displacement and the safety factor of the blade composed of the composite material are calculated from the structural-analysis results, and the optimal dimensional and material designs are performed. The optimal design parameters are selected by the shear-web lamination angle and the lamination thickness. The objective function is selected by the safety factor and the weight. For the optimum material design, the GFRP is converted into the CFRP. In this paper, the structural improvement is performed by optimizing the dimensional and material designs, the blade stiffness and weight are redesigned and compared with those of the designed blades, and the structural stability of the redesigned blades is also examined.
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Generally speaking, the high speed forming process is suitable for the precise manufacturing of hard-to-form and high strength materials. This study conducted microscale embossing and punching experiments by establishing a forming system that uses a laser induced acceleration. The changes in the flyer velocity with the laser energy, flyer thickness, and flyer diameter were measured using a high speed camera, and the effects of the noted acceleration characteristics of flyers on processing performance were investigated. It is particularly important that in the case of punching, the advantages of high speed processing, in which the accuracy was improved by increasing the shear zone of the workpiece, were identified. Significantly in the case of embossing, it was observed that the formability improved by increasing the flyer velocity as the flyer diameter decreased. However, in the case when the flyer thickness was decreased, increased energy was consumed in the plastic deformation of the flyer, and the advantages of high speed forming could not be realized. For this reason, further research is needed to take advantage and optimize the forming process using the laser induced acceleration through experiments which are noted as considering the various process variables and materials.
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Fault diagnosis and condition monitoring of rotating machines are important for the maintenance of the gas turbine system. In this paper, the Lab-scale rotor test device is simulated by a gas turbine, and faults are simulated such as Rubbing, Misalignment and Unbalance, which occurred from a gas turbine critical fault mode. In addition, blade rubbing is one of the gas turbine main faults, as well as a hard to detect fault early using FFT analysis and orbit plot. However, through a feature based analysis, the fault classification is evaluated according to several critical faults. Therefore, the possibility of a feature analysis of the vibration signal is confirmed for rotating machinery. The fault simulator for an acquired vibration signal is a rotor-kit based test rig with a simulated blade rubbing fault mode test device. Feature selection based on GA (Genetic Algorithms) one of the feature selection algorithm is selected. Then, through the Support Vector Machine, one of machine learning, feature classification is evaluated. The results of the performance of the GA compared with the PCA (Principle Component Analysis) for reducing dimension are presented. Therefore, through data learning, several main faults of the gas turbine are evaluated by fault classification using the SVM (Support Vector Machine).
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