This research presents a control structure for an omni-wheel mobile robot (OWMR). The control structure includes the path planning module and the motion control module. In order to secure the robustness and fast control performance required in the operating environment of OWMR, a bio-inspired control method, brain limbic system (BLS)-based control, was applied. Based on the derived OWMR kinematic model, a motion controller was designed. Additionally, an optimal path planning module is suggested by combining the advantages of A* algorithm and the fuzzy analytic hierarchy process (FAHP). In order to verify the performance of the proposed motion control strategy and path planning algorithm, numerical simulations were conducted. Through a point-to-point movement task, circular path tracking task, and randomly moving target tracking task, it was confirmed that the suggesting motion controller is superior to the existing controllers, such as PID. In addition, A*–FAHP was applied to the OWMR to verify the performance of the proposed path planning algorithm, and it was simulated based on the static warehouse environment, dynamic warehouse environment, and autonomous ballet parking scenarios. The simulation results demonstrated that the proposed algorithm generates the optimal path in a short time without collision with stop and moving obstacles.

This research presents the effect of the thermal boundary condition on the tilting pad journal bearing characteristics. The thermal boundary condition includes the temperature around the bearing pad, spinning journal, and lubricant supply temperature. Change in bearing performance according to the temperature around each element constituting the bearing was analyzed without paying attention to how the actual thermal boundary conditions around the bearing are configured. High fidelity numerical model of tilting pad journal bearing is presented for (1) the analysis of heat generation in the thin film, (2) heat transfer in the lubricant, (3) heat flux flowing into the journal and pad, (4) temperature change in the journal and bearing, (5) the resultant thermal deformation, (6) change in the lubricant film thickness arising from the thermal deformation of journal and bearing pads, and (7) the resulting change in the heat generation in the thin film. To reach the steady state of the bearing–journal system, the Runge–Kutta scheme with adaptive time step is adopted where the dynamic and thermal system are solved simultaneously in multi-physics model. Performance change of the bearing according to three changes: (a) boundary temperature around shaft, (b) boundary temperature around bearing pads, and (c) lubricant supply temperature were investigated.

Accurate health diagnostics of lithium-ion batteries are critical for ensuring safe, reliable, and prolonged battery operation. This study presents a data-driven approach to estimating electrode-level state-of-health (eSOH) using a Deep Neural Network (DNN), enabling the assessment of the loss of active material (LAM) in both electrodes and the loss of lithium inventory (LLI). To construct the DNN models, essential features are extracted from the differential voltage and incremental capacity analyses of the open-circuit voltage (OCV), derived from a mechanistic model for an NMC/Graphite battery. The DNNs are trained and tested using various hyperparameters, leveraging the OCV data. Specifically, accuracy and robustness are considered in the model selection and evaluation process to balance precision and generalization ability. Additionally, feature reduction is performed considering three aspects: the number of features, robustness to noise, and the SOC window for data acquisition. Based on the analysis, three DNN models with reduced futures are meticulously chosen and extensively evaluated against the baseline all(12)-feature model, demonstrating their performance. Lastly, a correction method utilizing the entropy change of the electrodes is proposed to improve the estimation accuracy in the presence of temperature variations from the training phase.

A study utilizing convolutional neural networks (CNN) has been conducted to detect and classify invisible debonding-type defects in reinforced structures. Training data for these defects is collected from the finite element models of honeycomb sandwich panels and skin-stringer systems, commonly employed reinforcement structures in aerospace applications. The excitation frequency is determined based on the amplitude of the reflected wave from the defect, and the optimal sensor array is selected. The constructed two-dimensional training image, created by vertically stacking the measured responses in the time domain, exhibits high classification performance even with a shallow neural network. The neural network undergoes optimization through adjustment to the kernel parameters and initial learning rate. To assess the general performance of the training model, k-fold cross-validation is employed. The CNN-based non-destructive evaluation algorithm demonstrates high classification performance for debonding defects in honeycomb sandwich panels and skin-stringer structures. Moreover, the suggested algorithm is robust against noise, emphasizing its effectiveness in real-world applications.

In this paper, for the purpose of increasing the wafer yield by controlling the non-uniformity of the material removal rate during the chemical mechanical polishing process, the influence of the cross-sectional shape of the metal-inserted retainer ring and the pressure distribution on the wafer and the retainer ring generated from the multi-zone carrier head are investigated. First, in order to verify the finite element analysis model, it is correlated using the test data. By using a validated finite element model, simulation studies involving several parameters are performed to reduce the irregularity in the wafer: (1) tapered bottom of the retainer ring, (2) machining round corners at the bottom of the retainer ring, (3) the changes in pressure applied to the wafer, (4) the changes in pressure applied to the retainer ring.

The objective of this research is to recommend practical noise control schemes for Hanwha Techwin’s multi-stage, centrifugal air compressors. For purpose of achieving this objective, far- and near-field noise measurements, sound intensity measurements, and structural vibration measurements were performed first to identify the acoustic characteristics of a compressor. Through these measurements, it was shown that the most dominant noise and vibration components were at the blade passing frequencies (BPFs) of the compression stage impellers. Thus, by suppressing these tonal BPF noise components, the overall noise level was reduced effectively. Here, Helmholtz resonators, dynamic dampers, noise insulation materials, and an enclosure were recommended to reduce these tonal noise components. The Helmholtz resonators could be used to reduce the overall SPL up to 3.5 dBA. By applying the mass dampers, 2.7 dBA reduction at 7 kHz and 1.5 dBA reduction at 10 kHz could be achieved. The installation of the sound insulation material could achieve 8 dBA overall SPL reduction. By installing the enclosure around the compressor, 4.3 dB reduction in the overall SPL could be achieved. To achieve a higher noise reduction using the enclosure, the size of leakage holes between the compressor pipe and the enclosure is recommended to be reduced.