Optimizing the Mean and Variance of Bead Geometry in the Wire + Arc Additive Manufacturing Using a Desirability Function Method

Wire + arc additive manufacturing (WAAM) is an arc welding process that uses non-consumable tungsten electrodes to produce the weld. The material used in this study is a titanium, carbon, zirconium, and molybdenum (TZM) alloy that is physically and chemically stable and has good performance for use as a welding and high-temperature heating element. However, the price is higher than that of other materials. Because welding cannot be modified after manufacturing, economic losses are high in the case of a defective product. Therefore, it is important to find the best welding settings for the target bead geometry during welding. In this study, welding experiments are designed based on a central composite design, and single-layer WAAM is performed using a TZM material. Consequently, we obtain 17 beads and measure the height, width, as well as left and right toe angles, which represent the geometry of the beads. Based on the measured geometry, we obtain the optimal settings for the WAAM parameters whereat the mean of each geometry is close to its target value and its variance is minimized by using a desirability function method. Furthermore, we conduct additional experiments to validate the optimal settings that we obtain. We compare the predicted and actual geometry values and find that they are quite close. This result indicates that valid optimal settings for the process parameters can be obtained via the proposed method.

Explainable U-Net model forMedical Image Segmentation

In this nutshell, we propose a simple, efficient, and explainable deep learning-based U-Net algorithm for the MedAI challenge, focusing on precise segmentation of polyp and instrument and transparency on algorithms. We develop a straightforward encoder-decoder-based algorithm for the task above. We make an effort to make a simple network as much as possible. Specially, we focus on input resolution and width of the model to find the best optimal settings for the network. We perform ablation studies to cover this aspect.

Optimal Estimate of Global Biome—Specific Parameter Settings to Reconstruct NDVI Time Series with the Harmonic ANalysis of Time Series (HANTS) Method

Terrestrial remote sensing data products retrieved from radiometric measurements in the optical and thermal infrared spectrum such as vegetation spectral indices can be heavily contaminated by atmospheric conditions, including cloud and aerosol layers. This contamination results in gaps or noisy observations. The harmonic analysis of time series (HANTS) has been widely used for time series reconstruction of remote sensing imagery in recent decades. To use HANTS model, a series of parameters, such as number of frequencies (NF), fitting error tolerance (FET), degree of over-determinedness (DoD), and regularization factor (Delta), need to be defined by users. These parameters provide flexibilities, but also make it difficult for non-expert users to determine appropriate settings for specific applications. This study systematically evaluated the reconstruction performance of the model under different parameter setting scenarios by simulating pixel-wise reference and noisy NDVI time series. The results of these numerical experiments were further used to identify optimal settings and improve global NDVI reconstruction performance. The results suggested optimal settings for different areas (local optimization). If a user opts to use unique settings for global reconstruction, the setting NF = 4, FET = 0.05, DoD = 5, and Delta = 0.5 can produce the best performance across all setting scenarios (global optimization). In addition, several internal improvements, such as dynamic weighting scheme, polynomial and inter-annual harmonic components, and ancillary attributes of input data can be used to further improve the performance of reconstruction. With these results, future non-expert users can easily determine appropriate settings of HANTS for specific applications in different regions.

Optimal setting of DOC relay in distribution system in presence of D-FACTS

The process of selecting optimal settings for directional over-current relays (DOC relays) is a selection of time dial setting (TDS) and IP (backup current), So that changes in the system of electrical power distribution. In this work, a breeder genetic algorithm (BGA) has been applied to optimal settings of DOC relays with multisystem D-FACTS devices. The simulation consists of two network operation scenarios, scenario without D-FACTS which consisting of coordination of DOC relays against three phase faults, and the second scenarios with multi TCSC. In general, had been verified on optimal settings of relays that the impacts of TCSC insertion in 33-bus distribution system on DOC relays.