Wire arc additive manufacturing (WAAM) is capable of rapidly depositing metal materials thus facilitating the fabrication of large-shape metal components. However, due to the multi-process-variability in the WAAM process, the deposited shape (bead width, height, depth of penetration) is difficult to predict and control within the desired level. Ultimately, the overall build will not achieve a near-net shape and will further hinder the part from performing its functionality without post-processing. Previous research primarily utilizes data analytical models (e.g., regression model, artificial neural network (ANN)) to forwardly predict the deposition width and height variation based on single or cross-linked process variables. However, these methods cannot effectively determine the optimal printable zone based on the desired deposition shape due to the inability to inversely deduce from these data analytical models. Additionally, the process variables are intercorrelated, and the bead width, height, and depth of penetration are highly codependent. Therefore, existing analysis cannot grant a reliable prediction model that allows the deposition (bead width, height, and penetration height) to remain within the desired level. This paper presents a novel machine learning framework for quantitatively analyzing the correlated relationship between the process parameters and deposition shape, thus providing an optimal process parameter selection to control the final deposition geometry. The proposed machine learning framework can systematically and quantitatively predict the deposition shape rather than just qualitatively as with other existing machine learning methods. The prediction model can also present the complex process-quality relations, and the determination of the deposition quality can guide the WAAM to be more prognostic and reliable. The correctness and effectiveness of the proposed quantitative process-quality analysis will be validated through experiments.

Cold chain logistics possesses unique characteristics, particularly the necessity to maintain low temperatures within containers throughout the distribution process. Real-world traffic conditions, such as congestion, significantly impact the efficiency of cold chain logistics and contribute to increased carbon emissions. To foster green and sustainable development in this sector, a carbon emission trading mechanism has been established, incentivizing companies to invest in energy conservation and emission reduction through economic transactions. This study introduces a multi-objective optimization model for route planning in port seafood logistics, integrating considerations of traffic congestion and zero-carbon transportation. To accurately reflect real-world traffic conditions, a time-dependent function is utilized to model traffic congestion within actual road networks. The road segments are divided, and the travel time for vehicles in each segment is computed. Additionally, the costs associated with the distribution process are analyzed, leading to the development of a multi-objective optimization model aimed at minimizing both distribution costs and zero-carbon transportation costs. The proposed model demonstrates significant economic savings and environmental advantages, providing a theoretical foundation for decision-making processes that support the green and sustainable development of port seafood logistics.

As an important part of the navigation facilities for water conservancy and electricity in Gezhouba, the operation safety of herringbone gates is critical. Due to the minor torsional stiffness of the gate, it is easy to produce torsional deformation during operating under the water pressure, wind load, and gravity, which may lead to fatigue damage. In this study, a gate model with a combination of plate unit and the solid unit was developed, taking a ship lock herringbone gate as an example. According to the gate load under different working conditions, such as self-weight, surge, etc., in this research, we used the finite element analysis software ANSYS to analyze and calculate the stress and strain of the gate, with and without a back tie, and obtained the characteristics of the gate torsional deformation under various working conditions. The results show that the gate’s deformation degree and the direction under different working conditions vary greatly. The maximum deformation point mostly appears in the upper or lower corners of the oblique joint column. The gate deformation can be significantly reduced by adding the back tie. The research results provide a theoretical basis for further optimizing the design of the gate and installation of the back tie to reduce the fatigue damage of the miter gate.