In the parallel flushing circuit for large-scale hydraulic motor testing, increasing flushing flow rate raises total pressure loss and casing pressure differential, while reducing pipeline pressure loss alone increases casing pressure. To solve this strongly conflicting engineering problem, this paper proposes an optimization method combining Maximin Latin Hypercube Sampling, Kriging surrogate model and NSGA-II algorithm. Specifically, five core design variables (pipeline lengths, elbow quantities, and pump flow) are systematically tuned to minimize casing pressure difference, minimize total pressure loss, and maximize flushing flow. The TOPSIS method is further applied to derive optimal solutions under different design preferences. The method was verified via AMESim simulation and leave-one-out cross-validation, achieving 0.8%–10.1% optimization for each target, and provides a valuable engineering reference for multi-objective optimization of similar hydraulic circuits. In conclusion, the proposed multi-objective optimization framework successfully resolves the inherent physical contradiction between casing heat dissipation and mechanical seal safety. It proves that combining MLHS, Kriging surrogate models, and NSGA-II is a highly robust approach for large-scale hydraulic test bench design.
This paper presents an investigation of condensation rate on surfaces with hydrophilic and hydrophobic patterns, inspired by the desert toktokkie beetle spot pattern and the leaf vein skeleton pattern. The objective of this paper is to compare the condensation rate of surfaces inspired by the desert toktokkie beetle spot pattern and the leaf vein skeleton pattern. Five types of surfaces, which were the desert toktokkie beetle spot pattern, leaf vein skeleton pattern, untreated copper, PVC-coated, and laser-etched surfaces, were prepared. The measured contact angles of a water droplet on the untreated copper, PVC-coated, and laser-etched surfaces were 62.21°,101.99°, and 54.75°, respectively. The condensation rates of water vapour on the five surface types were subsequently measured through experiments. The heat released from condensation was measured using a thermoelectric module. The rate of cooling power of the thermoelectric module is proportional to the condensation rate. The condensation rate of the surface with the desert toktokkie beetle spot pattern is 22.9% higher than that of the untreated copper surface. However, the condensation rate of the surface with the leaf vein skeleton pattern is only 9.53% higher than that of the untreated copper surface. The desert toktokkie beetle spot pattern induces the coalescence of droplets, which enhances the removal of condensate from the surface. The desert toktokkie beetle spot pattern also reduces the fluctuations in condensation rate by half, compared to the untreated copper surface. With appropriate adoption of features from nature, there is potential to reduce the size of a condenser.
Guided by the principles of Industry 4.0, the integration of artificial intelligence (AI), convolution neural networks into the lithium-ion battery manufacturing process for analysis and prediction is a significant advancement in the development of Quality 4.0. This review examines the methods that use AI to predict the state of health and state of charge of lithium-ion batteries. First, an overview of Industry 4.0, lithium-ion batteries, artificial intelligence, and convolutional neural networks is provided, followed by the discussion of key challenges in this field. Subsequently, these challenges are refined and the research objectives of this study are summarized. A matrix analysis of the relevant literature across six dimensions is conducted to identify areas of research that have the greatest value and significance to lithium-ion battery manufacturing. Furthermore, the process of lithium-ion battery manufacturing is introduced, the key steps and critical nodes involved are summarized, and digital and artificial intelligence technologies currently used in the production of lithium-ion batteries are reviewed. Finally, insights are given by responding to the refined research questions on the basis of a comprehensive synthesis of the literature to guide future research and innovation in the field of lithium-ion battery manufacturing and contribute to the high-quality and sustainable development of this field. In total, 200 representative articles published between 2004 and 2025 are analyzed in a 6×6 matrix of research objectives and methods, with approximately 50% of scholars adopting an optimistic view of AI integration and around 25% expressing concerns about its early-stage limitations.
Dry and cryogenic machining are green machining methods that offer environmental sustainability. Dry machining eliminates any use of cutting fluids, while cryogenic machining uses very low machining temperatures. The current implementation method (cutting fluid assistance) exposes machine operators to health risks and negatively impacts the environment through waste disposal. Therefore, to overcome this problem, sustainable alternatives such as dry and cryogenic machining are required. However, both methods pose significant challenges due to increased wear and heat generation (overheating and overcooling). This study aimed to evaluate and differentiate the machining performance of dry and cryogenic methods while observing the tool wear mechanism. The methodology used was through: cutting speed (80 m/min), depth of cut (0.5 mm), feed rate (0.05 mm/tooth), and various radial depths of cut (5, 7.5, 10, and 20 mm). At the same time, the wear mechanism was observed through scanning electron microscopy (SEM). The study demonstrated that cryogenic machining significantly outperformed dry machining, consistently delivering 50% to 100% longer cutting lengths under aggressive cutting conditions and achieving a maximum tool-life extension of 42% at a radial depth of 5 mm. In contrast, dry conditions led to severe thermochemical wear mechanisms, including diffusion, oxidation, and adhesive wear, whereas cryogenic conditions primarily exhibited chipping and catastrophic failure modes. Based on studies, cryogenic machining is better than dry machining because it can significantly extend cutting tool life and reduce severe tool wear.
Reliable joining of copper alloys and thermoplastic polymers remains a major challenge because of the substantial differences in their thermal and physical properties, and studies on the direct joining of copper alloy C23000 to polyethylene (PE) using pinless friction spot welding (FSW) remain limited. Using FSW with a pinless cylindrical tool, this study investigated a sustainable approach for producing hybrid metal–polymer joints by joining copper alloy C23000 with PE. Tensile shear testing was used to evaluate joint performance after systematically varying the process parameters, including tool rotational speed (1100, 1400, and 1800 rpm) and welding time (1, 1.5, and 2 min). According to the design of experiments (DOE) analysis, the maximum tensile shear strength of 3256 N was achieved at 1400 rpm and 2 min. Due to thermal degradation of the polymer, an excessive rotational speed (1800 rpm) reduced the joint strength. At the copper–PE interface, scanning electron microscopy (SEM) revealed a well-formed mechanical interlock with an interfacial width of 19–24 μm, indicating good interfacial continuity and a low density of observable defects. The results indicate that pinless friction stir welding is an effective, environmentally friendly solid-state joining technique for producing reliable metal–polymer hybrid joints without additional adhesives or mechanical fasteners. In conclusion, the developed joints demonstrate significant potential for lightweight applications in the automotive, aerospace, and electronics industries, where mechanical reliability, recyclability, and weight reduction are critical requirements.
