Journal of advanced manufacturing and processing最新文献

In modern communication systems, antennas perform a pivotal role, serving as indispensable components across a diverse spectrum of applications, from underground and maritime communications to aerospace and military operations. The advent of additive manufacturing has brought significant changes to antenna fabrication, with techniques namely Fused Deposition Modeling (FDM), Stereolithography (SLA), and Direct Metal Laser Sintering (DMLS) at the forefront of producing lightweight yet robust and mechanically resilient structures. This comprehensive review elucidates the multifaceted classifications of antennas, delineating their mechanical designs and application-specific frequency bands, whereas offering a nuanced comparative analysis of antennas fabricated using an array of materials, with particular emphasis on filament composition, operational frequency, and maximum realized gain. The study underscores the criticality of conductive coatings on dielectric filaments in achieving optimal radiation performance, thereby aligning additively manufactured antennas with the efficiency of their traditionally fabricated counterparts. Although the conclusions underscore the considerable potential of 3D printing in advancing antenna technology, they also acknowledge the necessity for continued research to overcome existing challenges and fully capitalize on the benefits of this innovative manufacturing method.

Millions of metric tons of textiles are landfilled or incinerated each year in the United States, with less than 1% of textiles recycled into new clothing or fabrics. To counter this trend, a growing number of companies and researchers are exploring how a circular economy can be applied to support textile-to-textile recycling. A significant barrier they face comes down to quickly and efficiently extracting pure feedstock material from post-consumer garments that feature a mix of natural and synthetic fibers. Textile recyclers prefer pure feedstocks, as working with mixed sources typically means lower throughput, higher risk of equipment failure, and diminished business margins. To facilitate a circular economy for textiles, methods, and technologies are needed that can efficiently separate out materials and contaminants from end-of-life textiles to increase the flow of pure feedstocks to recyclers. This paper summarizes findings from interviews with a cross section of textile recyclers and from a review of literature to define basic feedstock requirements. In addition to our qualitative research, we deconstruct a bale of post-consumer textiles and analyze them using computer-vision imaging, Fourier transform infrared spectroscopy (FTIR), and machine learning. The resulting data are used to set system-level design inputs for an automated contaminant removal system to process post-consumer clothing into appropriate feedstocks for recycling. To set the system's levels for automated real-time near-infrared analysis, we identify the minimum percentage of primary material that any single garment in a load of used clothing must contain for the average of the full output stream to meet the target purity levels of recyclers. The envisioned automated system can also address undesirable trace materials that might contaminate the processed stream by using imaging cameras coupled with artificial intelligence to identify sections of clothing for de-trimming. Proof-of-concept machine learning algorithms are evaluated to locate and identify trims or garment areas with hidden contaminant materials. Integrating these methods into automated textile cutting systems can provide a cost-effective means for increasing feedstock purity from used clothing, which can advance circularity for textiles by helping recyclers to reach production volumes and quality targets that were not possible solely with manual dismantling operations.

Gas stripping is an effective means to remove dissolved gases from liquids. To address the drawbacks of previous technologies, a unique planar bubbly cyclone (PBC) for continuous gas–liquid mass transfer is designed. This PBC meets the requirements of gas stripping, including a lower gas-to-liquid volumetric flow rate. A swirling flow and liquid backflow are formed under the constraints of the guide vanes and the cylinder wall. The population balance model accurately predicts the bubble diameter (1 to 6 mm), while the Kawase correlation gives excellent results and is in good agreement with the experimental data (±0.001 m/s). The accurate estimation, based on the assumption of gas plug flow and a well-mixed liquid flow, gives volumetric mass transfer coefficients of 0.15–0.95 s⁻¹ when the gas–liquid ratio was 0.01–1. Overall, the PBC provides an alternative approach to gas stripping, exhibiting a low gas–liquid ratio, long-term capabilities, and good reliability.

This paper introduces the distillation system utilizing the structured packing named PACK-13C, which is used to remove the krypton from commercially available xenon for PandaX-II dark matter detection experiment in China. Experimental results demonstrate that the purified krypton concentration reached $��2�\times �{10}^{�-�11�}��\mathrm{mol}�/�\mathrm{mol}�$ from $��3�\times �{10}^{�-�9�}��\mathrm{mol}�/�\mathrm{mol}�$ during the total reflux distillation process. Hence, a computational fluid dynamics (CFD) model incorporating multiphysics coupling is constructed, and the mass transfer coefficient is calculated based on the Delft model to investigate the mass transfer and gas–liquid separation process at the structured packing for extremely low impurity concentration of less than $��{10}^{�-�9�}�$ mol/mol during the cryogenic distillation. The simulation results agree with the experimental data, with a minimal deviation of merely 0.12%. The structural optimization results demonstrate that the aperture diameter and peak height of the packing significantly influence the mass transfer efficiency. Furthermore, it is found that increasing the wire mesh thickness from 0.3 to 0.5 mm reduces the mass transfer efficiency by 42.5%. The simulation and optimization results highlight the improvement in the efficiency of cryogenic distillation in producing ultra-high purity gas.

Products are often discarded when they fail or no longer meet user needs. These outcomes are frequently shaped by early design decisions. While remanufacturing offers a sustainable alternative by restoring products to like-new condition, its potential is often limited by designs that do not consider remanufacturing from the outset. This research addresses that challenge by introducing a structured Design for Remanufacturing (DfRem) methodology and a CAD-integrated tool to support real-time design decisions. The DfRem framework introduces a new primary design function focused on preserving product functionality across its life cycle. It is supported by a fault tree that identifies failure modes that limit remanufacturing potential and a hierarchy of design principles including Prevent, Minimize, Relocate, Restore, and others. Each principle is linked to actionable design rules that help engineers reduce the need for remanufacturing or improve its efficiency when necessary. To operationalize this framework, we developed CAD plugins for Autodesk Inventor and PTC Creo. These tools use a state machine model to present prioritized design rules based on selected failure modes and user input. By embedding DfRem logic directly into widely used CAD environments, the tool enables engineers to make sustainability-informed decisions without disrupting existing workflows. This approach highlights the critical role of design in enabling circular and resource-efficient product development, making remanufacturing a more practical and accessible strategy during the early stages of product design.

The U.S. automotive tire industry consumes more than 3.6 million tons of materials to manufacture 300 million tires annually. To ensure materials-related sustainability, it is important to enable the use of a greater recycling rate of steel scrap in the manufacturing of new tires. However, impurities like copper compromise the performance of steel wires that are cold-drawn from ~5.5 mm diameter rods. This article investigates copper precipitation in sensitized steel wire rods and their mechanical properties subjected to varying heat treatment procedures. The amount of copper precipitated in sensitized zones as a function of Cu concentration in the steel samples is quantified using scanning electron microscopy. Preliminary findings indicate that an increase in cooling rate during the heat treatment procedure increases the amount of Cu precipitated in the sensitized zones and increases the geometric footprint of the sensitized zones. Tensile tests on heat-treated 5 mm-diameter wire rods showed a higher strain and slightly lower peak stress as the cooling rate decreased. This research thus contributes to a better understanding of the influence of Cu concentration on the microstructure and performance of steel wires used in tire manufacturing, thereby allowing for the enhanced use of scrap steel.

Remanufacturing supply chains are complex due to the circular and interconnected nature of demand and supply. Good demand forecasts are critical for remanufacturers to optimize inventory (of cores, components and finished products) and production planning. Inventory shortages lead to lost sales, delayed fulfillment, or expensive substitutions with new parts, while excess inventory ties up working capital. We collaborated with a large industrial products manufacturer to improve demand forecasting for remanufactured parts requiring periodic replacement. The manufacturer's large global install base of products requires parts replacements at stipulated intervals as part of maintenance. However, the demand tends to be highly variable. We have developed an analytics-based approach to model this variability by considering equipment usage and customer behavior. For installed products, the duty cycles and hence the run-time vary considerably over time. We analyze historical run-time data from products in the field, modeling it as time-series and applying several time-series forecasting techniques to predict future usage more accurately. Additionally, we found that customers do not adhere to the stipulated replacement intervals. To account for such deviation, we characterized individual customer's replacement behavior. By combining each product unit's forecasted run-time with the customer's replacement pattern, we can more accurately predict the upcoming parts replacements. By aggregating forecasts across product units and regions, we generate insights into future demand at headquarters, regional, and dealer levels. Our approach enables manufacturers to optimize inventory across their entire network, streamline production processes, minimize operational costs, and enhance customer service by ensuring timely availability of replacement parts.