Seamless Production Research Articles

Path Planning Algorithm Analysis of Multiple AGV

Intelligent logistics is an important part of intelligent manufacturing, and Automated Guided vehicle (AGV), as a part of seamless enterprise production system and storage system, is an important technical equipment to achieve intelligent logistics, and has been widely used in factories, warehousing and logistics environment such as automatic handling scenarios. Since a single AGV in the same site can no longer meet the trend of automated factory, it is important to effectively schedule and plan paths for multiple AGVs in limited space. Aiming at multi-AGV system, this paper analyzes and organizes the common path planning algorithms in the market, in order to improve the effectiveness and safety of AGVs. The A* algorithm and the ant colony algorithm's principles will be analyzed in the first part of the paper. Then describe the research status of the two respectively and summarize the advantages and defects respectively. Finally, prospect the development progress of AGV coordinated scheduling and path planning.

DESIGN AND IMPLEMENTATION OF A SUPERMARKET MANAGEMENT SYSTEM USING UML FOR STREAMLINED INVENTORY, SALES, AND CUSTOMER MANAGEMENT

The growing demands on supermarket management have necessitated the adoption of robust systems to streamline operations such as inventory control, sales tracking, and customer management. This study introduces a UML-based design for a supermarket management system that addresses the core needs of store administration through structured modules and defined workflows. The system encompasses store registration, inventory management, sales tracking, and customer engagement, further facilitating seamless product browsing, checkout, and post-sale support. Through structured modeling, the proposed system simplifies process flows, thereby enhancing the operational efficiency and user experience in a supermarket environment. This paper details the UML diagrams used, outlines the functional processes, and discusses the potential benefits of the system for modern retail management.

Satellite retrieval of oceanic particulate organic carbon: Towards an accurate and seamless dataset for the global ocean

Particulate organic carbon (POC) plays crucial roles in the global ocean carbon cycle and the oceanic biological pump. Satellite remote sensing has been demonstrated to be an effective technique for the retrieval of surface oceanic POC concentration. However, the complex spatiotemporal variations of the relationships between POC and oceanic optical properties across different waters posed challenges for accurate retrieval of POC concentration from satellite observations. Additionally, interference factors, such as cloud cover and sun glint, resulted in severe data missing problems and impeding daily coverage of the global ocean. With an attempt to generate accurate, seamless and readily available POC products for the global ocean, this study aimed to develop accurate satellite POC retrieval models for the Moderate Resolution Imaging Spectroradiometer (MODIS) data from both Terra and Aqua satellites, and to explore the possibility of using the empirical orthogonal function interpolation technique (DINEOF) to reconstruct satellite-retrieved POC data to generate gap-free global oceanic POC products. Results showed that the eXtreme Gradient Boosting (XGBoost) method could accurately retrieve POC with R2 approximately 0.80 and RMSE about 0.20 in log10 scale, obviously outperforming the operational blue-to-green band ratio algorithm and the hybrid polynomial algorithm based on two multi-band indices; and the DINEOF method, which could reconstruct approximately 88 % missing pixels for the global ocean, contributed to better revealing the global oceanic POC variations at a daily scale than the satellite-retrieved POC products. Based on the developed models, a suit of long time-series accurate and seamless POC products of the global surface ocean were generated, which is readily available for other applications and should be helpful to investigate the spatiotemporal variations of POC concentrations over global ocean and its roles in the global carbon cycle. The generated seamless products are openly accessible via the DOIs listed in the data availability section.

Alternatives to Reduce Hot Cracking Susceptibility of IN718 Casting Alloy Laser Beam Welds with a Mushroom Shape

Reducing hot cracking is essential for ensuring seamless production of nickel superalloys, which are extensively used in welded structures for aircraft engines. The prevalence of hot cracking in precipitation-strengthened alloy 718 is primarily governed by two factors: firstly, the chemical composition and the coarse microstructure formed during solidification, and secondly, the activation of hot cracking mechanisms, which is particularly critical in mushroom-shaped welding morphologies. In this study, different nickel-based superalloys welded using laser beam welding (LBW), more specifically bead on plate welding (BoP), specimens are compared. The cracking susceptibility of both wrought and two investment casting 718 alloys with tailored chemical compositions is examined through the application of both continuous and pulsed LBW. Additionally, various pre-weld treatments, including with and without Pre-HIP (hot isostatic pressing), are analyzed. The influences of chemical composition, LBW parameters and pre- and post-welding treatments on both internal and external cracks determined by conventional and advanced non-destructive tests are studied. A clear reduction of hot cracking susceptibility and overall welding quality improvement was observed in a tailored 718 alloy with relatively high Ni (55.6% wt) and Co (1.11% wt) contents.

Evaluation of global seamless soil moisture products over China: A perspective of soil moisture sensitivity to precipitation

Accurate and timely information about soil moisture (SM) is not only critical to the study of climate change, but is also significant for agricultural production, drought monitoring, and hydrometeorological predictions. With the development of satellite remote sensing techniques, land surface models, and data assimilation techniques, a number of global seamless soil moisture products have been released with highly variable accuracy. A comprehensive evaluation of these products is necessary to select a suitable SM product for use. In this study, we provided such an evaluation of four global seamless SM products (SMAP-L4, ERA5-Land, GLDAS-Noah, and GLEAM) over China based on the stations from the automatic soil moisture observation (ASMO) system. Compared to previous studies that focused on a direct comparison between SM products and in situ SM observations, we specifically evaluated these products from the perspective of SM sensitivity to precipitation. This is because in situ precipitation observations are more spatially representative than in situ SM observations. Precipitation is a regional event, therefore in situ precipitation observations can be better scale-matched with coarse resolution SM products compared to in situ SM observations. Further, in addition to surface soil moisture (SSM), root zone soil moisture (RZSM) was evaluated in this study. The combination of all statistics illustrates that SMAP-L4 and ERA5-Land generally performed better than GLDAS-Noah and GLEAM. Regardless of the SSM or RZSM estimates, SMAP-L4 had the lowest median bias and RMSE of the four products, and ERA5-Land had the lowest median correlation coefficient (R) value. The lowest median ubRMSE was obtained by SMAP-L4 in SSM evaluation and ERA5-Land performed better than other RZSM products with the lowest median ubRMSE. From the perspective of SM sensitivity to precipitation, SMAP-L4 can capture daily precipitation dynamics most effectively, closely followed by ERA5-Land. This partially explains the superiority of SMAP-L4 and ERA5-Land over GLDAS-Noah and GLEAM for SM monitoring. The results of the metrics under different precipitation conditions varied considerably among the products. But while considering of ubRMSE alone, all products performed better under arid and humid conditions than to semi-arid and semi-humid conditions. This may be due to the fact that SM products had a limited ability to capture the SM dynamics under moderate precipitation. These findings may provide new insights into the selection of appropriate SM products and SM estimation in the future.

Agronomic Advances and Challenges of Chia Production in Kenya: A Review

Chia (Salvia hispanica L.) is an annual tropical plant belonging to the Lamiaceae family. In Kenya, Chia has gained significant attention in recent years due to its nutritional benefits and increasing demand in the local and global market. This review aims to explore the potential of chia production in Kenya as a source of income for farmers. The review highlights the agronomic requirements of chia, including soil, water, and climate conditions, as well as practices necessary for successful cultivation. Chia production is becoming a favorite enterprise for most farmers in Kenya due to its ease of management and short growth period. However, a seamless production has been encountered by several challenges, including scarce information regarding its agronomic management practices, poor yielding varieties, emerging pests and diseases, and poor harvesting and post-harvesting techniques. The growing demand for chia globally presents an opportunity for Kenyan farmers to tap into this market and generate income through export. Chia, has the potential to become a new golden crop for Kenyan farmers, contributing to food security, poverty reduction, and sustainable development. However, to realize this potential, there is a need for investment in research, extension services, and market linkages to support chia production, improve agronomic practices, and create a sustainable market for the crop.

Monitoring Methane Concentrations with High Spatial Resolution over China by Using Random Forest Model

Atmospheric methane is one of the major greenhouse gases with a drastic impact on climate change. This study developed a random forest model to obtain a daily 5 km resolution atmospheric methane concentration dataset with full spatial coverage (100%) from 2019 to 2021 in mainland China, thereby filling the gap in the methane product data from the Tropospheric Monitoring Instrument (TROPOMI). The coefficients of determination for a sample-based and spatial-based cross-validation are 0.97 and 0.93, respectively. The average deviation of the seamless methane product reconstructed by the random forest model is less than 1%, validated with the measured methane concentration data from the Total Carbon Column Observing Network sites. Methane concentrations in China show a distribution of high in the east and south and low in the west and north. The high-concentration areas include Central China, the Sichuan Basin, the Pearl River Delta, and the Yangtze River Delta. In terms of time scale, the methane concentration has evident seasonal variation, as it is low in spring (average 1852 ppb) and winter (average 1881 ppb) and high in summer (average 1885 ppb) and autumn (average 1886 ppb). This is mainly due to the significant increase in emissions from rice cultivation and wetlands during the summer and autumn. During the COVID-19 pandemic, the methane concentration decreases significantly and then starts to return to normal around 70 days after the Lunar New Year, indicating that the seamless methane product can potentially detect anomalous changes in methane concentration.

DIGITAL BOUNCER

Digital Bouncer presents an innovative solution, amalgamating an integrated platform for streamlined mass messaging and e-commerce capabilities. This comprehensive platform empowers users with efficient message dissemination to diverse audiences, comprising two pivotal components: a feature-rich e-commerce website and specialized software for message/email dissemination. The user-friendly website interface facilitates tailored messaging preferences and seamless product purchases. Key features include "Number Harbour," facilitating access to a large volume of numbers pertinent to specific location-based requirements following with dissemination of message according to user preference. Additionally, the integration of Telegram Automator and WhatsApp Automator software enriches communication avenues. Furthermore, the provision of a secure payment gateway enables hassle-free transactions, elevating Digital Bouncer as an all-encompassing solution catering to large-scale communication and e-commerce needs. Index Terms - Integrated Platform, Mass Messaging, E-commerce, User Empowerment, Tailored Messaging, Product Purchases, Number Harbour , Automatic Replies, Telegram Automator, WhatsApp Automator, Payment Gateway, Secure Transactions.

On the Fundamentals of Reverse Ring Rolling: A Numerical Proof of Concept.

Ring Rolling is a near-net manufacturing process with some measurable dimensional inaccuracies in its products. This fact is exaggerated even more under the scope of high-precision manufacturing, where these imprecisions render such products unfitting for the strict dimensional requirements of high-precision applications (e.g., bearings, casings for turbojets, etc.). In order to remedy some of the dimensional inaccuracies of Ring Rolling, the novel approach of Reverse Ring Rolling is proposed and investigated in the current analysis. The conducted research was based on a numerical simulation of a flat Ring Rolling process, previously presented by the authors. Since the final dimensions of the ring from the authors' previous work diverged from those initially expected, the simulation of a subsequent Reverse Ring Rolling process was performed to reach the target dimensions. The calculated deformational results revealed a great agreement in at least two of the three crucial dimensions. Additionally, the evaluation of the calculated stress, strain, temperature and load results revealed key aspects of the mechanisms that occur during the proposed process. Overall, it was concluded that Reverse Ring Rolling can effectively function as a corrective process, which can increase the dimensional accuracy of a seamless ring product with little additional post-processing and cost.

Using Content-Based Filtering and Apriori for Recommendation Systems in a Smart Shopping System

This research is motivated by the increasing significance of online shopping platforms and the challenges faced by users in locating products that align with their preferences and requirements, which can significantly influence the sales performance of online retailers. Consequently, the primary objective of this study is to design and implement a recommendation system capable of identifying suitable products and forecasting the purchase frequency for various product combinations, while also integrating this recommendation system with a smart shopping platform. To achieve this objective, the research employs machine learning techniques, specifically content-based filtering and the Apriori algorithm. Content-based filtering is utilized to analyze user preferences and behavioral patterns related to visited products, while the Apriori algorithm is employed to evaluate support and confidence values for item set combinations, thereby generating frequency values for future transactions involving product combinations. Additionally, a smart shopping system is developed and integrated, enhancing the shopping experience through smartphone applications and streamlining the payment process to facilitate seamless product purchases. The research methodology involves data collection pertaining to products and user preferences, followed by several testing involving a sample group of user respondents. The results demonstrate that the developed recommendation system effectively delivers relevant product recommendations based on user preferences, achieving a confidence value up to 98%. Furthermore, the smart shopping system proves capable of independently assisting users throughout the transaction process, thereby enhancing overall user experience and convenience.

Fixed position varying pore structure of weft-knitted seamless fabric and heat/moisture transfer mechanism

In this work, two functional superfine polyester yarns were selected as face yarns, three conventional yarns were selected as ground yarns, and 18 fabrics were designed by using a combination of three different plating structures. Then, through contact angle measurements, moisture management performance, thermal management performance, and breathability tests, we evaluated the effects of raw material, structure, and pore characteristics on the fabric’s hygrothermal comfort, and furthermore assessed the prioritization of the three factors using a range analysis. The results show that fabrics with face yarn of 75 D/288 F have better hydrophilicity, and fabrics with face yarn of 50 D/216 F have a better temperature control effect. The fabric with spandex/polyester covered yarn or spandex/nylon covered yarn has the best moisture transmission, and the unidirectional transfer index can reach 300%. The microstructure has a great influence on air permeability, and the surface porosity is positively correlated with air permeability. Combined with the heat and humidity division of the sports body, the development of efficient zonal positioning of heat and moisture management sports undershirts can provide certain theoretical support for the design of weft-knitted seamless sports products.

Enhancing conceptual models with computational capabilities: A methodical approach to executable integrative modeling

AbstractThe lack of a common executable modeling framework that integrates systems engineering, software design, and other engineering domains is a major impediment to seamless product development processes. Our research aims to overcome this system‐software modeling gap by integrating computational, software‐related, and model execution capabilities into OPM‐based conceptual modeling, resulting in a holistic unified executable quantitative‐qualitative modeling framework. The gap is overcome via a Methodical Approach to Executable Integrative Modeling—MAXIM, an extension of OPM ISO 19450:2015, a standardization approvement given on 2015. We present the principles of MAXIM and demonstrate its operation within OPCloud—a web‐based collaborative conceptual OPM modeling framework. As a proof‐of‐concept, a model of an Airbus civil aircraft landing gear braking system is constructed and executed. Using MAXIM, engineers from five domains can collaborate at the very early phase of the system development and jointly construct a unified model that fuses qualitative and quantitative aspects of the various disciplines. This case study illustrates an important first step towards satisfying the critical and growing need to integrate systems engineering with software computations into a unified framework that enables a smooth transition from high‐level architecting to detailed, discipline‐oriented design. Such a framework is a key to agile yet robust future development of software‐intensive systems.

SatellStitch: Satellite Imagery-Assisted UAV Image Seamless Stitching for Emergency Response without GCP and GNSS

Rapidly stitching unmanned aerial vehicle (UAV) imagery to produce high-resolution fast-stitch maps is key to UAV emergency mapping. However, common problems such as gaps and ghosting in image stitching remain challenging and directly affect the visual interpretation value of the imagery product. Inspired by the data characteristics of high-precision satellite images with rich access and geographic coordinates, a seamless stitching method is proposed for emergency response without the support of ground control points (CGPs) and global navigation satellite systems (GNSS). This method aims to eliminate stitching traces and solve the problem of stitching error accumulation. Firstly, satellite images are introduced to support image alignment and geographic coordinate acquisition simultaneously using matching relationships. Then a dynamic contour point set is constructed to locate the stitching region and adaptively extract the fused region of interest (FROI). Finally, the gradient weight cost map of the FROI image is computed and the Laplacian pyramid fusion rule is improved to achieve seamless production of the fast-stitch image map with geolocation information. Experimental results indicate that the method is well adapted to two representative sets of UAV images. Compared with the Laplacian pyramid fusion algorithm, the peak signal-to-noise ratio (PSNR) of the image stitching results can be improved by 31.73% on average, and the mutual information (MI) can be improved by 19.98% on average. With no reliance on CGPs or GNSS support, fast-stitch image maps are more robust in harsh environments, making them ideal for emergency mapping and security applications.

Energy Management System Technology in Smart Grid

The smart grid, envisioned as the future of the power industry, aims to revolutionize energy supply with efficiency and sustainability. This paper extensively examines the critical role of Energy Management System (EMS) technology within the smart grid framework. By harnessing advanced technologies like distributed energy storage, intelligent energy management, electric vehicle charging, intelligent energy dispatching, and fault perception diagnosis, the smart grid promises heightened power system efficiency, reliability, and environmental consciousness. In today's dynamic energy landscape, the integration of renewable sources introduces a unique challenge due to their inherent volatility. EMS emerges as the linchpin, offering real-time monitoring and data-driven optimization to ensure grid stability amid fluctuating supply. The smart grid, with advanced information technology and intelligent control strategies, leans heavily on EMS for seamless energy production, transmission, and consumption. However, challenges persist, encompassing stable energy supply, security, infrastructure upgrades, and cross-border standardization. Yet, with sustained technological innovation and collaborative efforts, a future of intelligent, sustainable, and reliable power systems is attainable. In conclusion, EMS integration into the smart grid not only signifies technological progress but also heralds a monumental shift towards a greener, more sustainable energy landscape. This paper provides a comprehensive exploration of how EMS, as the backbone of the smart grid, empowers us to navigate the complexities of modern energy demands, paving the way for a brighter, more efficient future.

Maximizing Sales Efficiency and Revenue with Salesforce CPQ Solutions

Salesforce CPQ is an advanced sales tool that enables companies to deliver accurate and highly configured quotes to their customers. This document aims to elucidate the multifaceted benefits of CPQ Salesforce, discussing how its integration can transform sales processes, enhance efficiency, and substantially increase revenue. It delves into the software’s essential features, such as seamless product configuration, precise pricing mechanisms, and streamlined quoting. Furthermore, the document explores various case studies demonstrating the successful implementation of CPQ Salesforce in diverse business scenarios. By examining how CPQ Salesforce solutions can be leveraged to optimize sales operations, this document serves as an informative guide for organizations considering the adoption of this innovative tool

The Stress Strain Analysis of Forward Extrusion Process of Hollow Elements

This paper presents an analysis focused on the stress-strain dynamics within the forward extrusion process for hollow elements. The forward extrusion of hollow elements is characterized by extensive plastic deformation and complex material flow. This process involves splitting the metal continuum, subjecting it to plastic deformation, and merging it into a seamless product with a predetermined cross-sectional shape as it exits the tool. Employing modern numerical finite element methods is essential for designing such processes, as it enables the simulation of metal behavior within the tool. These simulations help predict mechanical parameters within the deformable material continuum, such as stress, strain, temperatures, and strain rates. Additionally, they allow for the optimization of tool designs tailored to the specific product, improving process efficiency and quality. Understanding the stress-strain distribution at individual nodes enables proactive measures to prevent mechanical disruptions within the continuum, ensuring the integrity and quality of the final product—an essential criterion in the forward extrusion process for hollow elements.

Temporal Gap‐Filling of 12‐Hourly SMAP Soil Moisture Over the CONUS Using Water Balance Budgeting

AbstractTemporal gaps in satellite‐based soil moisture (SM) products are a persistent issue. This study presents an entirely observation‐based method to derive volumetric SM content for filling gaps in Soil Moisture Active Passive (SMAP) retrievals. Using a water balance equation, 12‐hr topsoil water amount variations are determined based on observed precipitation from the Global Precipitation Measurement Mission (inflow) and a hydrologic loss function (outflow) built on SMAP dry‐downs. A temporally seamless SM product, composed of SMAP dry‐downs and precipitation‐driven moisture approximations, was generated as a secondary outcome in determining optimal water balance parameters. This data set maintains the original SMAP SM dynamics with a median Pearson correlation (R) of 0.69 and an unbiased root‐mean‐square error (ubRMSE) of 0.05 m3/m3. Using these parameters and available SMAP observations, a 12‐hourly SM product was produced over the conterminous United States. Validated against in situ measurements, this 12‐hourly SM product exhibits good performance with a median R of 0.63 and captures most SM peaks induced by heavy rainfall. A time series examination revealed the produced 12‐hourly SM product closely corresponds to in situ SM variations and outperforms two other SMAP‐based 12‐hourly SM products gap‐filled using temporal linear interpolation and a three‐dimensional smoothing approach, especially during sparse SMAP data periods. The proposed scheme's validity is further verified by the comparable performance of the exclusive filled‐on SM estimates. Utilizing the 12‐hourly SM data set and its paired hydrologic losses could enhance the quantification connections among the hydrologic components and benefit the understanding of land‐surface hydrology.

Research on plated structure design in weft knitting seamless fabric and sweat management

During exercise, the degree of sweating in different parts of the body varies. The organization of different functions in different parts can be realized using weft knitting seamless technology. The advantage of this technology was that it can be one-piece woven, has high efficiency, offers environmental protection, provides superior wearing comfort, and facilitates the rapid response of sweat and sweat management in different parts. In this work, 9.33 tex/384 F yarn was selected as the face yarn, and 5.55 tex/24 F yarn was selected as the ground yarn, seven fabrics were designed using a combination of three basic plated loops. The effect of two factors, surface porosity and fabric tightness, on the sweat management performance of plated fabrics was investigated through experiments, including moisture management performance, liquid spreading performance, wicking performance, evaporation performance, air permeability and moisture permeability performance, etc. The results showed that the smaller the surface porosity of the fabric, the better the one-way transmission performance of the liquid. However, the larger the surface porosity, the easier the liquid plane diffusion was. The tightness of the yarn filling in the fabric was beneficial for the weft flow of the liquid. A certain degree of tightness was helpful for the unidirectional conduction of fluid, but both too tight and too loose fabrics were not conducive to fluid conduction. Studying the difference in performance caused by the surface porosity and the tightness of the fabric can provide some theoretical help for the design of weft-knit seamless sports products.

Scale-up of antisolvent precipitation process with ultrasonic microreactors: Cavitation patterns, mixing characteristics and application in nanoparticle manufacturing

In this study, a seamless scale-up strategy for the antisolvent precipitation process was developed using a series of ultrasonic microreactors (USMRs). The key principle of this scale-up strategy was to maintain a constant mixing time, ensuring consistent nanoparticle (NP) production across different scales. First, lab- and production-scale USMRs were developed, enabling the customization of reactors to varying ultrasound frequencies, channel diameters, and power outputs. Then, the distinct cavitation patterns, including array, slug cluster and cluster, along with their corresponding mixing performances were studied in nine USMRs. By establishing a comprehensive map of cavitation patterns, primary factors affecting the formation of cavitation patterns were identified. Among the three cavitation patterns, the slug cluster pattern demonstrated the best mixing performance, wherein cavitation bubbles effectively occupied the channel and traversed rapidly across the cross-section. To guide the operation of USMRs under the slug cluster pattern, a tailored mixing model was developed. This model, in combination with the relationship between NP size and mixing time, enabled the successful seamless scale-up production of 55 nm PLGA-PEG NPs, with a required mixing time of 27 ms. This USMR-based scale-up strategy facilitated efficient process screening with low sample consumption (2 mL/min, 200 μL/sample) as well as high-throughput production (100 mL/min, 30 g/h).

Improving the Production Efficiency Based on Algorithmization of the Planning Process

Planning and managing the production process are key challenges faced by every manufacturing organization. The main contribution of this article lies in the analysis and design of a planning algorithm that takes into consideration the specifics of this environment. The proposed algorithm encompasses elements of batch production, including a just-in-time approach. The article focuses on scenarios within batch production. Managers of manufacturing and supply companies must ensure smooth fulfillment and uninterrupted production of the agreed-upon quantity of parts. However, this task presents complex challenges. The product portfolio requires meticulous sequencing of production batches, and subsequent parts need to be temporarily stored in their raw state for further processing. Moreover, product variability necessitates frequent adjustments to the production line, resulting in delays. Shortages in manpower additionally place demands on shift organization. The company’s primary objective is to increase production efficiency while simultaneously reducing inventory and minimizing non-standard shift work. The challenge was to reconcile seemingly conflicting company requirements and to concentrate on solutions with swift implementation and minimal costs. Ensuring seamless production operation can be addressed by expanding supporting technologies or by increasing production capacity, such as acquiring an additional production line. However, these options entail costs and do not align with the company’s expectation for immediate impact and cost savings. However, improving production efficiency can also be achieved by altering the approach to production planning, which is the central theme of this article. The key element is ensuring that the customer plan is adhered to while working with a fixed production logic and variable input factors that must account for various non-standard situations.