Processing Requirements Research Articles

High-resolution image processing and entity recognition algorithm based on artificial intelligence

Abstract Objective With the popularity of high-resolution devices such as high-definition, ultra-high-definition televisions, and smartphones, the demand for high-resolution images is also increasing, which puts forward higher requirements for high-resolution image processing and entity recognition technology. Method This article introduced the research progress and application of high-resolution image processing and entity recognition algorithms from the perspective of artificial intelligence (AI). First, the important role of AI in high-resolution image processing and entity recognition was introduced, and then the applications of deep learning-based algorithms in high-resolution image grayscale equalization, denoising, and deblurring were introduced. Subsequently, the application of AI-based object detection and image segmentation algorithms in entity recognition was explored, and the superiority of AI-based high-resolution image processing and entity recognition algorithms was verified through training and testing. The accuracy of the model was verified through testing experiments. Finally, a summary and outlook were made on high-resolution image processing and entity recognition algorithms based on AI. Result After experimental testing, it was found that high-resolution image processing and entity recognition based on AI had higher efficiency, and the overall image recognition ability was improved by 29.6% compared to traditional image recognition models. The recognition speed and accuracy were also improved. Conclusion High-resolution image processing and element recognition algorithms based on AI enabled observers to see the detailed information in the image more clearly, thus improving the efficiency and accuracy of image analysis. Through continuous improvement of algorithm performance, real-time application, and expansion of cross-disciplinary applications, people can look forward to the development of more advanced and powerful image processing and entity recognition technologies, which will bring huge impetus to research and application in various fields.

Methods for the Construction and Editing of an Efficient Control Network for the Photogrammetric Processing of Massive Planetary Remote Sensing Images

Planetary photogrammetry remains an important technical means of producing high-precision planetary maps. High-quality control networks are fundamental to successful bundle adjustment. However, current software tools used by the planetary mapping community to construct and edit control networks exhibit very low efficiency. Moreover, redundant and invalid control points in the control network can further increase the time required for the bundle adjustment process. Due to a lack of targeted algorithm optimization, existing software tools and methods are unable to meet the photogrammetric processing requirements of massive planetary remote sensing images. To address these issues, we first proposed an efficient control network construction framework based on approximate orthoimage matching and hash quick search. Next, to effectively reduce the redundant control points in the control network and decrease the computation time required for bundle adjustment, we then proposed a control network-thinning algorithm based on a K-D tree fast search. Finally, we developed an automatic detection method based on ray tracing for identifying invalid control points in the control network. To validate the proposed methods, we conducted photogrammetric processing experiments using both the Lunar Reconnaissance Orbiter (LRO) narrow-angle camera (NAC) images and the Origins Spectral Interpretation Resource Identification Security Regolith Explorer (OSIRIS-REx) PolyCam images; we then compared the results with those derived from the famous open-source planetary photogrammetric software, the United States Geological Survey (USGS) Integrated Software for Imagers and Spectrometers (ISIS) version 8.0.0. The experimental results demonstrate that the proposed methods significantly improve the efficiency and quality of constructing control networks for large-scale planetary images. For thousands of planetary images, we were able to speed up the generation and editing of the control network by more than two orders of magnitude.

Can reducing learning costs improve public support for means‐tested benefit programs?

AbstractThis study investigates how efforts to reduce learning costs of means‐tested public benefit programs impact public support of these programs and perceived deservingness of program beneficiaries. Focusing on the Supplemental Nutrition Assistance Program (SNAP) in the United States, a well‐known means‐tested public benefit program, we integrate research from educational psychology with policy feedback theory, predicting that the structure of information about SNAP's application process and eligibility requirements affects learning costs and public attitudes toward this program and its beneficiaries. Testing these predictions through a preregistered dose–response survey experiment, participants are randomly assigned to control or one of three treatment groups, which incrementally alters the structure of SNAP information participants are exposed to. Our findings demonstrate that enhancing the SNAP information structure lowers learning costs and indirectly improves public support and perceived deservingness of beneficiaries. We discuss implications for theory and practice.

Digital light processing printed resin based conductive composites with CF@Ag and HGMs@Ag as fillers for metamaterial absorbers

AbstractAn integrated fabrication process based on multi‐material Digital Light Processing (DLP) technique for metamaterial absorbers (MMAs) is proposed. Considering the requirements of the DLP process for the rheological properties and photocuring properties of the slurry, a photopolymer resin slurry was developed using silver‐plated carbon fiber (CF@Ag) and silver‐plated hollow glass microspheres (HGMs@Ag) as conductive fillers. To meet the conductivity requirements of resistive films in MMAs, the study systematically investigated the influence of material parameters and DLP process parameters on the conductivity and precision of the fabricated patterns. Optimized DLP parameters for isotropic photocurable conductive composites with high conductivity and low percolation thresholds were obtained. Additionally, a MMAs was designed to achieve effective absorption in the wavelength range of 7 to 28 GHz. The absorber utilized a resistive film with a thickness of 30 μm and an average surface resistivity of 23 ohm/sq., prepared using a photocurable resin filled with a total mass fraction of 35 wt% CF@Ag and HGMs@Ag in a 2:1 mass ratio. This work shows that it is feasible to fabricate resistive film patterns with prescribed surface resistivity and geometric shapes through DLP printing directly without sintering, providing a promising process for the integrated manufacturing of high‐performance MMAs.Highlights Developed a conductive slurry using the synergy between CF@Ag and HGMs@Ag. Proposed the preparation process of resistance controllable resistive film. The first demonstration of manufacturing resistive film‐type MMAs via DLP.

Ground Robots Bring Potential for Efficiency, Safety in FPSO Operations

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 32872, “Evaluation of the Potential Impact of Ground Robots on FPSO Operations,” by Gustavo L.L. da Silva, Maurício M. Cordeiro Jr., and Maurício Galassi, Petrobras, et al. The paper has not been peer reviewed. Copyright 2024 Offshore Technology Conference. _ The objective of this paper is to present the operator’s learnings in evaluating the processes by which the asset crew of a floating production, storage, and offloading (FPSO) vessel identifies scenarios for the application of robotics in day-to-day offshore activities. The studies reveal a glimpse of the addressable market size of ground robots for routine offshore verification tasks. An initial analysis suggests that 30–50% of the theoretical equivalent of hours per year could be freed. Introduction In the complete paper, the hypothetical use of ground mobile robots to carry out operational activities is considered. For that goal, the capability of robotic platforms to move along an offshore facility (an FPSO, for the purposes of this paper) has been explored. This includes the ability to overcome obstacles, move up and down stairs or ramps, and walk on uneven floors or gratings while being piloted remotely or even doing so autonomously. Some of the considered equipment may have coupled robotic manipulators that also enable them to interact with the environment, allowing them to open or close doors, turn handles, and activate commands. These robotic platforms, in general, include actuator mechanisms and sensors that enable visual inspections, thermography, dimensional analysis, area mapping, gas measurements, noise and vibration analysis, and other operations requiring specific sensing. Operational Requirements A paramount requirement for a robot to be widely used in an FPSO is its ability to operate in hazardous areas. Robotic platforms for hazardous zones are not widely available. However, some companies are quickly advancing in the development and commercialization of these platforms. A Swiss company has already made available a quadruped robot with high mobilization capacity, including the ability to navigate stairs and carry out autonomous missions that have been preprogrammed. An Austrian company has marketed a caterpillar robot that can carry out inspections autonomously in classified areas. Every such activity will demand thorough analysis to properly define technical and process requirements. On FPSO platforms, the procedure for planning any service takes place at least a few days before its execution, a task already demanding many hours. Subsequently, a permit to work (PTW) is issued to perform the service. Sensors and Actuators for a Robotic System Some of the main data-acquisition or processing sensors used in robotic systems are detailed in the complete paper. In this context, “data” means everything that can be processed online or is capable of producing useful information in the future. Cameras for Acquisition of Images and Videos. These can be very useful in the visual inspection of systems or equipment, the visualization of the workspace, or the generation of input for image processing with the purpose of identifying patterns of interest, segmentation, classification, or other aspects related to computer vision.

Topology synthesis based on FIS theory of double-layer parallel mechanism with all drives fixed

The manipulation of large workpieces often requires manipulators with complex structures and high stiffness to ensure stability and precision during operation. In situ processing equipment is typically employed for this purpose, comprising a mobile carriage (CGA), mechanical arms, and parallel processing units. The end mechanism of in situ processing equipment must exhibit high rigidity and a wide range of motion in order to effectively satisfy the high-efficiency processing requirements of large and complex structural components. This paper presents a comprehensive process for the topology synthesis of double-layer parallel mechanisms and conducts research on its topology synthesis. Firstly, a comprehensive analysis is conducted on the number and types of degrees of freedom required for basic tasks such as stretching, derotating, twisting, and grasping. This results in the simplest mathematical expression for continuous motion corresponding to the processing tasks. Subsequently, the double-layer superimposition principle of the parallel mechanism is elucidated in accordance with the requirements of the processing tasks. Proposed are anticipated motion patterns and allocation methods. Furthermore, the standard chains are analyzed and characterized based on the desired motion patterns. Derived standard chains that satisfy the desired motion patterns are obtained through joint equivalent transformations. Finally, the assembly conditions are determined in order to obtain the various available configuration structures that satisfy the processing requirements. This study proposes a novel manipulator design that can precisely control the motion of the end platform using only one set of drives, significantly improving the stability and precision of large workpiece manipulation, a challenge that has not been fully addressed in the existing literature. This provides a robust theoretical foundation for the subsequent development of in situ processing equipment.

МОДЕЛЬНЫЙ РЯД УНИФИЦИРОВАННЫХ СИЛОВЫХ ЭЛЕКТРОПРИВОДОВ ДЛЯ КОММЕРЧЕСКОГО ЭЛЕКТРОТРАНСПОРТА

The article considers the creation of a model range of unified electric power drives for commercial electric vehicles. The peculiarity of the work is the realization of the concept of reducing the cost of production of electromechanical power units (EMPU) of electric vehicles by unification of units and components included in its composition. When forming a number of unified electric power drives, the components were subjected to a set of design, layout, technological and economic requirements ensuring the production on their basis of cargo or passenger electric vehicles with gross weight from 3.5 to 12 t, which corresponds to vehicle categories N1, N2, M2, M3. The developed main components of the unified EMPU include: asynchronous three-phase traction electric motors with power range from 80 to 300 kW, torque range from 250 to 1,000 N·m respectively, output shaft rotation frequency of 9,000 rpm and liquid cooling of stator and rotor; inverters designed to control traction motors with input voltage range from 300 to 900 V, output phase current range from 200 to 600 A and liquid-cooled power keys; reduction gearboxes with gear ratio from 3 to 4.5 and step gearboxes with the number of gears from 2 to 4. The result of works on unification of electric power drives for commercial transport also is a number of standard designs of parts and units and assembly units; normative and technical support of control (tests, analysis, measurements) of certification and quality assessment of products; establishment of requirements for technological processes, including for reduction of material, energy and labor intensity to ensure the use of low-waste technologies. When creating the electric truck MAZ-4381EE, corresponding to the vehicle category N2, the EMPU was used as a part of electric motor TAEM 130 with capacity of 130 kW and two-stage shaft gearbox. The created prototype of electric truck is characterized by almost complete localization, providing import independence of most components and intellectual component of the car.

Real-time Detection of Abnormal Financial Transactions Using Generative Adversarial Networks: An Enterprise Application

This paper presents a novel real-time financial fraud detection framework utilizing Generative Adversarial Networks (GANs) for enterprise applications. The proposed system addresses critical challenges in fraud detection, including class imbalance, real-time processing requirements, and enterprise scalability. Implementing a sophisticated multi-layered architecture, the system integrates advanced preprocessing techniques with an optimized GAN model explicitly designed for fraud pattern recognition. The framework incorporates parallel processing capabilities and adaptive batch processing mechanisms to maintain high throughput while ensuring sub-second latency. The experimental evaluation uses a subset of the European Credit Card Transaction dataset, comprising 50,000 transactions with a balanced representation achieved through strategic sampling and SMOTE technique. The proposed model achieves 97.8% accuracy, 96.5% precision, and 95.8% recall, demonstrating competitive performance compared to traditional machine learning approaches. Real-time performance analysis shows consistent sub-100ms latency while maintaining robust performance under varying load conditions. The system demonstrates linear scalability up to 32 nodes, with high availability and failover capabilities. The comprehensive assessment validates the framework's effectiveness in enterprise environments, providing practical solutions for financial institutions facing evolving fraud challenges. This research contributes to the advancement of financial security technology through the innovative application of adversarial learning in fraud detection.

Rheology and early age evaluation of 3D printable cement-limestone filler pastes with nanoclays and methylcellulose

Nanoclays (NC) are known to increase the structural build-up of cement-based materials (CBM) while methylcellulose (MC) is used to increase open time and workability, helping to prevent cold joints in 3D Printing applications. The combination and compatibility of both NC and MC could lead to the design of new 3D printable CBM able to tackle all the manufacturing process requirements. The aim of the study is to evaluate the effect of sepiolite nanoclay and MC on rheology and early age (EA) parameters of cement-limestone filler pastes for 3D printing applications, identifying fresh structural build up and setting mechanisms. The effect of two cement types with coarser and finer particle size distribution (CEM I 42.5-R and CEM I 52.5-R, respectively) was considered. Pastes rheology was characterized by Cone Penetration Test (CPT) and Dynamic Shear Rheometer (DSR). EA was monitored by P- and S-waves Ultrasonic Pulse Velocity (UPV), internal temperature, capillary pressure and drying shrinkage. Fresh Shear Yield Stress (τ0) and shear elastic modulus (G), effective thixotropy (Athix), critical strain (γcrit) and their evolution over time were calculated form CPT and DSR experimental results. A limit of usability was defined for τ0 up to 20 kPa for CPT with a 30º tip cone. Although pastes with both NC and MC showed thixotropy, NC reduced γcrit while MC strongly increased it. NC significantly increased Athix and ΔG with coarser particle size cement when compared to the finer cement. MC delayed hydration reaction and setting, while NC did not affect early age stages. On the other hand, combining NC and MC increased superplasticiser demand and reduced effective thixotropy (Athix). Finally, an integrated overview of the physical and chemical mechanisms involved in rheology and early age properties of cement-based systems and the key experimental parameters for their identification was proposed.

Securing the internet of things frontier: a deep learning ensemble for cyber-attack detection in smart environments

<span lang="EN-US">This study presents a novel and innovative approach using deep learning (DL) ensemble technique to improve the security of internet of things (IoT) by identifying intricate cyber-attacks. By utilising advanced DL models like deep neural network (DNN) and long short-term memory (LSTM), our approach significantly enhances the accuracy of categorization compared to basic models. The initial binary classifier achieved an accuracy of 85.2%, while the multi-class classifier achieved an accuracy of 79.7%. Both classifiers continually enhanced, achieving accuracies of 99.34% and 98.26%, respectively, after 100 epochs. Real-time scenario evaluations showed that the average execution time per sample record was 0.9439 ms, confirming its efficiency. The DL ensemble exhibited improved performance in comparison to traditional models, indicating its potential for wider implementation in IoT security. The study not only emphasises significant improvements in accuracy, but also emphasises the method’s ability to perform well across many evaluation measures. This study presents a thorough and pragmatic method for identifying cyber-attacks in IoT settings. The stacked ensemble technique outperforms earlier models and fulfils real-time processing requirements, offering substantial advancements in IoT security. These findings enhance both the theoretical comprehension and practical application, establishing a novel benchmark for protecting intelligent IoT systems.</span>

A high-performance flame retardant liquid synthesized on the basis of DOPO hydrolysis products avoids the defects of DOPO

It was first proposed to use 2-(2-hydroxyphenyl) phenyl phosphonic acid (HPPA), which is hydrolysis product of DOPO, has a structure more similar to EP, to replace DOPO in the preparation of flame retardant, and finally synthesized a highly efficient liquid flame retardant DP based on HPPA, the theoretical phosphorus content of DP is up to 17.5wt%, so EP can achieve excellent flame retardant/inhibit combustion effect with a small amount addition of DP. For example, the LOI of EP/DP6.0 is increased to 41.2 % and reaches the V-0 rate of UL-94, the PHRR and THR are reduced by 52.4 % and 45.2 % respectively, in addition, the addition of DP in small quantities does not destroy the overall performance of EP, maintains and improves the excellent mechanical and thermal properties of epoxy resin as a whole, and DP has excellent curing promoting effects, meeting the processing and use requirement of high performance EP. It is better than DOPO overall and has good application prospects.

Optimal design and experimentation of full tailings and cementitious materials: a case study of a lead-zinc mine in China

As mineral resources become increasingly scarce and environmental awareness grows, mining companies urgently need cost-effective and environmentally friendly filling methods for mines. The use of ultrafine tailings combined with various binders at different ratios plays a key role in determining the rheological properties and mechanical strength of cemented paste backfill. This paper provides parameter basis for the design of a mine filling system, firstly, different filling materials and proportions were selected, and basic physicochemical property tests, along with cementitious material analysis, were conducted to determine the key physical and chemical properties and mineral composition of the materials. Next, the rheological properties, transport performance, and bleeding characteristics of the filling slurry were tested to assess the feasibility and process requirements for self-flow transport through pipelines. The results indicated that the transportation performance of the slurry was better when gelling powder was used as the binder. Finally, the compressive, shear, and tensile strengths of the filling blocks were tested under varying slurry mass concentrations and cement-tailings ratios at different curing times. This helped identify the optimal filling concentration and proportions needed to meet the strength requirements of the filling structure. The final study found that the sample with a cement-tailings ratio of 1:6, a mass concentration of 68%, and a gelling powder as the binder reached the strength requirements for mine filling after 28 days. This research offers valuable insights for other mining companies seeking to implement similar environmentally sustainable and cost-effective filling technologies, promoting wider adoption and advancements in the field.

Wi-Fi CSI fingerprinting-based indoor positioning using deep learning and vector embedding for temporal stability

Fingerprinting systems based on channel state information (CSI) often rely on updated databases to achieve indoor positioning with high accuracy and resolution of centimeter-level. However, regularly maintaining a large fingerprint database is labor-intensive and computationally expensive. In this paper, we explore the use of deep learning for recognizing long-term temporal CSI data, wherein the site survey was completed weeks before the online testing phase. Compared to other positioning algorithms such as time-reversal resonating strength (TRRS), support vector machines (SVM), and Gaussian classifiers, our deep neural network (DNN) model shows a performance improvement of up to 10% for multi-position classification with centimeter-level resolution. We also exploit vector embeddings, such as i-vectors and d-vectors, which are traditionally employed in speech processing. With d-vectors as the compact representation of CSI, storage and processing requirements can be reduced without affecting performance, facilitating deployments on resource-constrained devices in IoT networks. By injecting i-vectors into a hidden layer, the DNN model originally for multi-position localization can be transformed to location-specific DNN to detect whether the device is static or has moved, resulting in a performance boost from 75.47% to 80.62%. This model adaptation requires a smaller number of recently collected fingerprints as opposed to a full database.

Cloud-Native Microservices for Real-Time Data Systems: A Technical Deep Dive

Cloud-native microservices have emerged as a transformative solution for organizations facing the challenges of real-time data processing and analysis in today's digital landscape. This comprehensive article explores the fundamental components of cloud-native architectures, from their foundational principles to implementation best practices. The article examines how containerization, orchestration, and event-driven architectures enable unprecedented scalability and efficiency in handling modern data processing requirements. Through a detailed examination of microservices as building blocks, the research demonstrates how these architectural patterns significantly improve system reliability, performance, and maintainability. The article encompasses advanced monitoring and observability practices, highlighting their crucial role in maintaining robust cloud-native systems. By investigating real-world implementations across various industries, this study provides valuable insights into the practical benefits and challenges of adopting cloud-native approaches.

Preparation, process optimization, and performance study of tea polyphenol-sodium alginate/ethyl cellulose composite microcapsules for green slow-release formaldehyde capture agent

Aiming at the problem of prolonged formaldehyde release from artificial boards, a green slow-release formaldehyde scavenger was proposed in this study. Tea polyphenol microcapsules (TPM) were prepared by ionic gel-solvent evaporation method using sodium alginate/ethyl cellulose as the wall material and tea polyphenol as the core material. Fourier transform infrared spectroscopy (FTIR) analysis showed that the TPM exhibited the same characteristic absorption peaks as those of tea polyphenol, ethyl cellulose and sodium alginate. This indicates that the TPM was successfully prepared and the thermal stability of TPM meets the requirements of the hot pressing process of man-made boards. The optimal process of TPM was as follows: the mass ratio of tea polyphenol to ethyl cellulose was 2.58, the dosage of emulsifier was 1.01 g, and the mass fraction of sodium alginate was 17.23%, the encapsulation rate of TPM was 78.31%, and the average particle size was 18.69 ± 4.66 μm. After 180 min. After 180 min, the aldehyde reduction efficiency was 61.08%. In conclusion, TPM meets the requirements of the production process of wood-based panels and the need for continuous control of formaldehyde emission from wood-based panels.

Data Processing of Used Car Export Service Flow Based on Zookeeper + Kafka

The used car export industry has shown a trend of rapid growth in recent years. The export of used cars involves data pertaining to vehicle information, market demand, logistics, customs, and numerous other aspects. These data are constantly generated in the form of streams, posing challenges to the data processing capabilities of service platforms. Traditional data processing methods struggle to meet the requirements of real-time and large-scale data processing, thus necessitating an efficient streaming data processing architecture. Based on this, this paper proposes a streaming data processing scheme for a used car export service platform utilizing Zookeeper and Kafka. It describes the architecture design and data processing flow, analyzes the advantages of the scheme in terms of reliability, scalability, and real-time performance, and demonstrates its effectiveness through experiments and case studies.

The Influence of Financial Literacy and Digital Literacy on Mobile Banking Adoption

This research examines the influence of Financial Literacy and Digital Literacy on Mobile Banking Adoption in Jabodetabek. This research data was obtained using a questionnaire in the form of a Google form and distributed via the WhatsApp friendship network and other social media such as TikTok and Instagram. The respondent criteria for this research are bank customers who are over 20 years old, have a mobile banking application, and actively use the mobile banking application for daily transactions. A total of 210 answers have been filled in and all answers have met the requirements for processing. This research observes that there is a significant influence of Financial Literacy and Digital Literacy on Mobile Banking Adoption. The dependent variables, namely Perceived Ease of Use, Perceived Usefulness, and Relative Advantage, have high explanatory power of the model. The independent variables Financial Literacy and Digital Literacy have a strong influence on the three dependent variables of this research, showing that Financial Literacy and Digital Literacy have a direct or positive influence on the use of Mobile Banking.

Literatur Review: Analisis Penempatan Kerja terhadap Prestasi Kerja Karyawan di Lingkungan Perkantoran

This study aims to describe the concept of job placement, essential requirements in the job placement process, factors that must be considered, and types of employee placement. The research method used is a literature study. Job placement is placing an employee in a position that is in accordance with their knowledge, experience, and skills. By considering the placement that is following the knowledge possessed, both conceptual understanding and technical knowledge of the work to be done, work skills, and an employee's attitude towards the conditions of the job (The right man in the right place) so that employees can more easily carry out their duties/jobs in order to achieve better work performance. This means that the better employees' job satisfaction, the better their performance.

The effects of growth interruptions in the GaAsBi/InAs/GaAs quantum dots: The emergence of three-phase nanoparticles

The study investigated the impact of introducing bismuth into the GaAs capping layer (CL) on InAs quantum dots (QDs) to enhance their QD properties. Three different time-temperature routes (TTRs) were examined, as growth interruption (GI) stages are necessary due to the temperature requirements for the growth processes of QDs (510 °C) and GaAsBi CL (370 °C). Two of the TTRs revealed defective regions with bismuth-free nanotracks in the GaAsBi CL, which are linked to the formation of bismuth-rich droplets on the surface. Interestingly, in one of the TTRs, novel icosahedral-type nanoparticles appeared embedded at the first interface, leaving trails behind them. Upon detailed characterization, it was found that these nanoparticles consist of three distinct phases containing rhombohedral Bi, pure Ga, and a new In4Bi phase that had not been experimentally described before. The long particle trajectories and low temperatures suggest that the NPs remained liquid throughout the growth process, solidifying upon final cooling to room temperature. This work presents a new technique for incorporating plasmonic nanoparticle arrays made of non-noble metals into buried semiconductor layered interfaces, which offers greater flexibility in device design.

Smart contracting for production supply in shared manufacturing: a practical simulation approach

Enhancing transparency in production processes, especially in shared manufacturing, relies heavily on sharing data. Information asymmetries and coordination problems between parties with conflicting interests pose a challenge in this multi-stakeholder interaction. Blockchain technology with smart contracting can be a solution due to its immutable data and decentralised data storage features. Designing and executing blockchain in industrial applications is a highly intricate task that requires extensive testing, expertise, and proficiency. This paper is the first to propose a holistic simulation model for evaluating the impact of smart contracting on shared manufacturing, including a novel approach to simulated smart contracting in time-lapse for Ethereum-based networks. The introduced model guides the design and implementation process of blockchain applications in shared manufacturing to address this challenge. A systematic literature review establishes ten design process requirements and ten smart contract functions. The implementation is developed based on the design benchmarks of three Ethereum-based frameworks to investigate the simulation model's respective feasibility and scalability. The simulation model validation demonstrates our approach's suitability for simulating smart contracting in shared manufacturing within a hybrid production. It enables fast and scalable simulations, offering an innovative approach to extensively testing blockchain applications before their introduction to ongoing industrial operations.