Virtual Chain Research Articles

A probabilistic virtual process chain to quantify process-induced uncertainties in Sheet Molding Compounds

The manufacturing process of Sheet Molding Compound (SMC) influences the properties of a component in a non-deterministic fashion. To predict this influence on the mechanical performance, we develop a virtual process chain acting as a digital twin for SMC specimens from compounding to failure. More specifically, we inform a structural simulation with individual fields for orientation and volume fraction computed from a direct bundle simulation of the manufacturing process. The structural simulation employs an interpolated direct deep material network to upscale a tailored SMC damage model. We evaluate hundreds of virtual specimens and conduct a probabilistic analysis of the mechanical performance. We estimate the contribution to uncertainty originating from the process-induced inherent random microstructure and from varying initial SMC stack configurations. Our predicted results are in good agreement with experimental tensile tests and thermogravimetric analysis.

To Share or Not to Share? The Optimal Technology Investment in a Virtual Product Supply Chain

With the rapid rise of the virtual economy, the “brand + platform” virtual product distribution model led by virtual technology platforms has emerged, bringing a series of new virtual experiences to consumers. Considering that consumers have heterogeneous preferences for virtual technologies, we investigate a virtual product supply chain consisting of a brand owner, a virtual technology platform, and consumers (where the virtual technology platform has superior information about consumer preferences). We develop a signaling game model with preference information signaled by the virtual technology investment to study the optimal information-sharing strategy for the virtual product supply chain. We find that the virtual technology platform always prefers information sharing without paying any signaling cost. Moreover, we also observe that the brand owner and the platform can achieve alignment, that is, when consumers are more likely to prefer virtual technology, both the brand owner and the virtual technology platform are better off if the virtual technology platform chooses to share information with the brand owner. Finally, we analyze the consumer welfare and find that when consumers are moderately likely to prefer virtual technology, consumers can gain more benefits in the information-sharing scenario.

Viscoelastic Properties of Soft Adhesives

AbstractIn this paper the viscoelastic behavior of different soft adhesives with substantially similar adhesive performance is analyzed in the light of a Slip‐Tube Model. In this model, the junction points between network chains and virtual chains are replaced by slip‐links attached to fluctuating ends of virtual chains. The slip‐links are allowed to slide along the contour of the network chains but not pass through each other. The predictions of the slip‐tube model are in excellent agreement with the experimental data on uniaxial deformation of soft adhesive networks. Furthermore, the study shows that some adhesive performances can be described qualitatively by Small Amplitude Oscillatory Shear (SAOS), and the in‐service properties can be described with adhesives with different viscoelastic spectra. Highly nonlinear uniaxial stretching tests reveal deeper insights into the description of the adhesives performances. Some well‐established models are adopted and slightly modified by introducing the rate‐dependence of their physical parameters to describe the response of soft adhesive to stretching.

RESEARCH ON 5G NETWORK RESOURCE ORCHESTRATION ALGORITHM BASED ON NETWORK VIRTUALIZATION TECHNOLOGY

With the advancement of wireless network technology to the next generation, network function virtualization (NFV) brings the advantages of centralized scheduling of virtual wireless resources and the ability to orchestrate virtual network functions. This makes it possible to dynamically deploy and manage service function chains (SFCs) in virtualized wireless networks according to changes in network load. Through the network function virtualization technology, the DU/CU separation architecture under 5G-NG-RAN is considered. Aiming at the resource orchestration problem of the 5G access network virtual protocol stack function and service function chain after deployment, an energy-aware virtualized network function instance (VNFI) orchestration algorithm is proposed. By modeling the underlying physical network and node energy consumption, the node load, resource type, tolerable delay and migration loss are included in the decision, and the state of the general server node is divided into two types: running state and sleep state. The algorithm decouples the decision-making process of VNF migration into two specific modules, namely "the VNF selection module" and "the migration destination node selection module". It is verified by simulation that compared with other energy-saving strategies, the algorithm in this paper has better performance in reducing energy consumption and reducing migration loss, and it can take into account both the reduction of the number of open server nodes and the improvement of resource utilization.

CONTINUUM OF PARAPHRASING – SYNTAX SYNONYMY IN THE DICHOTOMY LANGUAGE IS SPEECH (on the material of modern French artistic prose)

This paper is dedicated to the studies of syntactical synonymy as one of kinds of periphrasing in the dichotomy language/ discourse and theory of mental kinetism. It is establishedthat in the process of transformation in language are formed the one basic (with one terminal chain) and two basic (with two or more terminal chains)synonymic periphrases of pivotal (primary) syntagm or proposition, that are actualized in discourse in the form of co(n)textually preferential options. On the bases of syntactical and semantic valence of elements of synonymic constructionsare defined the key lexemes of each option which form with peripheral lexical components certain models of synonymic derivational trees (nominal, verbal, adjectival etc.). As the result of studies the virtual terminal chains of discourse innovations-synonymic structures are reconstructed and the hierarchical and horizontal semantic and syntactical relations between them are established in the functional-semantic field of syntactical synonymy.

A Blockchain Storage Architecture Based on Information-Centric Networking

Blockchain technology is a unique distributed ledger technology that has been widely used in various areas. With the increase in data on the blockchain and the append-only nature of the blockchain, the traditional blockchain’s full replica storage technique leads to blockchain storage scalability problem. Existing methods prioritize minimizing the storage strain on blockchain nodes while ignoring the availability of data, resulting in a lengthy average response time for users to access the blockchain. To address the shortcomings, this paper proposes an Information-Centric Networking-based blockchain storage architecture. The architecture uses the enhanced resolution system for community division to build blockchain node partitions and store blockchain ledgers in the underlying network. It introduces virtual chain for rapid blockchain indexing and adopts a collaborative block replica deletion algorithm across neighboring partitions, including replica number decision based on blockchain access decay characteristics and replica deletion based on resource relationship. Finally, we compare and analyze the proposed blockchain storage architecture with BC-store and KASARASA, and the results demonstrate that this architecture has significantly lower average access time than others. The replica data volume of this method is reduced by 57.2% compared to the full replica policy, but the access time is only 5.2% slower when compared to the full replica policy, which substantially increases the replica storage utilization.

Pricing and quality decisions in virtual product supply chains with information sharing

This article investigates the retail price and quality effort joint decisions in a virtual product supply chain with information sharing. The virtual product supply chain consists of a supplier, who develops a virtual product, and a distributor, who distributes the product. We model two popular competitions as Stackelberg and Nash games and identify the equilibrium conditions for the pricing and quality effort decisions. In Stackelberg game, the supplier’s profit monotonically increases in the elasticity of quality effort. In Nash game, and a higher elasticity of quality effort does not always benefit the supplier. The distributor will voluntarily share demand information only with a large quality economy. Our analyses reveal that the double marginalisation is weaker when the distributor shares information. However, the distributor’s mark-up decision is independent with the cost of quality effort. With a large quality economy, information sharing benefits both supply chain players under certain conditions. We show that supply chain game structure does not affect the distributor’s decision in information sharing, and the decision only depends on the market structure. In the extension, nonlinear demand functions are investigated, and the results show that most of the results from the main analyses still hold with a few exceptions.

Application of Cloud Computing Combined with GIS Virtual Reality in Construction Process of Building Steel Structure

In order to ensure that the steel structure of the building can meet the requirements of the strength of the building structure and the strict requirements in the fields of earthquake resistance, fire protection, energy saving, and environmental protection in the construction process, a method based on virtual reality supply chain cloud computing collaborative management technology is proposed. This method analyzes the main technology of building steel structure and establishes personalized cloud computing collaborative management technology based on virtual reality supply chain. Based on the analysis of industry characteristics and personalized service characteristics, the virtual reality control mechanism of personalized data mining is proposed by considering the service time cost and user experience quality. Based on cloud computing and VIRTUAL reality GIS, a collaborative management and control system architecture is proposed to optimize user personalized needs and solve the impact of differentiation on the supply chain. Experimental results show that compared with the noncollaborative management scheme, the collaborative management algorithm proposed by this method reduces the interference of personalized differences on supply chain management by 25%. The experimental results show that this method can greatly guarantee the satisfaction of steel structure construction process.

Blockchain and IoT embedded sustainable virtual closed-loop supply chain in E-commerce towards the circular economy

Blockchain and the internet of things (IoT) have accelerated e-commerce growth and empowered online businesses to create value even in a globally competitive environment. The goal of this study is to develop a framework for the virtual closed-loop supply chain (VCLSC) that is based on novel blockchain and IoT technologies, as well as the concepts of sustainability and circular economy. This study integrates the normal and virtual supply chains into a single framework. To maximise the total expected revenue (TER) from the VCLSC, the costs of getting raw materials and parts, transporting them with a carbon emission tax, handling, processing, and making final products, refurbishing, recycling, and virtualization are all taken into account. These include running blockchain and IoT facilities, buying RFID tags, ensuring security, and storing data in blockchain in a way that cannot be changed. The above costs are captured in a single mixed-integer non-linear programming (MINLP) model. The main objective is to maximise the TER by minimising the cost of the VCLSC network. For the solution purposes, the global solver in LINGO 19 software package is used. The obtained results and performed sensitivity analysis are used to make some implications for business organisations and government administrators to create some important policies in favour of the environment, end-users, and the development of the organisation along with the nation.

Multiobjective Optimization Model and Algorithm Based on Differential Brain Storm for Service Path Constructing

Network function virtualization (NFV) can provide the resource according to the request and can improve the flexibility of the network. It has become the key technology of the next-generation communication. Resource scheduling for virtual network function service chain (VNF-SC) mapping is the key issue of the NFV. Aiming at previous research primarily focused on constructing service paths with a single objective, for example, latency minimization, cost minimization, or load balance, which ignored the overall performance of constructed service paths, a multiobjective model, which minimizes benefit, expense, time delay, and load balance, to solve the service path constructing problem, is established. In this model, VNF-SCs are divided into two classes, i.e., part of the required VNFs in each VNF-SC is dependent, and others are independent. To solve this multiobjective model, an algorithm based on discrete difference brain storm (MO2DBS) in the framework of MOEA/D was proposed. In the new algorithm, a two dimensional integer coding is designed. In addition, a difference mutation was used to replace the original Gaussian mutation to adjust the mutation step adaptively. Simulation experiments show that the proposed algorithms can obtain higher benefit, load balance, lower expenses, and time delay than the compared algorithms.

Tensor interpolation in virtual manufacturing chains for fiber reinforced composites

Preeminent weight-specific mechanical properties predestine fiber-reinforced plastics for application in structural components. Virtual manufacturing chains (CAE-chains) capture a multi-step process sequence by interlinking models with corresponding constitutive laws for each process step. In common numerical solving techniques, spatial discretization of governing equations yields discrete solutions. Since mesh type and fineness are usually problem-specific, mesh-to-mesh mapping must be embedded in the data interfaces between the individual simulation steps. To receive meaningful data, the underlying averaging and interpolation schemes between non-congruent meshes must be mathematically and physically consistent. In engineering applications, interpolation of tensors is usually carried out component-based, treating each component as an independent scalar-field and thus neglecting the tensorial character. The work at hand gives an overview of sophisticated techniques that preserve specific tensor characteristics in interpolation. Compared to available approaches, an enhanced decomposition-based interpolation method is proposed, allowing for a generalization with more than two basic values. In the context of process simulation for short fiber-reinforced injection molding, the influence of the interpolation techniques is evaluated in a three-stage approach with increasing proximity to application. Firstly, mathematical examples demonstrate that component-based interpolation can result in erroneous, non-monotonous tensor characteristics. Secondly, an analytically resolvable problem is derived, and the solution is compared to reconstructed tensors from different interpolation schemes. Thirdly, a numerical CFD-simulation is conducted. Field recovery via interpolation is performed and systematical errors are statistically evaluated. The results reveal significant biases for certain tensor characteristics induced by conventional component-wise interpolation. Overall, a reduction in systematic interpolation errors is achieved by the proposed decomposition-based interpolation.

Optimal HEV cooling system design through a computer-aided virtual development chain

To reduce CO2 and pollutant emissions, automotive manufacturers tend to turn increasingly toward electrified powertrains solutions, such as Hybrid Vehicles (HEV). Model Based System Engineering (MBSE) and computational tools are useful to support the development of HEV systems. System architectures need to be investigated to find optimal solutions regarding multiple possible goals constraints (fuel consumption, CO2 emissions, etc.). In this study, a novel global optimization framework is proposed to enable an automated cooling system optimization. The methodology starts with a set of components, design rules, and system requirements to search and automatically generate all the admissible cooling system architectures. System architectures are generated by a concept of decision tree. The decision tree algorithm is coupled to a design rules database to avoid exploring solutions that do not satisfy system requirements. Hydraulic models are automatically built for each system architecture generated to optimize head losses and minimize the pump consumption. For the most interesting solutions, 3D piping geometrical data (lengths, angles, and curve radius) are automatically drawn by a 3D optimization tool. Piping geometrical data are integrated in GT-SUITE thermo-hydraulic simulation models automatically built to evaluate the most interesting cooling system architectures generated. The best candidates are identified by a multi-criteria decision-making strategy. A first case of conventional vehicle cooling system is studied. Results on the given example indicate that the difference of energy consumption on WLTC cycle between the best performing architecture and the worst one is 69%.

Precision analysis of series-parallel machine tool based on hierarchy motion function units

This paper takes the motion units of the series-parallel machine tool (SPMT) as the basic research objects. Combined with the meta-action theory, the FMA-MBS structure model is proposed to describe the spatial structure relationships and motion transfer relationships of SPMT. Based on the simplified model, the kinematics characteristics of the machine tool are gradually researched from four layers: joint layer, limb layer, subsystem layer, and the whole machine. According to the twist system of limbs and moving platform, the parallel motion system can be simplified into a virtual motion chain with specific constraints. Then, the kinematics model of SPMT is constructed in twist exponential form. By introducing two virtual degrees of freedom (DOFs), the unified expressions of active and passive joint errors are obtained. All motion systems error models are derived based on three basic error terms of the meta-action layer. Based on the error mapping relationships, the precision of the machine tool can be calculated and evaluated. In order to illustrate the applicability and effectiveness of the proposed method, a sample library of common SPMTs and a case study of a 5-axis series-parallel machining center are provided.

Advanced process simulation of compression molded carbon fiber sheet molding compound (C-SMC) parts in automotive series applications

The following paper focuses on the development of a 3D virtual process chain (VPC) for parts made from Carbon Fiber Sheet Molding Compounds (C-SMC). The VPC consists of two modules addressing the compression molding and warpage simulation of parts made from two different high fiber volume fraction C-SMC materials. The press simulation module applies a solid mechanics material modelling approach within the Coupled Eulerian-Lagrangian (CEL) simulation framework available in ABAQUS/Explicit. Through a combination of flow characterization experiments and numerical simulations, local mechanical properties and fiber orientation information can be predicted from numerically simulated local flow strains. A warpage simulation module has also been developed in ABAQUS/Standard together with a data processing/transfer interface implemented in ANSA. With these highly integrated numerical tools, process characteristics such as press forces, flow fronts and weld-line development based on material amount, insert geometry and placement position in the mold can all be predicted and analyzed.

GNN-Based Hierarchical Deep Reinforcement Learning for NFV-Oriented Online Resource Orchestration in Elastic Optical DCIs

Network function virtualization (NFV) in elastic optical datacenter interconnections (EO-DCIs) enables flexible and timely deployment of network services. However, as the service provisioning of virtual network function service chains (vNF-SCs) in an EO-DCI needs to orchestrate the allocations of IT resources in datacenters (DCs) and spectrum resources on fiber links dynamically, it is a complex and challenging problem. In this work, we model the problem as a Markov decision process (MDP), and propose a hierarchical deep reinforcement learning (DRL) model based on graph neural network (GNN), namely, HRLOrch, to tackle it. To ensure its universality and scalability, we design the policy neural network (NN) in HRLOrch based on a GNN. As the GNN-based policy NN can operate on the graph-structured network state of an EO-DCI directly, it can adapt to an arbitrary EO-DCI topology without any structural changes. Then, through analysis, we find that the EO-DCI is a sparse reward environment if we want to train a DRL model to minimize the blocking probability of vNF-SCs in it directly. To address this issue, we design a hierarchical DRL with lower-level and upper-level models to improve the convergence performance of training. Specifically, we make the lower-level DRL optimize the provisioning scheme of each vNF-SC to minimize its resource usage, while the upper-level one coordinates the provisioning of all the active vNF-SCs to minimize the overall blocking probability. Hence, the lower-level and upper-level DRL models operate cooperatively in the training to optimize the dynamic provisioning of vNF-SCs. Our simulations demonstrate the universality and scalability of HRLOrch, and confirm that it can outperform the existing algorithms for vNF-SC provisioning in an EO-DCI.

Optimization of virtual closed-loop supply chain under uncertainty: application of IoT

PurposeSupply chains (SCs) have been growingly virtualized in response to the market challenges and opportunities that are presented by new and cost-effective internet-based technologies today. This paper designed a virtual closed-loop supply chain (VCLSC) network based on multiperiod, multiproduct and by using the Internet of Things (IoT). The purpose of the paper is the optimization of the VCLSC network.Design/methodology/approachThe proposed model considers the maximization of profit. For this purpose, costs related to virtualization such as security, energy consumption, recall and IoT facilities along with the usual costs of the SC are considered in the model. Due to real-world demand fluctuations, in this model, demand is considered fuzzy. Finally, the problem is solved using the Grey Wolf algorithm and Firefly algorithm. A numerical example and sensitivity analysis on the main parameters of the model are used to describe the importance and applicability of the developed model.FindingsThe findings showed that the Firefly algorithm performed better and identified more profit for the SC in each period. Also, the results of the sensitivity analysis using the IoT in a VCLSC showed that the profit of the virtual supply chain (VSC) is higher compared to not using IoT due to tracking defective parts and identifying reversible products. In proposed model, chain members can help improve chain operations by tracking raw materials and products, delivering products faster and with higher quality to customers, bringing a new level of SC efficiency to industries. As a result, VSCs can be controlled, programmed and optimized remotely over the Internet based on virtual objects rather than direct observation.Originality/valueThere are limited researches on designing and optimizing the VCLSC network. This study is one of the first studies that optimize the VSC networks considering minimization of virtual costs and maximization of profits. In most researches, the theory of VSC and its advantages have been described, while in this research, mathematical optimization and modeling of the VSC have been done, and it has been tried to apply SC virtualization using the IoT. Considering virtual costs in VSC optimization is another originality of this research. Also, considering the uncertainty in the SC brings the issue closer to the real world. In this study, virtualization costs including security, recall and energy consumption in SC optimization are considered.HighlightsInvestigates the role of IoT for virtual supply chain profit optimization and mathematical optimization of virtual closed-loop supply chain (VCLSC) based on multiperiod, multiproduct with emphasis on using the IoT under uncertainty.Considering the most important costs of virtualization of supply chain include: cost of IoT information security, cost of IoT energy consumption, cost of recall the production department, cost of IoT facilities.Selection of the optimal suppliers in each period and determination of the price of each returned product in virtual supply chain.Solving and validating the proposed model with two meta-heuristic algorithms (the Grey Wolf algorithm and Firefly algorithm).

On establishing and task scheduling of data-oriented vNF-SCs in an optical DCI

The development of network function virtualization (NFV) enables service providers to provision various network services with virtual network function service chains (vNF-SCs). However, most of the existing studies on the service provisioning of vNF-SCs address only the flow-oriented ones, while the provisioning schemes of data-oriented vNF-SCs, each of which needs to process and transfer bulk data through a series of vNFs before a preset deadline, have not been fully explored yet. Therefore, this paper studies how to jointly optimize the establishing and task scheduling of data-oriented vNF-SCs in an optical datacenter interconnection (DCI), such that the probability of scheduling data-oriented vNF-SCs to satisfy their deadlines can be maximized. To make the best use of the resources in the optical DCI for meeting each vNF-SC’s deadline, we leverage dynamic programming (DP) to propose two time-efficient algorithms with deadline-prioritized and conflict-aware approaches. Extensive simulations evaluate the performance of our proposed algorithms in different network scenarios, and confirm their effectiveness. Specifically, compared with a greedy-based benchmark, our DP-based algorithms can reduce the blocking probability of data-oriented vNF-SCs by up to two magnitudes and maintain similar service completion time.

Utilization of an In Silico 16S Ribosomal RNA Gene-Based Restriction Enzyme Approach for Identification of Lactic Acid Bacteria

In silico 16S ribosomal RNA gene-based restriction enzyme approach is a computer-simulated polymerase chain reaction-restriction fragment length polymorphism analysis method, namely customizable in silico sequence evaluation for restriction sites. The purpose of this study was to identify lactic acid bacteria using restriction enzyme analysis of 16S gene (16S ribosomal RNA polymerase chain reaction-restriction fragment length polymorphism). An in silico investigation was conducted to detect species-specific molecular markers in different lactic acid bacteria strains. We selected six out of 20 16S ribosomal RNA gene sequences from probiotic-potential lactic acid bacteria strains. Initially, the corresponding 16S ribosomal RNA gene sequences were selected from the database aligned using the Clustal Omega program and their evolutionary relationships were determined. The results indicated that the studied lactic acid bacteria strains belonged to 6 distinct clades except for the genus of Lactobacillus. Furthermore, the in silico polymerase chain reactionrestriction fragment length polymorphism was conducted for further taxonomic classification. Different restriction enzymes were used to recognize sites present on the lactic acid bacteria 16S ribosomal RNA genes. Customizable in silico sequence evaluation for restriction sites output comprised a predicted agarose gel as virtual polymerase chain reaction-restriction fragment length polymorphism patterns, for proper selection of the appropriate enzyme (s) to be used for polymerase chain reaction-restriction fragment length polymorphism marker generation. Our study demonstrated that ApyPI, CchIII and NheI produced distinct restriction patterns as those of Ku324937_C species. The most abundant species-specific markers were seen for Ku324928_C (10 markers), followed by Ku324908_C (8 markers) of order Hemiptera, then Ku324909_C (5 markers), Ku324898_C (4 markers) and finally, Ku324937_C (3 markers). The results demonstrated that customizable in silico sequence evaluation for restriction sites is a powerful tool to detect species-specific markers to provide better understanding of organisms found in raw and/or fermented milk.

Time-To-Recovery Prediction in a Disrupted Three-Echelon Supply Chain Using LSTM*

This paper introduces a Long-Short Term Memory (LSTM) neural network-based method for Time-To-Recovery (TTR) prediction within a cognitive digital supply chain twin framework to enhance supply chain resilience and improve decision-making under disruption. The introduced method is applied to a virtual three-echelon supply chain network modelled using discrete event simulation. The virtual system was used to train an LSTM neural network model to predict TTR under several disruption scenarios. Results show that predicted TTR values tend to be relatively lower than the actual values at early disruption stages, then improve throughout the progression of the disruption effect on the supply chain network.

Secured Insurance Framework Using Blockchain and Smart Contract

Traditional insurance policy settlement is a manual process that is never hassle-free. There are many issues, such as hidden conditions from the insurer or fraud claims by the insured, making the settlement process rough. This process also consumes a significant amount of time that makes the process very inefficient. This whole scenario can be disrupted by the implementation of blockchain and smart contracts in insurance. Blockchain and innovative contract technology can provide immutable data storage, security, transparency, authenticity, and security while any transaction process is triggered. With the implementation of blockchain, the whole insurance process, from authentication to claim settlement, can be done with more transparency and security. A blockchain is a virtual chain of data blocks that is a decentralized technology. Any transaction or change in the blocks is done after the decentralized validator entity, not a single person. The smart contract is a unique facility stored on the blockchain that gets executed when the predetermined conditions are met. This paper presents a framework where smart contracts are used for insurance contracts and stored on blockchain. In the case of a claim, if all the predetermined conditions are met, the transaction happens; otherwise, it is discarded. The conditions are immutable. That means there is scope for alteration from either side. This blockchain and intelligent contract-based framework are hosted on a private Ethereum network. The Solidity programming language is used to create smart contracts. The framework uses the Proof of Authority (PoA) consensus algorithm to validate the transactions. In the case of any faulty transaction request, the consensus algorithm acts according to and cancels the claim. With blockchain and smart contract implementation, this framework can solve all the trust and security issues that rely on a standard insurance policy.