Hypergraph-Based Modeling of Manufacturing Services in Cloud Manufacturing

2017 ◽  
Author(s):  
Meng Yu ◽  
Wenjun Xu ◽  
Jiwei Hu ◽  
Zude Zhou ◽  
Duc Truong Pham
Keyword(s):  
Product Life Cycle ◽  
Service Management ◽  
Job Shop ◽  
Cloud Manufacturing ◽  
Proposed Model ◽  
Manufacturing Resources ◽  
Resources And Capabilities ◽  
Level Process ◽  
Process Level

Cloud manufacturing (CMfg) aims to realize the full-scale sharing, free circulation and transaction, and on-demand use of various manufacturing resources and capabilities in the form of manufacturing services. During the whole product life-cycle, the number of manufacturing services is huge, and services are highly dynamic and changeful. Without the effective operation and technical support of manufacturing service management, the implementation and aim of CMfg could not be achieved. In this paper, a multi-layer model of manufacturing service is proposed for a job shop in cloud manufacturing, in order to solve the description problem of different manufacturing services from different level view, e.g. machine level, process level and shop level. Consequently, a hypergraph-based network model of manufacturing service is developed, so as to facilitate the management of different services during the whole production process in job shop. A case study and some applications of the proposed model for supporting the manufacturing services management to practical manufacturing system are studied, to demonstrate the feasibility and efficiency of such model.

Towards a Digital Twin for Cloud Manufacturing: Case Study

2018 ◽  
Author(s):  
Diane Ngo ◽  
David A. Guerra-Zubiaga ◽  
Germánico González-Badillo ◽  
Reza Vatankhah Barenji
Keyword(s):  
Real Life ◽  
Simulation Method ◽  
Cloud Manufacturing ◽  
Conveyor Belt ◽  
Life Change ◽  
Simulation Methods ◽  
Manufacturing Cell ◽  
Digital Twins ◽  
Resources And Capabilities

Cloud manufacturing (CMfg) is a new manufacturing paradigm designed to enable manufacturing enterprise to share their resources and capabilities. Prior to any real-life change in the system, for CMfg it is important to anticipate and optimize the response of the system through simulation. Digital Twins (DT) is a simulation method for this paradigm that is different from existing simulation methods in two ways. It is a virtual copy of the system containing all the components and can connect to the controller in real time. The goal of this work is to develop a DT for an educational manufacturing cell. The educational manufacturing cell is a FESTO Reconfigurable Mechatronics System (RMS). The cell has four stations that uses pallets to transport the product on the conveyor belt and assembles a part of the product. The Siemens Process Simulate: TECNOMATIX, was used to create the DT of the system. The system is modeled in a CAD program and then imported into TECNOMATIX Process Simulate, where it is programmed to replicate the processes.

Requirements and Concept for a Manufacturing Service Management and Execution Platform for Customizable Products

2013 ◽  
Author(s):  
Ursula Rauschecker ◽  
Matthias Stöhr ◽  
Daniel Schel
Keyword(s):  
Flexible Manufacturing ◽  
Service Management ◽  
Cloud Manufacturing ◽  
Use Cases ◽  
It Infrastructure ◽  
Production Networks ◽  
Special Regard ◽  
Execution Platform ◽  
Manufacturing Resources ◽  
Internet Marketplace

Cloud manufacturing provides solutions for a number of tasks concerning the integration of manufacturing resources and production networks. Through it, new possibilities also arise for increasing product individualization. The paper describes how cloud manufacturing concepts allow an Internet marketplace to be established for flexible manufacturing services, which can be used to provide customized products. To do this, first of all use cases related to an appropriate IT infrastructure are analyzed with special regard to the management of manufacturing services which are used to represent manufacturing resources from a technical, financial, logistical, and contractual perspective. Furthermore, requirements on the platform which have to be fulfilled during execution of manufacturing services in a manufacturing cloud are explained and concepts and an architecture for realization of both are described.

Feature-Based Adaptive Manufacturing Equipment Control for Cloud Environments

2016 ◽  
Author(s):  
Göran Adamson ◽  
Lihui Wang ◽  
Magnus Holm ◽  
Philip Moore
Keyword(s):  
Resource Sharing ◽  
Service Management ◽  
Cloud Service ◽  
Cloud Manufacturing ◽  
Manufacturing Equipment ◽  
Control Approach ◽  
Function Block ◽  
Feature Based ◽  
Cloud Environments ◽  
Manufacturing Resources

The ideas of on-demand, scalable and pay-for-usage resource-sharing in Cloud Manufacturing are steadily attracting more interest. For implementing the concept of Manufacturing-as-a-Service in a cloud environment, description models and implementation language for resources and their capabilities are required. A standardized approach for systemized virtualization, servisilisation, retrieval, selection and composition into higher levels of functionality is necessary. For the collaborative sharing and use of networked manufacturing resources there is also a need for a control approach for distributed manufacturing equipment. In this paper, the technological perspective for an adaptive cloud service-based control approach is described, and a supporting information model for its implementation. The control is realized through the use of a network of intelligent and distributable Function Block decision modules, enabling run-time manufacturing activities to be performed according to actual manufacturing conditions. The control system’s integration to the cloud service management functionality is described, as well as a feature-level capability model and the use of ontologies and the Semantic Web.

Stochastic Model of Train Running Time and Arrival Delay: A Case Study of Wuhan–Guangzhou High-Speed Rail

2018 ◽  
Vol 2672 (10) ◽  
pp. 215-223 ◽  
Author(s):  
Javad Lessan ◽  
Liping Fu ◽  
Chao Wen ◽  
Ping Huang ◽  
Chaozhe Jiang
Keyword(s):  
High Speed ◽  
Service Management ◽  
Empirical Distribution ◽  
Distribution Models ◽  
High Speed Rail ◽  
Proposed Model ◽  
Train Operation ◽  
Individual Track

Train operations are subject to stochastic variations, reducing service punctuality and thus the quality of service (QoS). Models of such variations are needed to evaluate and predict the potential impact of disturbances and to avoid service punctuality reduction in train service management and timetabling. In this paper, through a case study of the Wuhan–Guangzhou (WH–GZ) high-speed rail (HSR), we show how a wealth of train operation records can be used to model the stochastic nature of train operations at each level, section and station. Specifically, we examine different distribution models for running times of individual sections and show that the Log-logistic probability density function is the best distributional form to approximate the empirical distribution of running times on the specified line. Next, we show that the distribution of running times in each section can be used to accurately infer arrival delays. Consequently, we construct the underlying analytical model and derive the respective arrival delay distribution at the downstream stations. The results support the correctness of the model presented and show that the proposed model is suitable for constructing the distribution of arrival delays at every station of the specified line. We show that the integrated distribution models of running times and arrival delays, driven by empirical data, can also be used to evaluate the QoS at individual track sections.

scholarly journals Smart cyber-physical production system enabled workpiece production in digital twin job shop

2021 ◽  
Vol 13 (9) ◽  
pp. 168781402110408
Author(s):  
Wang Chuang ◽  
Zhou Guanghui ◽  
Wu Junsheng
Keyword(s):  
Production System ◽  
Industry 4.0 ◽  
Job Shop ◽  
Interaction Model ◽  
Machining Processes ◽  
Digital Twin ◽  
Future Production ◽  
Manufacturing Resources ◽  
Level Production ◽  
Process Level

Industry 4.0 describes the future production of workpiece in job shop as: the workpiece is a smart one; it knows the details of how to manufacture itself; and it can communicate with manufacturing environment to support its own machining processes. This means that the production of workpiece places more emphasis on the smart realization of the process level in Industry 4.0. However, how to implement the production scenario based on existing technologies has not yet been well studied. On account of this, this article aims to study how to use existing technologies in job shop such as digital twins, Internet of Things (IoT), Cyber-physical Production System (CPPS), etc., to realize the workpiece-driven process-level production. The process-level production of a workpiece is divided into three stages according to the different manufacturing resources involved. On this basis, the production of the workpiece in digital twin job shop is divided into process level, operation level, and IoT/sensor level. Firstly, the manufacturing requirements at process level are generated according to production planning and process sheet. And these requirements are written into RFID tag of the workpiece. The workpiece dynamically interacts with different workstations via RFID reader/antenna in order to complete the manufacturing requirements. Secondly, based on the tag data, the interaction model of operation level, and IoT/sensor level CPPSs is given. Thirdly, at IoT/sensor level, the RFID devices are treated as a CPPS to track the manufacturing resources. And different smart sensors are used as independent sensor CPPSs to monitor the running status of machine tool. The RFID and sensor CPPSs are triggered by operation level CPPSs. Finally, a digital twin job shop is taken as an example to illustrate the feasibility of the proposed models and methods.

scholarly journals A Forager Adjustment Strategy Used by the Bees Algorithm for Solving Optimization Problems in Cloud Manufacturing

2015 ◽  
Author(s):  
Yongquan Xie ◽  
Zude Zhou ◽  
Duc Truong Pham ◽  
Quan Liu ◽  
Wenjun Xu ◽  
...  
Keyword(s):  
Service Management ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Cloud Manufacturing ◽  
Bees Algorithm ◽  
Function Evaluation ◽  
Adjustment Strategy ◽  
Optimal Service ◽  
Intelligent Tools

Intelligent technologies have become increasingly important in manufacturing nowadays. Optimal service management and allocation in current cloud manufacturing model are impossible without applications of appropriate intelligent tools. The Bees Algorithm (BA) is a swarm-based intelligent optimizer that provides support for smart decision-making process in manufacturing models. A novel forager adjustment strategy (FAS) is proposed in this paper to manage the forager division in the algorithm, so as to make the entire colony perform with higher efficiency. The proposed FAS based Bees Algorithm (FAS-BA) is able to realize flexible allocation of its forager resources between different roles in accordance with the solution fitness sampled by current scout population. The proposed algorithm is presented in detail. Experiments are conducted based on a set of well-known benchmark functions and a case study. Comparisons between FAS-BA and an improved Bees Algorithm are made to highlight the effectiveness of FAS. The results demonstrate that the proposed algorithm requires less function evaluation cost than the improved version but is capable of obtaining at least the same optimal solution to a problem.

scholarly journals Study on Resource Configuration on Cloud Manufacturing

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Yanlong Cao ◽  
Zijian Wu ◽  
Qijian Zhao ◽  
Huiwen Yan ◽  
Jiangxin Yang
Keyword(s):  
Manufacturing Process ◽  
Cloud Manufacturing ◽  
Geometric Constraints ◽  
Configuration Model ◽  
Growing Up ◽  
Resource Configuration ◽  
Proposed Model ◽  
Manufacturing Platform ◽  
Numerical Case Study

The purpose of manufacturing is to realize the requirement of customer. In manufacturing process of cloud system, there exist a lot of resource services which have similar functional characteristics to realize the requirement. It makes the manufacturing process more diverse. To develop the quality and reduce cost, a resource configuration model on cloud-manufacturing platform is put forward in this paper. According to the generalized six-point location principle, a growth design from the requirement of customers to entities with geometric constraints is proposed. By the requirement growing up to product, a configuration process is used to match the entities with the instances which the resources in the database could supply. Different from most existing studies, this paper studies the tolerance design with multiple candidate resource suppliers on cloud manufacturing to make the market play a two-level game considering the benefit of customers and the profit of resources to give an optimal result. A numerical case study is used to illustrate the proposed model and configuration process. The performance and advantage of the proposed method are discussed at the end.

Rough set based optimization method of machine tools in cloud manufacturing

2018 ◽  
Vol 09 (01) ◽  
pp. 1750058
Author(s):  
Song Huang ◽  
Chao Yin ◽  
Xiaobin Li ◽  
Fei Liu
Keyword(s):  
Cloud Computing ◽  
Rough Set ◽  
Machine Tools ◽  
Optimization Method ◽  
Cloud Manufacturing ◽  
Cloud Environment ◽  
Optimization Criteria ◽  
Key Technologies ◽  
Manufacturing Resources

Cloud Manufacturing (CMfg), combining with the technologies of Cloud computing and Internet of Things, is an intelligent networked manufacturing model, which can quickly integrate various distributed manufacturing resources for collaboratively completing the complex and customized manufacturing tasks. One of the key technologies supporting this model is the optimal manufacturing resources in the CMfg systems, typically machine tools (MTs). In this paper, the attributes of MTs in cloud environment are analyzed, the constraint relationship between the attributes and the optimization criteria of MTs is established, and an optimization method of MTs based on rough set is proposed. Finally, a case study is discussed to validate the feasibility and effectiveness of the proposed method.

A Cloud Solution for Service Oriented Workshop Management

2021 ◽  
Author(s):  
Xiaobin Li ◽  
Chao Yin
Keyword(s):  
Cloud Service ◽  
Cloud Manufacturing ◽  
Heterogeneous Data ◽  
Heterogeneous Data Integration ◽  
Resource Virtualization ◽  
Service Oriented ◽  
Manufacturing Resource ◽  
Manufacturing Resources ◽  
Water Gas

Abstract Cloud manufacturing is a state-of-art networked manufacturing model with the idea and technologies of cloud computing to transform traditional production-oriented manufacturing into service-oriented manufacturing. This emerging model can make manufacturing resources, in a manner similar to traditional utilities such as water, gas and electricity, available (offered) over the internet as convenient, scalable, on-demand services to enterprises. The aim is to improve the sharing efficiency of manufacturing resources and reduce manufacturing costs in industries. In this paper, the current research of cloud manufacturing is summarized, including relevant theories, technologies and applications. A cloud solution for workshop management is proposed from a service perspective, along with its architecture and business process. The methodologies, including manufacturing resource virtualization and workshop sensor network configuration are developed to support heterogeneous data integration and effective collaboration among services in cloud. A case study is demonstrated and discussed to validate the proposed cloud service system.

scholarly journals Modeling of Emergency Supply Scheduling Problem Based on Reliability and Its Solution Algorithm under Variable Road Network after Sudden-Onset Disasters

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15 ◽  
Author(s):  
Tinggui Chen ◽  
Shiwen Wu ◽  
Jianjun Yang ◽  
Guodong Cong ◽  
Gongfa Li
Keyword(s):  
Road Network ◽  
Road Networks ◽  
Sudden Onset ◽  
The Road ◽  
Bee Colony ◽  
Proposed Model ◽  
Effectiveness And Efficiency ◽  
In Transit ◽  
Emergency Supply

It is common that many roads in disaster areas are damaged and obstructed after sudden-onset disasters. The phenomenon often comes with escalated traffic deterioration that raises the time and cost of emergency supply scheduling. Fortunately, repairing road network will shorten the time of in-transit distribution. In this paper, according to the characteristics of emergency supplies distribution, an emergency supply scheduling model based on multiple warehouses and stricken locations is constructed to deal with the failure of part of road networks in the early postdisaster phase. The detailed process is as follows. When part of the road networks fail, we firstly determine whether to repair the damaged road networks, and then a model of reliable emergency supply scheduling based on bi-level programming is proposed. Subsequently, an improved artificial bee colony algorithm is presented to solve the problem mentioned above. Finally, through a case study, the effectiveness and efficiency of the proposed model and algorithm are verified.

Sign in / Sign up

Export Citation Format

Share Document