Interoperability in Cloud Manufacturing and Practice on Private Cloud Structure for SMEs

Author(s):  
Xi Vincent Wang ◽  
Lihui Wang

In recent years, Cloud manufacturing has become a new research trend in manufacturing systems leading to the next generation of production paradigm. However, the interoperability issue still requires more research due to the heterogeneous environment caused by multiple Cloud services and applications developed in different platforms and languages. Therefore, this research aims to combat the interoperability issue in Cloud Manufacturing System. During implementation, the industrial users, especially Small- and Medium-sized Enterprises (SMEs), are normally short of budget for hardware and software investment due to financial stresses, but they are facing multiple challenges required by customers at the same time including security requirements, safety regulations. Therefore in this research work, the proposed Cloud manufacturing system is specifically tailored for SMEs.

2018 ◽  
Vol 25 (1) ◽  
pp. 280-296 ◽  
Author(s):  
Ram Prakash ◽  
Sandeep Singhal ◽  
Ashish Agarwal

Purpose The research paper presents analysis and prioritization of barriers influencing the improvement in the effectiveness of manufacturing system. The purpose of this paper is to develop an integrated fuzzy-based multi-criteria decision-making (F-MCDM) framework to assist management of the case company in the selection of most effective manufacturing system. The framework helps in prioritizing the manufacturing systems on the basis of their effectiveness affected by the barriers. Design/methodology/approach In this paper, on the basis of experts’ opinion, five barriers have been identified in a brain-storming session. The problem of prioritization of manufacturing system is a multi-criteria decision-making (MCDM) problem and hence is solved by using the F-MCDM approach using dominance matrix. Findings Manufacturing systems’ effectiveness for Indian industries is influenced by barriers. The prioritization of manufacturing systems depends on qualitative factor decision-making criteria. Among the manufacturing systems, leagile manufacturing system is given the highest priority followed by lean manufacturing system, agile manufacturing system, flexible manufacturing system and cellular manufacturing system. Research limitations/implications The selection of an appropriate manufacturing system plays a vital role for sustainable growth of the manufacturing company. In the present work, barriers which influence the effectiveness of manufacturing system have been identified. On the basis of degree of influence of barriers on the effectiveness of the manufacturing system, five alternative manufacturing systems are prioritized. The framework will help the management of the case company to take reasonable decision for the adoption of the appropriate manufacturing system. Practical implications The results of the research work are very useful for the manufacturing companies interested in analyzing the alternative manufacturing systems on the basis of their effectiveness and their sensitivity toward various barriers. The management of Indian manufacturing company will take decision to adopt a manufacturing system whose effectiveness is least sensitive toward barriers. Effectiveness of such manufacturing system will improve with time without having retardation due to barriers. With improved effectiveness of the manufacturing system, the manufacturing company would be able to survive with global competition. The result of the present work is based on the inputs from the case company and may vary for the other manufacturing company. In the present work, only five alternative manufacturing systems and five barriers have been considered. To obtain the better result, MCDM approach with more number of alternative manufacturing systems and barriers might be considered. Originality/value The research work is based on the fuzzy analytic hierarchy process framework and on the case study conducted by the authors. The work carried out is original in nature and based on the real-life case study.


2011 ◽  
Vol 314-316 ◽  
pp. 2259-2262 ◽  
Author(s):  
Hua Guo ◽  
Lin Zhang ◽  
Fei Tao

As a new manufacturing paradigm, cloud manufacturing (CMfg) is proposed to realize the added-value and on-demand use of manufacturing resource and ability in the form of manufacturing services. Considering that there always exist correlations among cloud services (CS), which affect the cloud service composition (CSC). Hence, how to mine the correlations among CSs and apply them to CSC is a key issue for realizing the added-value. This paper presents a framework for correlation relationship mining for CSC. Four function modules for mining correlations among CSs are analyzed, and the involving key issues were preliminarily discussed as well.


2016 ◽  
Vol 8 (1) ◽  
pp. 9 ◽  
Author(s):  
Rajeev Kumar

In present market scenario, manufacturing industries need to focus towards capability to provide high product variety and availability of products at the point of demand. This situation creates pressure on manufacturing firms to be flexible and to reduce lead time to fulfill customer's demand on time. Flexible Manufacturing Systems (FMS) with appropriate Routing Flexibility (RF) in addition to different scheduling strategies is the appropriate manufacturing alternative in such a case. Such systems are capable to adjust changing product mix yet providing higher performance in dynamic business environment. This research work presents simulation analysis of a FMS with varying Routing Flexibility (RF) level at different part mix ratio to validate this. The results show that varying part mix ratio has appreciable effect on the system performance, when no routing flexibility is present in the system. Also for all product mix ratios, increase in routing flexibility levels continues to improve MST performance with diminishing return.


2012 ◽  
Vol 622-623 ◽  
pp. 60-63
Author(s):  
Pawan Kumar Arora ◽  
Abid Haleem ◽  
M.K. Singh ◽  
Harish Kumar

Though Cellular Manufacturing System (CMS) has been an active area of research for past few decades, but, still it has not received the requisite attention so far. Despite of a useful manufacturing strategy based on the group technology (GT), it is yet to be established on a larger scale. The CMS allows the grouping of the facilities on the basis of similarity in manufacturing processes and design considerations of the products to be manufactured. A lot of researchers have worked for various developments related to various issues of CMS, but for last decades, the modern optimization tools like genetic algorithm (GA), artificial neural networks (ANN) have changed the scenario and research work has been accelerated related to CMS. The present paper is an attempt to discuss the GA related research work by various researchers for CMS. Research work along with their impact of past researchers has been discussed and reported here.


Author(s):  
Xi Vincent Wang ◽  
Brenda N. Lopez N ◽  
Winifred Ijomah ◽  
Lihui Wang ◽  
Jinhui Li

Waste electrical and electronic equipment (WEEE) is both valuable and harmful since it contains a large number of profitable and hazardous materials and elements at the same time. At component level, many parts of the discarded equipment are still functional and recoverable. Thus, it is necessary to develop a distributed and intelligent system to support WEEE component recovery and recycling. In recent years, the Cloud concept has gained increasing popularity since it provides a service-oriented architecture (SOA) that integrates various resources over the network. Cloud manufacturing systems are proposed worldwide to support operational manufacturing processes. In this research, Cloud manufacturing is further extended to the WEEE recovery and recycling context. The Cloud services are applied in WEEE recovery and recycling processes by tracking and management services. These services include all the stakeholders from the beginning to the end of life of the electric and electronic equipment. A Cloud-based WEEE recovery system is developed to provide modularized recovery services on the Cloud. A data management system is developed as well, which maintains the knowledge throughout the product lifecycle. A product tracking mechanism is also proposed with the help of the Quick Respond code method.


2021 ◽  
Vol 7 ◽  
pp. e743
Author(s):  
Seyyed-Alireza Radmanesh ◽  
Alireza Haji ◽  
Omid Fatahi Valilai

Cloud manufacturing is a new globalized manufacturing framework which has improved the performance of manufacturing systems. The service-oriented architecture as the main idea behind this framework means that all resources and capabilities are considered as services. The agents interact by way of service exchanging, which has been a part of service composition research topics. Service allocations to demanders in a cloud manufacturing system have a dynamic behavior. However, the current research studies on cloud-based service composition are mainly based on centralized global optimization models. Therefore, a distributed deployment and real-time synchronization platform, which enables the globalized collaboration in service composition, is required. This paper proposes a method of using blockchain to solve these issues. Each service composition is considered as a transaction in the blockchain concept. A block includes a set of service compositions and its validity is confirmed by a predefined consensus mechanism. In the suggested platform, the mining role in blockchain is interpreted as an endeavor for proposing the proper service composition in the manufacturing paradigm. The proposed platform has interesting capabilities as it can increase the response time using the blockchain technology and improve the overall optimality of supply-demand matching in cloud manufacturing. The efficiency of the proposed model was evaluated by investigating a service allocation problem in a cloud manufacturing system in four large scale problems. Each problem is examined in four centralized modes, two, three and four solvers in blockchain-based model. The simulation results indicate the high quality of the proposed solution. The proposed solution will lead to at least 15.14% and a maximum of 34.8 percent reduction in costs and 20 to 68.4 percent at the solving time of the problem. It is also observed that with increasing the number of solvers (especially in problems with larger dimensions) the solution speed increases sharply (more than 68% improvement in some problems), which indicates the positive effect of distribution on reducing the problem-solving time.


2018 ◽  
Vol 1 (1) ◽  
pp. 673-680
Author(s):  
Julia Siderska ◽  
Khambi Mubarok

Abstract Cloud Manufacturing (CMfg) is an emerging networked manufacturing paradigm and service-oriented manufacturing model, in which distributed manufacturing resources are made available by providers according to consumers’ requirements as on-demand manufacturing services via networks (manufacturing clouds) and cloud manufacturing service platforms. Considering the concept of cloud manufacturing and its operation principle, the paper designs and introduces new proposal of cloud manufacturing service platform architecture, including the following six layers: physical resources layer, soft resources layer, virtual resources layer, services layer, application layer and service-oriented interface layer. After surveying majority of papers introducing architectures of CMfg service platforms, the paper recommends to map both hard as well as soft resources into cloud services from resource layers to virtual resource layer which highlights the core idea of the concept. The paper discusses also the fundamentals of the CMfg paradigm, introduces three groups of actors that participate in a cloud manufacturing system, as well as indicates briefly a typical hierarchy architecture of cloud manufacturing system.


ACTA IMEKO ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 39
Author(s):  
Mariorosario Prist ◽  
Andrea Monteriù ◽  
Emanuele Pallotta ◽  
Paolo Cicconi ◽  
Alessandro Freddi ◽  
...  

The pillars of Industry 4.0 require the integration of a modern smart factory, data storage in the Cloud, access to the Cloud for data analytics, and information sharing at the software level for simulation and hardware-in-the-loop (HIL) capabilities. The resulting cyber-physical system (CPS) is often termed the cyber-physical manufacturing system, and it has become crucial to cope with this increased system complexity and to attain the desired performances. However, since a great number of old production systems are based on monolithic architectures with limited external communication ports and reduced local computational capabilities, it is difficult to ensure such production lines are compliant with the Industry 4.0 pillars. A wireless sensor network is one solution for the smart connection of a production line to a CPS elaborating data through cloud computing. The scope of this research work lies in developing a modular software architecture based on the open service gateway initiative framework, which is able to seamlessly integrate both hardware and software wireless sensors, send data into the Cloud for further data analysis and enable both HIL and cloud computing capabilities. The CPS architecture was initially tested using HIL tools before it was deployed within a real manufacturing line for data collection and analysis over a period of two months.


2019 ◽  
Vol 277 ◽  
pp. 01005
Author(s):  
Qingqing Yang ◽  
Jia Liu ◽  
Kewei Yang

In the cloud manufacturing systems, both manufacturing tasks and manufacturing services are in a dynamic environment. How could cloud manufacturing platform optimizes manufacturing cloud services based on QoS, matching an optimal service composition for manufacturing tasks has become an urgent problem at present. In view of this problem, we study the matching of manufacturing tasks and manufacturing services from the perspective of complex network theory. On the basis of manufacturing task network and manufacturing service network, a dynamic matching network theory model of manufacturing task-service is constructed. And then, we take a dynamic assessment of QoS. Finally, we use load and dynamic QoS as the optimization objectivities, transform the optimal manufacturing service composition problem into the shortest path problem, and the dynamic scheduling of manufacturing services is realized.


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