Common engines of cloud manufacturing service platform for SMEs

Ting-xin, Song; Liu, Huimin; Chun-mei, Wei; Zhang, Chenglei

doi:10.1007/s00170-014-5863-y

Cited by 41 publications

(15 citation statements)

References 16 publications

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“…The development of the cloud manufacturing service platform provides technical support. A cloud manufacturing service platform for small-and medium-sized enterprises, which implements semantic intelligence search, order tracking, event task guidance, and collaborative management, was developed [235].…”

Section: Collaborative Manufacturingmentioning

confidence: 99%

Digital twin-based sustainable intelligent manufacturing: a review

2020

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As the next-generation manufacturing system, intelligent manufacturing enables better quality, higher productivity, lower cost, and increased manufacturing flexibility. The concept of sustainability is receiving increasing attention, and sustainable manufacturing is evolving. The digital twin is an emerging technology used in intelligent manufacturing that can grasp the state of intelligent manufacturing systems in real-time and predict system failures. Sustainable intelligent manufacturing based on a digital twin has advantages in practical applications. To fully understand the intelligent manufacturing that provides the digital twin, this study reviews both technologies and discusses the sustainability of intelligent manufacturing. Firstly, the relevant content of intelligent manufacturing, including intelligent manufacturing equipment, systems, and services, is analyzed. In addition, the sustainability of intelligent manufacturing is discussed. Subsequently, a digital twin and its application are introduced along with the development of intelligent manufacturing based on the digital twin technology. Finally, combined with the current status, the future development direction of intelligent manufacturing is presented.

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Section: Collaborative Manufacturingmentioning

confidence: 99%

Digital twin-based sustainable intelligent manufacturing: a review

2020

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“…The matching process of tasks and resources [20,21] is the key link in cloud manufacturing, so the problem of efficiently and accurately matching manufacturing resources for manufacturing tasks is the research focus of this paper. The task complexity of the demand resource is judged by the information constraint of the task requirement.…”

Section: Matching Of Resources and Manufacturing Task Requiring A Single Resourcementioning

confidence: 99%

Matching of Manufacturing Resources in Cloud Manufacturing Environment

Pan

et al. 2021

Symmetry

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With the introduction and application of new information technology in manufacturing, various advanced manufacturing models and national strategies have received more and more attention. The goal of cloud manufacturing is to closely link the resources and capabilities of manufacturers through a variety of services to create a dedicated platform for complex manufacturing process needs. How to achieve effective matching of various manufacturing resources and capabilities in the form of services will be a common problem in the future. In order to effectively improve cloud manufacturing tasks and resource matching efficiency and save resources, this study considers the common aspects of cloud manufacturing resource matching as service quality indicators, and extends the scope to the requirements of manufacturing resources, and the matching pattern of traditional service resources. There are additional restrictions on the resource service matching process. At the same time, the resource service matching is usually asymmetric. Therefore, we introduce the concept of task complexity of demand resources, and propose a combination system based on task complexity and service quality evaluation. The artificial bee colony algorithm (ABC) is used for analysis and verification. The experimental paper further validates the proposed the feasibility and efficiency of the method.

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“…For example, Luo et al [12] studied the formal description of multidimensional information for manufacturing capability in CMfg system, Wang et al [13] discussed standardized data models describing cloud services and relevant features for supporting interoperable CMfg, and Tao et al [14] investigated how to realize manufacturing resource intelligent perception and access IoTbased technologies. At the same time, the architecture, typical characteristics, and key technologies for implementing CMfg platforms were presented in [5,[15][16][17][18][19]. However, most of these researches are at the macro level with less consideration at the micro level for CMfg service composition.…”

Section: Maintenancementioning

confidence: 99%

A hybrid artificial bee colony algorithm for optimal selection of QoS-based cloud manufacturing service composition

Zhou

Yao

2016

Int J Adv Manuf Technol

130

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With the advent of cloud manufacturing (CMfg), more and more services in CMfg platforms may provide the same functionality but differ in performance. In order to insure the manufacturing cloud to match the complicated task requirements, composited CMfg service optimal selection (CCSOS) is becoming increasingly important. This study proposes a new approach for such CCSOS problems, the so-called hybrid artificial bee colony (HABC) algorithm, which employs both the probabilistic model of Archimedean copula estimation of distribution algorithm (ACEDA) and the chaos operators of global best-guided artificial bee colony to generate the offspring individuals with consideration of quality of service (QoS) and CMfg environment. Different-scale CCSOS problems are adopted to evaluate the performance of the proposed HABC. Experimental results have shown that the HABC can find better solutions compared with such algorithms as genetic algorithm, particle swarm optimization, and basic artificial bee colony algorithm.

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Common engines of cloud manufacturing service platform for SMEs

Cited by 41 publications

References 16 publications

Digital twin-based sustainable intelligent manufacturing: a review

Digital twin-based sustainable intelligent manufacturing: a review

Matching of Manufacturing Resources in Cloud Manufacturing Environment

A hybrid artificial bee colony algorithm for optimal selection of QoS-based cloud manufacturing service composition

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