Dynamic Modeling of Manufacturing Equipment Capability Using Condition Information in Cloud Manufacturing

Author(s):  
Wenjun Xu ◽  
Jiajia Yu ◽  
Zude Zhou ◽  
Yongquan Xie ◽  
Duc Truong Pham ◽  
...  

There is a growing need of knowledge description of manufacturing equipment and their capabilities for users, in order to efficiently obtain the on-demand services of manufacturing equipment in cloud manufacturing, and the understanding of the manufacturing capability of equipment is the most important basis for optimizing the cloud service management. During the manufacturing processes, a number of uncertain incidents may occur, which could degrade the manufacturing system performance or even paralyze the production line. Hence, all aspects about the equipment should be reflected within the knowledge description, and the static and dynamic information are both included in the knowledge model of manufacturing equipment. Unification and dynamics are the most important characteristics of the framework of knowledge description. The primary work of this study is fourfold. First, three fundamental ontologies are built, namely, basic information ontology, functional ontology, and manufacturing process ontology. Second, the correlation between the equipment ontology and the fundamental ontology that forms the unified description framework is determined. Third, the mapping relationship between the real-time condition data and the model of manufacturing equipment capability ontology is established. On the basis of the mapping relationship, the knowledge structure of the manufacturing equipment capability ontology is able to update in real-time. Finally, a prototype system is developed to validate the feasibility of the proposed dynamic modeling method. The system implementation demonstrates that the proposed knowledge description framework and method are capable of reflecting the current conditions and the dynamic capability of manufacturing equipment.

Author(s):  
Göran Adamson ◽  
Lihui Wang ◽  
Magnus Holm ◽  
Philip Moore

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.


2013 ◽  
Vol 760-762 ◽  
pp. 1758-1761
Author(s):  
Ji Ming Lan ◽  
Shu Jie Lu ◽  
Li Ming Zhang

Proposed that idea of cloud computing ecology development, supports and guiding cloud model deployment, the cloud service management and Clouds protocols observes the purification of mix cloud environment. Has designed the multiple dimension data saving structure and real-time mass-data processing of model as well as the asynchronous overall construction distributional ecology cloud structure. It has been shown that this ecology cloud structure is healthy.


Author(s):  
Jiajia Yu ◽  
Zude Zhou ◽  
Wenjun Xu

In order to efficiently obtain the on-demand services of manufacturing equipment resources in terms of the customers’ requirements, the manufacturing equipment resources together with their capability should be searched and matched during the production processes in Cloud Manufacturing. In this context, the issue of how to realize the dynamic formal description of manufacturing equipment capability becomes a basis to support such manufacturing activities. In this paper, the manufacturing equipment capability was modeled dynamically using OWL (Web Ontology Language), based on the mapping relation which associates manufacturing equipment capability attributes and real-time data. The semantic relationship between concepts in the ontology model can be explicitly and implicitly reflected using the OWL which makes it convenient to discover, share and reuse the manufacturing equipment capability. The effectiveness of the proposed modeling method was validated by a case study, and the results showed that the dynamic modeling method could reflect current state and dynamic capability of manufacturing equipment resources.


2019 ◽  
Vol 11 (9) ◽  
pp. 2619 ◽  
Author(s):  
Wei He ◽  
Guozhu Jia ◽  
Hengshan Zong ◽  
Jili Kong

Service management in cloud manufacturing (CMfg), especially the service selection and scheduling (SSS) problem has aroused general attention due to its broad industrial application prospects. Due to the diversity of CMfg services, SSS usually need to take into account multiple objectives in terms of time, cost, quality, and environment. As one of the keys to solving multi-objective problems, the preference information of decision maker (DM) is less considered in current research. In this paper, linguistic preference is considered, and a novel two-phase model based on a desirable satisfying degree is proposed for solving the multi-objective SSS problem with linguistic preference. In the first phase, the maximum comprehensive satisfying degree is calculated. In the second phase, the satisfying solution is obtained by repeatedly solving the model and interaction with DM. Compared with the traditional model, the two-phase is more effective, which is verified in the calculation experiment. The proposed method could offer useful insights which help DM balance multiple objectives with linguistic preference and promote sustainable development of CMfg.


2017 ◽  
Vol 13 (7) ◽  
pp. 155014771772181 ◽  
Author(s):  
Seok-Woo Jang ◽  
Gye-Young Kim

This article proposes an intelligent monitoring system for semiconductor manufacturing equipment, which determines spec-in or spec-out for a wafer in process, using Internet of Things–based big data analysis. The proposed system consists of three phases: initialization, learning, and prediction in real time. The initialization sets the weights and the effective steps for all parameters of equipment to be monitored. The learning performs a clustering to assign similar patterns to the same class. The patterns consist of a multiple time-series produced by semiconductor manufacturing equipment and an after clean inspection measured by the corresponding tester. We modify the Line, Buzo, and Gray algorithm for classifying the time-series patterns. The modified Line, Buzo, and Gray algorithm outputs a reference model for every cluster. The prediction compares a time-series entered in real time with the reference model using statistical dynamic time warping to find the best matched pattern and then calculates a predicted after clean inspection by combining the measured after clean inspection, the dissimilarity, and the weights. Finally, it determines spec-in or spec-out for the wafer. We will present experimental results that show how the proposed system is applied on the data acquired from semiconductor etching equipment.


Sign in / Sign up

Export Citation Format

Share Document