Manufacturing Resource Ontology Modeling Techniques Based on Carrier

2013 ◽  
Vol 774-776 ◽  
pp. 1382-1385
Author(s):  
Xin Shang ◽  
Jing Liu
Keyword(s):  
Product Development ◽  
Development Time ◽  
Modeling Method ◽  
Resource Integration ◽  
Ontology Modeling ◽  
Modeling Techniques ◽  
Packaging Machine ◽  
Manufacturing Resource ◽  
Manufacturing Resources ◽  
Resources Sharing

Aimed at The key problem for manufacturing resource integration and retrieval of the networked manufacturing resources sharing and reuse, by analyzing the characteristics of the enterprise manufacturing resource and ontology,OWL ontology modeling method based on manufacturing resource carrier was proposed,manufacturing resource was integrated and index by modules; so, manufacturing resource was network reused and shared by carrier was evaluated and index, product development time was reduced, competitiveness was enhance. Finally, wrapper module of paper packaging machine as an example to proves the feasibility of the method.

Reconfigurable Manufacturing Resource Modeling Technology

2011 ◽  
Vol 341-342 ◽  
pp. 364-368
Author(s):  
Bei Rong Zheng ◽  
Wei Xue ◽  
Xiao Wen Xie
Keyword(s):  
Strong Support ◽  
Reconfigurable Manufacturing ◽  
New Production ◽  
Modeling Technology ◽  
Resource Modeling ◽  
Paper Manufacturing ◽  
Ontology Modeling ◽  
Manufacturing Resource ◽  
Manufacturing Resources ◽  
Resource Description

How to model reconfigurable resources, how to make manufacturing resources to rapidly form into new production functions and capability is an unsolved problem. Based on current manufacturing resource knowledge representation, in the paper, manufacturing resource ontology modeling method is presented which has considered characteristics of reconfigurable manufacturing system (RMS), configuration changeable and system adjustable. Furthermore, resource description ontology is defined and example model is also presented; after formalize the concepts, attributes and relations of ontology, the knowledge base including ontology base, attribute base and relation base are constructed. The method proposed can provide strong support for reconfiguration and deformation of RMS.

Research on Manufacturing Resource Domain Ontology Modeling Method

2012 ◽  
Vol 220-223 ◽  
pp. 3058-3063
Author(s):  
Zhong Liu ◽  
Xian Guo Yan ◽  
Hong Guo ◽  
Shi Su ◽  
Chang Gui Xu ◽  
...  
Keyword(s):  
Search Engine ◽  
Recall Rate ◽  
Domain Ontology ◽  
Modeling Method ◽  
Model Description ◽  
Networked Manufacturing ◽  
Paper Model ◽  
Ontology Modeling ◽  
General Search ◽  
Manufacturing Resource

Retrieval, recall rate and precision are not acceptable according to the general search engine in searching networked manufacturing resource. Manufacturing Resource Domain Ontology was derived and established based on the concept of the ontology in this paper. Model description was completed with protégé.

Deep learning and complex network theory based analysis on socialized manufacturing resources utilisations and an application case study

2021 ◽  
pp. 1063293X2110031
Author(s):  
Maolin Yang ◽  
Auwal H Abubakar ◽  
Pingyu Jiang
Keyword(s):  
Deep Learning ◽  
Complex Network ◽  
Network Models ◽  
Neural Network Models ◽  
Complex Network Theory ◽  
New Type ◽  
Manufacturing Resource ◽  
Manufacturing Resources ◽  
Resource Characteristics

Social manufacturing is characterized by its capability of utilizing socialized manufacturing resources to achieve value adding. Recently, a new type of social manufacturing pattern emerges and shows potential for core factories to improve their limited manufacturing capabilities by utilizing the resources from outside socialized manufacturing resource communities. However, the core factories need to analyze the resource characteristics of the socialized resource communities before making operation plans, and this is challenging due to the unaffiliated and self-driven characteristics of the resource providers in socialized resource communities. In this paper, a deep learning and complex network based approach is established to address this challenge by using socialized designer community for demonstration. Firstly, convolutional neural network models are trained to identify the design resource characteristics of each socialized designer in designer community according to the interaction texts posted by the socialized designer on internet platforms. During the process, an iterative dataset labelling method is established to reduce the time cost for training set labelling. Secondly, complex networks are used to model the design resource characteristics of the community according to the resource characteristics of all the socialized designers in the community. Two real communities from RepRap 3D printer project are used as case study.

Research on Mass Manufacturing Resource Sensory Data Management Based on Hadoop

2016 ◽  
Vol 693 ◽  
pp. 1880-1885 ◽  
Author(s):  
Kai Kai Su ◽  
Wen Sheng Xu ◽  
Jian Yong Li
Keyword(s):  
Distributed Storage ◽  
Cloud Manufacturing ◽  
Distributed File System ◽  
Service Platform ◽  
Sensory Data ◽  
Computing Model ◽  
Hadoop Distributed File System ◽  
Manufacturing Resource ◽  
Manufacturing Resources ◽  
Management Method

Aiming at the management issue of mass sensory data from the manufacturing resources in cloud manufacturing, a management method for mass sensory data based on Hadoop is proposed. Firstly, characteristics of sensory data in cloud manufacturing are analyzed, meanings and advantages of Internet of Things and cloud computing are elaborated. Then the structure of the cloud manufacturing service platform is proposed based on Hadoop, the information model of manufacturing resources in cloud manufacturing is defined, and the data cloud in the cloud manufacturing service platform is designed. The distributed storage of mass sensory data is implemented and a universal distributed computing model of mass sensory data is established based on the characteristics of Hadoop Distributed File System (HDFS).

Application of a Genetic Algorithm to Concept Variant Selection

2006 ◽  
Author(s):  
Ryan S. Hutcheson ◽  
Robert L. Jordan ◽  
Robert B. Stone ◽  
Janis P. Terpenny ◽  
Xiaomeng Chang
Keyword(s):  
Genetic Algorithm ◽  
Product Development ◽  
Solution Space ◽  
Variant Selection ◽  
Functional Modeling ◽  
Detailed Design ◽  
Modeling Techniques ◽  
Break Even Point ◽  
Future Work ◽  
Selection Of

This paper outlines a framework for applying a genetic algorithm to the selection of component variants between the conceptual and detailed design stages of product development. A genetic algorithm (GA) is defined for the problem and an example is presented that demonstrates its application and usefulness. Functional modeling techniques are used to formulate the design problem and generate the chromosomes that are evaluated with the algorithm. In the presented example, suitable GA parameters and the break-even point where the GA surpassed an enumerated search of the same solution space were found. Recommend uses of the GA along with limitations of the method and future work are presented as well.

Defining Product Architecture During Conceptual Design

1998 ◽  
Author(s):  
Kevin R. Allen ◽  
Susan Carlson-Skalak
Keyword(s):  
Life Cycle ◽  
Product Development ◽  
Conceptual Design ◽  
Development Time ◽  
Product Variety ◽  
Product Architecture ◽  
Small Company ◽  
Product Lines ◽  
Modular Product ◽  
Modular Products

Abstract Product architecture can have a significant impact on a product’s life-cycle and its development time. Modular product architecture allows for easy disassembly upon product retirement and allows for wide product variety. In a small company, the team structure of the company can correspond to the modules, and modules can be used across product lines. By using similar modules from one generation to the next, product development time can be reduced. The methodology described in this paper gives a small company the framework from which to develop modular products.

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