Hybrid sensing-based approach for the monitoring and maintenance of shared manufacturing resources

2021 ◽  
pp. 1-19
Author(s):  
Geng Zhang ◽  
Chun-Hsien Chen ◽  
Bufan Liu ◽  
Xinyu Li ◽  
Zuoxu Wang
Keyword(s):  
Manufacturing Resources

scholarly journals Cloud-Edge Collaboration-Based Knowledge Sharing Mechanism for Manufacturing Resources

2021 ◽  
Vol 11 (7) ◽  
pp. 3188
Author(s):  
Xixiang Wang ◽  
Jiafu Wan
Keyword(s):  
Knowledge Sharing ◽  
Comparative Experiment ◽  
Mixed Flow ◽  
Processing Technologies ◽  
Experiment Platform ◽  
Low Degree ◽  
Manufacturing Resources ◽  
Ontological Model ◽  
Manufacturing Environments ◽  
Similar Processing

The development of multi-variety, mixed-flow manufacturing environments is hampered by a low degree of automation in information and empirical parameters’ reuse among similar processing technologies. This paper proposes a mechanism for knowledge sharing between manufacturing resources that is based on cloud-edge collaboration. The manufacturing process knowledge is coded using an ontological model, based on which the manufacturing task is refined and decomposed to the lowest-granularity concepts, i.e., knowledge primitives. On this basis, the learning process between devices is realized by effectively screening, matching, and combining the existing knowledge primitives contained in the knowledge base deployed on the cloud and the edge. The proposed method’s effectiveness was verified through a comparative experiment contrasting manual configuration and knowledge sharing configuration on a multi-variety, small-batch manufacturing experiment platform.

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 Span of Manufacturing Task with Time-Sequence Constraint for Manufacturing Collaborative Alliances

2012 ◽  
Vol 201-202 ◽  
pp. 975-978
Author(s):  
Hong Fei Wang
Keyword(s):  
Quantitative Methods ◽  
Time Sequence ◽  
Influential Factors ◽  
Qualitative And Quantitative Methods ◽  
Sequence Constraint ◽  
Manufacturing Resources ◽  
Production Period ◽  
The Relationship ◽  
Task Span ◽  
Collaborative Alliances

For the manufacturing task of manufacturing collaborative alliances, the relationship between manufacturing task programming and manufacturing resources deployment is analyzed and the model for the span of manufacturing task with time sequence constraint is constructed. The problem of span programming of manufacturing task with time sequence constraint is analyzed by integrating qualitative and quantitative methods from production period for the manufacturing task. The mathematical formulations of influential factors and task span are constructed, and the optimal values of task span are obtained. By analyzing the results of quasi-quantitative study, some meaningful results that benefit to programming for collaborative manufacturing task are achieved.

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).

scholarly journals The development of an ontology for describing the capabilities of manufacturing resources

2018 ◽  
Vol 30 (2) ◽  
pp. 959-978 ◽  
Author(s):  
Eeva Järvenpää ◽  
Niko Siltala ◽  
Otto Hylli ◽  
Minna Lanz
Keyword(s):  
Manufacturing Resources

Cloud Manufacturing Resource Management Based on Metadata

2015 ◽  
Author(s):  
Chun Zhao ◽  
Lin Zhang ◽  
Xuesong Zhang ◽  
Liang Zhang
Keyword(s):  
Resource Management ◽  
Cloud Manufacturing ◽  
State Monitoring ◽  
Knowledge Resources ◽  
Centralized Management ◽  
Service Oriented ◽  
Manufacturing Resources ◽  
Resource Data ◽  
Manufacturing Platform ◽  
Management Approaches

Centralized management and sharing of manufacturing resources is one of the important functions of cloud manufacturing platform. There are many kinds of manufacturing resources, centralized management, optimized scheduling, quick searching for various manufacturing resources become important issues in a cloud manufacturing platform. This paper presents a resource management model based on metadata to realize the access and unified management of the hardware resources, software resources and knowledge resources. Two management approaches respectively for static and dynamic resource data are introduced to realize resource state monitoring and real-time information collecting. On this basis, the relationship between static and dynamic data is determined and service-oriented of resources is realized.

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