A grey correlation based supply–demand matching of machine tools with multiple quality factors in cloud manufacturing environment

2018 ◽  
Vol 10 (3) ◽  
pp. 1025-1038 ◽  
Author(s):  
Xiaorong Gong ◽  
Chao Yin ◽  
Xiaobin Li
Keyword(s):  
Machine Tools ◽  
Cloud Manufacturing ◽  
Quality Factors ◽  
Manufacturing Environment ◽  
Grey Correlation

An OSGi-Based Adaptation Access of Machine Tools in the Cloud Manufacturing Environment

2021 ◽  
Author(s):  
Xiaobin Li ◽  
Chao Yin
Keyword(s):  
Information Acquisition ◽  
Machine Tools ◽  
Universal Access ◽  
Cloud Manufacturing ◽  
Manufacturing Environment ◽  
Access Method ◽  
Manufacturing Enterprises ◽  
Cloud Application ◽  
Key Enabling Technologies ◽  
Manufacturing Mode

Abstract Machine tools (MTs) are the core manufacturing resources for discrete manufacturing enterprises. In the cloud manufacturing environment, MTs are massive, heterogeneous, widely dispersed and highly autonomous, which makes it difficult for cloud manufacturing mode to be deeply applied to support the networked collaboration operation among manufacturing enterprises. Realizing universal access and cloud application of various MTs is an essential prerequisite to solve the above problem. In this paper, an OSGi-based adaptation access method of MTs is proposed. First, the MTs information description model in the cloud manufacturing environment is built. Then, an OSGi-based adaptation access framework of MTs is constructed, and key enabling technologies, including machine tool information acquisition and processing, Bundle and Subsystem construction, are studied. Finally, an application case is conducted to verify the effectiveness and feasibility of the proposed method.

Research on the Selection Problem of Machine Tools in Cloud Manufacturing Environment

2014 ◽  
Vol 800-801 ◽  
pp. 649-653 ◽  
Author(s):  
Shu Qi Wang ◽  
Yun Xia Jiang ◽  
Min Li Zheng ◽  
Dong Nan Sun ◽  
Xiao Liang Cheng
Keyword(s):  
Genetic Algorithms ◽  
Machine Tools ◽  
Selection Process ◽  
Cloud Manufacturing ◽  
Optimal Selection ◽  
Cloud Environment ◽  
Manufacturing Environment ◽  
On Demand ◽  
Matching Process ◽  
Multi Level

This paper proposes a method to choose machine resources in order to realize the on-demand use of machine resources in cloud manufacturing environment. A convergence mode of the machine resources is described and the selection process is given. A multi-level matching process of machine tools is proposed. Different matching methods are designed for different parameter types of machining tasks and machine resources, and then machine resources are screened according to the requirements of machining tasks to form the machine resources candidate sets. Then a multi-objective optimal selection model of machine resources is constructed, regarding minimization of costs and time and maximization of service quality and reputation as the target, which is solved by using genetic algorithms. Finally, the algorithm is analyzed and validated with an example, and a kind of solution thinking and method is provided to select machine tools in manufacturing cloud environment.

Trust Model Based on Feedback Evaluation in Cloud Manufacturing Environment

2011 ◽  
Vol 308-310 ◽  
pp. 1740-1745 ◽  
Author(s):  
Xiao Lan Xie ◽  
Liang Liu ◽  
Ying Zhong Cao
Keyword(s):  
Cloud Manufacturing ◽  
Trust Model ◽  
Human Society ◽  
Manufacturing Environment ◽  
Trust Mechanism ◽  
Model Based ◽  
Service Behavior ◽  
Relationship Of ◽  
Dynamic Adaptability ◽  
Feedback Evaluation

Aiming at the existing trust issues under manufacturing environment. This paper proposes a trust model based on feedback evaluation, TMBFCM, from the characteristics of human of trust relationship of human society. The model proposed a set of evaluation indicators of cloud manufacturing services properties, introduced the dynamic trust mechanism for attenuation by time,established the service which cloud manufacturing services providers provided and the feedback evaluation and incentive mechanism given by the user of cloud manufacturing service, improved the dynamic adaptability of the model. The results show that, compared with the existing trust model, the evaluation results are closer to the true service behavior of cloud manufacturing services provider, it can resist all kinds of malicious attacks acts effectively, demonstrated good robustness and recognition.

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