A Unified Sustainable Manufacturing Capability Model for Representing Industrial Robot Systems in Cloud Manufacturing

被引：5

作者：

Wu, Xingxing ^{[1
,2
]}

Jiang, Xuemei ^{[1
,2
]}

Xu, Wenjun ^{[1
,2
]}

Ai, Qingsong ^{[1
,2
]}

Liu, Quan ^{[1
,2
]}

机构：

[1] Wuhan Univ Technol, Sch Informat Engn, Wuhan 430070, Peoples R China

[2] Minist Educ, Key Lab Fiber Sensing Technol & Informat Proc, Wuhan, Peoples R China

来源：

ADVANCES IN PRODUCTION MANAGEMENT SYSTEMS: INNOVATIVE PRODUCTION MANAGEMENT TOWARDS SUSTAINABLE GROWTH (AMPS 2015), PT II | 2015年 / 460卷

关键词：

Industrial robot systems; Sustainable manufacturing capability; Unified model; Ontology;

D O I：

10.1007/978-3-319-22759-7_45

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Nowadays, the sustainable manufacturing capability of manufacturing devices has attracted more and more attention from academia and industry, in order to coordinate the conflicts between serious environmental impacts and economic benefits. As one kind of advanced manufacturing devices with intelligence, the industrial robot (IR) is an important driving force to make the production activities more efficient, safe and sustainable. A unified sustainable manufacturing capability model for representing IR systems in cloud manufacturing based on ontology was proposed in this paper, so as to solve the description problems in terms of the various capabilities of IR systems, and also to facilitate the factories to effectively manage the IR systems' manufacturing activities during the whole production life-cycle. The case study and its implementation show the developed ontology model is suitable for all types of IR systems, and can comprehensively reflects their sustainable manufacturing capabilities in real-time.

引用

页码：388 / 395

页数：8

共 22 条

[21] ManuService ontology: a product data model for service-oriented business interactions in a cloud manufacturing environment [J].

Yuqian Lu ;

Hongqiang Wang ;

Xun Xu .

Journal of Intelligent Manufacturing, 2019, 30 :317-334

[22] Information Model to Advance Explainable AI-Based Decision Support Systems in Manufacturing System Design [J].

Cochran, David S. ;

Smith, Joseph ;

Mark, Benedikt G. ;

Rauch, Erwin .

MANAGING AND IMPLEMENTING THE DIGITAL TRANSFORMATION, ISIEA 2022, 2022, 525 :49-60

← 1 2 3 →