A Cloud Manufacturing Architecture for Complex Parts MachiningSource: Journal of Manufacturing Science and Engineering:;2015:;volume( 137 ):;issue: 006::page 61009DOI: 10.1115/1.4029856Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Service provider (SP) knowhows are essential in machining service (MS) encapsulation in the cloud. However, since the acquisition of the knowhows for complex parts machining requires investing considerable manpower and resources in R&D, this kind of machining knowhows is usually considered as one of the core competences of the SP who makes them unshareable. Targeting the problem, this paper presents a new cloud manufacturing (CM) architecture in which MSs are encapsulated within each SP with standardized machining task description strategies (SMTDS). Only the capability information about what the SP can do is provided to the cloud. During service matching, SMTDS is also applied for user request formulation to improve the matching efficiency and quality. For complex parts in large size, high machining requirements, high value, short delivery cycle, and complex structures, e.g., aircraft structural parts, unacceptable machining quality or delivery delay may cause a much greater loss not only in economy. In the proposed CM architecture, to guarantee the feasibility of the MSs for complex structural parts, machining operations for the user preferred services could be generated by mapping the corresponding typical machining plans (TMP) to the part based on the dynamic feature concept to support accurate evaluations of the MSs. The machining of an aircraft structural part is then applied as a test user request to demonstrate how the proposed method works for finding MS for complex parts.
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contributor author | Liu, Xu | |
contributor author | Li, Yingguang | |
contributor author | Wang, Lihui | |
date accessioned | 2017-05-09T01:20:42Z | |
date available | 2017-05-09T01:20:42Z | |
date issued | 2015 | |
identifier issn | 1087-1357 | |
identifier other | manu_137_06_061009.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/158762 | |
description abstract | Service provider (SP) knowhows are essential in machining service (MS) encapsulation in the cloud. However, since the acquisition of the knowhows for complex parts machining requires investing considerable manpower and resources in R&D, this kind of machining knowhows is usually considered as one of the core competences of the SP who makes them unshareable. Targeting the problem, this paper presents a new cloud manufacturing (CM) architecture in which MSs are encapsulated within each SP with standardized machining task description strategies (SMTDS). Only the capability information about what the SP can do is provided to the cloud. During service matching, SMTDS is also applied for user request formulation to improve the matching efficiency and quality. For complex parts in large size, high machining requirements, high value, short delivery cycle, and complex structures, e.g., aircraft structural parts, unacceptable machining quality or delivery delay may cause a much greater loss not only in economy. In the proposed CM architecture, to guarantee the feasibility of the MSs for complex structural parts, machining operations for the user preferred services could be generated by mapping the corresponding typical machining plans (TMP) to the part based on the dynamic feature concept to support accurate evaluations of the MSs. The machining of an aircraft structural part is then applied as a test user request to demonstrate how the proposed method works for finding MS for complex parts. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | A Cloud Manufacturing Architecture for Complex Parts Machining | |
type | Journal Paper | |
journal volume | 137 | |
journal issue | 6 | |
journal title | Journal of Manufacturing Science and Engineering | |
identifier doi | 10.1115/1.4029856 | |
journal fristpage | 61009 | |
journal lastpage | 61009 | |
identifier eissn | 1528-8935 | |
tree | Journal of Manufacturing Science and Engineering:;2015:;volume( 137 ):;issue: 006 | |
contenttype | Fulltext |