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Special Section Articles

Cloud-Based Platform for Optimal Machining Parameter Selection Based on Function Blocks and Real-Time Monitoring

[+] Author and Article Information
Nikolaos Tapoglou

Manufacturing and Materials Department,
Cranfield University,
College Road,
Cranfield MK43 0AL, UK
e-mail: n.tapoglou@cranfield.ac.uk

Jörn Mehnen

Manufacturing and Materials Department,
Cranfield University,
College Road,
Cranfield MK43 0AL, UK
e-mail: j.mehnen@cranfield.ac.uk mailto

Aikaterini Vlachou

Laboratory for Manufacturing Systems
and Automation,
University of Patras,
Patras 26500, Greece
e-mail: vlachou@lms.mech.upatras.gr

Michael Doukas

Laboratory for Manufacturing
Systems and Automation,
University of Patras, Patras 26500, Greece
e-mail: mdoukas@lms.mech.upatras.gr

Nikolaos Milas

Laboratory for Manufacturing Systems
and Automation,
University of Patras,
Patras 26500, Greece
e-mail: milas@lms.mech.upatras.gr

Dimitris Mourtzis

Laboratory for Manufacturing Systems
and Automation,
University of Patras,
Patras 26500, Greece
e-mail: mourtzis@lms.mech.upatras.gr

1Corresponding author.

Contributed by the Manufacturing Engineering Division of ASME for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received October 15, 2014; final manuscript received February 8, 2015; published online July 8, 2015. Assoc. Editor: Xun Xu.

J. Manuf. Sci. Eng 137(4), 040909 (Aug 01, 2015) (11 pages) Paper No: MANU-14-1548; doi: 10.1115/1.4029806 History: Received October 15, 2014; Revised February 08, 2015; Online July 08, 2015

The way machining operations have been running has changed over the years. Nowadays, machine utilization and availability monitoring are becoming increasingly important for the smooth operation of modern workshops. Moreover, the nature of jobs undertaken by manufacturing small and medium enterprises (SMEs) has shifted from a mass production to small batch. To address the challenges caused by modern fast changing environments, a new cloud-based approach for monitoring the use of manufacturing equipment, dispatching jobs to the selected computer numerical control (CNC) machines, and creating the optimum machining code is presented. In this approach the manufacturing equipment is monitored using a sensor network and though an information fusion technique it derives and broadcasts the data of available tools and machines through the internet to a cloud-based platform. On the manufacturing equipment event driven function blocks with embedded optimization algorithms are responsible for selecting the optimal cutting parameters and generating the moves required for machining the parts while considering the latest information regarding the available machines and cutting tools. A case study based on scenario from a shop floor that undertakes machining jobs is used to demonstrate the developed methods and tools.

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Figures

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Fig. 2

The proposed Smart Factory concept

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Fig. 1

The structure of basic and composite function blocks according to IEC 61499

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Fig. 6

Toolpath generation module architecture

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Fig. 3

Software architecture of the cloud-based application

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Fig. 4

Status of monitored machines

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Fig. 5

An interface with detailed monitoring information

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Fig. 7

Monitoring system on the machine's electrical cabinet

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Fig. 8

Face milling process parameters

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Fig. 9

Face milling optimization function block along with the ECC

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Fig. 10

Toolpath simulation as seen through the simulation program NCSpeedVR. The red path is the first path of the solution while the yellow path shows the second step of the proposed solution.

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