Supervision Controller for Real-time Surface Quality Assurance in CNC Machining using Artificial Intelligence

Lorena Caires Moreira; Weidong Li; Xin Lu; Michael Fitzpatrick

doi:10.1016/j.cie.2018.12.016

Supervision Controller for Real-time Surface Quality Assurance in CNC Machining using Artificial Intelligence

Lorena Caires Moreira, Weidong Li, Xin Lu, Michael Fitzpatrick

College of Engineering, Environment & Science

Research output: Contribution to journal › Article › peer-review

37 Citations (Scopus)

357 Downloads (Pure)

Abstract

A major challenge for Computer Numerical Control (CNC) machining is how to manufacture high-quality workpieces effectively. Consequences of poor surface quality incur re-processing and higher wastes generating negative impacts on production costs and profitability. The complex relationships between surface quality and machining parameters could overwhelm machinists’ capabilities to correctly select machining parameters to produce satisfied quality of machined workpieces. This paper presents a novel approach of designing an intelligent supervision controller for real-time adjustments on feed rate and spindle speed to achieve desired surface quality of machined workpieces. The controller is an innovative model-based closed-loop system, consisting of a surface roughness prediction model and a multi-variable controller, to ensure real-time improvements on surface quality during machining processes. A case study based on milling processes for BS EN24T steel alloy has been used for testing and validating the approach. Simulation results show that the controller significantly reduced the difference between required and predicted surface roughness from 3.6 μm (based on planned parameters) to 0.12 μm (after the supervision controller adjustments). The results demonstrate that the proposed approach can effectively support high-quality machining processes.

Original language	English
Pages (from-to)	158-168
Number of pages	11
Journal	Computers & Industrial Engineering
Volume	127
Early online date	5 Dec 2018
DOIs	https://doi.org/10.1016/j.cie.2018.12.016
Publication status	Published - Jan 2019

Bibliographical note

NOTICE: this is the author’s version of a work that was accepted for publication in
Computers & Industrial Engineering. Changes resulting from the publishing
process, such as peer review, editing, corrections, structural formatting, and other
quality control mechanisms may not be reflected in this document. Changes may
have been made to this work since it was submitted for publication. A definitive
version was subsequently published in Computers & Industrial Engineering, 127,
(2019) DOI: 10.1016/j.cie.2018.12.016

© 2019, Elsevier. Licensed under the Creative Commons AttributionNonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/

Funder

EU, Lloyd’s Register Foundation

Keywords

CNC machining
Quality control
Smart manufacturing
Surface roughness

ASJC Scopus subject areas

Computer Science(all)
Engineering(all)

Access to Document

10.1016/j.cie.2018.12.016

Post-PrintAccepted author manuscript, 2.76 MBLicence: CC BY-NC-ND

Cite this

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title = "Supervision Controller for Real-time Surface Quality Assurance in CNC Machining using Artificial Intelligence",

abstract = "A major challenge for Computer Numerical Control (CNC) machining is how to manufacture high-quality workpieces effectively. Consequences of poor surface quality incur re-processing and higher wastes generating negative impacts on production costs and profitability. The complex relationships between surface quality and machining parameters could overwhelm machinists{\textquoteright} capabilities to correctly select machining parameters to produce satisfied quality of machined workpieces. This paper presents a novel approach of designing an intelligent supervision controller for real-time adjustments on feed rate and spindle speed to achieve desired surface quality of machined workpieces. The controller is an innovative model-based closed-loop system, consisting of a surface roughness prediction model and a multi-variable controller, to ensure real-time improvements on surface quality during machining processes. A case study based on milling processes for BS EN24T steel alloy has been used for testing and validating the approach. Simulation results show that the controller significantly reduced the difference between required and predicted surface roughness from 3.6 μm (based on planned parameters) to 0.12 μm (after the supervision controller adjustments). The results demonstrate that the proposed approach can effectively support high-quality machining processes.",

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note = "NOTICE: this is the author{\textquoteright}s version of a work that was accepted for publication in Computers & Industrial Engineering. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computers & Industrial Engineering, 127, (2019) DOI: 10.1016/j.cie.2018.12.016 {\textcopyright} 2019, Elsevier. Licensed under the Creative Commons AttributionNonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/",

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AU - Caires Moreira, Lorena

AU - Li, Weidong

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N1 - NOTICE: this is the author’s version of a work that was accepted for publication in Computers & Industrial Engineering. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computers & Industrial Engineering, 127, (2019) DOI: 10.1016/j.cie.2018.12.016 © 2019, Elsevier. Licensed under the Creative Commons AttributionNonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

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N2 - A major challenge for Computer Numerical Control (CNC) machining is how to manufacture high-quality workpieces effectively. Consequences of poor surface quality incur re-processing and higher wastes generating negative impacts on production costs and profitability. The complex relationships between surface quality and machining parameters could overwhelm machinists’ capabilities to correctly select machining parameters to produce satisfied quality of machined workpieces. This paper presents a novel approach of designing an intelligent supervision controller for real-time adjustments on feed rate and spindle speed to achieve desired surface quality of machined workpieces. The controller is an innovative model-based closed-loop system, consisting of a surface roughness prediction model and a multi-variable controller, to ensure real-time improvements on surface quality during machining processes. A case study based on milling processes for BS EN24T steel alloy has been used for testing and validating the approach. Simulation results show that the controller significantly reduced the difference between required and predicted surface roughness from 3.6 μm (based on planned parameters) to 0.12 μm (after the supervision controller adjustments). The results demonstrate that the proposed approach can effectively support high-quality machining processes.

AB - A major challenge for Computer Numerical Control (CNC) machining is how to manufacture high-quality workpieces effectively. Consequences of poor surface quality incur re-processing and higher wastes generating negative impacts on production costs and profitability. The complex relationships between surface quality and machining parameters could overwhelm machinists’ capabilities to correctly select machining parameters to produce satisfied quality of machined workpieces. This paper presents a novel approach of designing an intelligent supervision controller for real-time adjustments on feed rate and spindle speed to achieve desired surface quality of machined workpieces. The controller is an innovative model-based closed-loop system, consisting of a surface roughness prediction model and a multi-variable controller, to ensure real-time improvements on surface quality during machining processes. A case study based on milling processes for BS EN24T steel alloy has been used for testing and validating the approach. Simulation results show that the controller significantly reduced the difference between required and predicted surface roughness from 3.6 μm (based on planned parameters) to 0.12 μm (after the supervision controller adjustments). The results demonstrate that the proposed approach can effectively support high-quality machining processes.

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Supervision Controller for Real-time Surface Quality Assurance in CNC Machining using Artificial Intelligence

Abstract

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