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Optimising the machining time, deviation and energy consumption through a multi-objective feature sequencing approach.

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Title: Optimising the machining time, deviation and energy consumption through a multi-objective feature sequencing approach.
Authors: Hu, Luoke1, Tang, Renzhong1,2 tangrz@zju.edu.cn, Liu, Ying3, Cao, Yanlong1,2, Tiwari, Ashutosh4
Source: Energy Conversion & Management. Mar2018, Vol. 160, p126-140. 15p.
Subjects: Energy consumption, Machine tool path, Pollution, Mechanical engineering, Automatic control systems
Abstract: A considerable amount of global energy consumption is attributable to the machining energy consumption of the machine tool. Thus, reducing the machining energy consumption can alleviate the energy crisis and energy-related environmental pollution. It has been approved that feature sequencing is an effective and economical approach to reduce the machining energy consumption. The single objective model that only minimises the machining energy consumption has been developed in previous research. However, the machining time and deviation, which are also affected by the feature sequence, have not been considered. Thus, this article first aims to understand and model the sequence-related machining time, deviation, and energy consumption (S-MT, S-MD, and S-MEC) while machining a part. Accordingly, a multi-objective feature sequencing problem, which optimises the trade-off among S-MT, S-MD, and S-MEC, is introduced. To solve it, two optimisation approaches, including Non-dominated Inserting Enumeration Algorithm (NIEA) and Non-dominated Sorting Genetic Algorithm II (NSGA-II), are proposed and employed. A case study was conducted to demonstrate the developed models and the optimisation approaches. The experiment results show that the optimal or near-optimal solution sets can be obtained for eight machine parts. By comparison, 20.51% S-MT, 5.29% S-MD, and 16.66% S-MEC can be reduced. Between the two algorithms, NIEA is recommended for the part that has fewer than 12 features. Finally, more optimisation approaches for the multi-objective problem are proposed and discussed. [ABSTRACT FROM AUTHOR]
Copyright of Energy Conversion & Management is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 128165925
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PubType: Academic Journal
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  Data: Optimising the machining time, deviation and energy consumption through a multi-objective feature sequencing approach.
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  Data: <searchLink fieldCode="AR" term="%22Hu%2C+Luoke%22">Hu, Luoke</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Tang%2C+Renzhong%22">Tang, Renzhong</searchLink><relatesTo>1,2</relatesTo><i> tangrz@zju.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Ying%22">Liu, Ying</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Cao%2C+Yanlong%22">Cao, Yanlong</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Tiwari%2C+Ashutosh%22">Tiwari, Ashutosh</searchLink><relatesTo>4</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Energy+Conversion+%26+Management%22">Energy Conversion & Management</searchLink>. Mar2018, Vol. 160, p126-140. 15p.
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  Data: <searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+tool+path%22">Machine tool path</searchLink><br /><searchLink fieldCode="DE" term="%22Pollution%22">Pollution</searchLink><br /><searchLink fieldCode="DE" term="%22Mechanical+engineering%22">Mechanical engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+control+systems%22">Automatic control systems</searchLink>
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  Label: Abstract
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  Data: A considerable amount of global energy consumption is attributable to the machining energy consumption of the machine tool. Thus, reducing the machining energy consumption can alleviate the energy crisis and energy-related environmental pollution. It has been approved that feature sequencing is an effective and economical approach to reduce the machining energy consumption. The single objective model that only minimises the machining energy consumption has been developed in previous research. However, the machining time and deviation, which are also affected by the feature sequence, have not been considered. Thus, this article first aims to understand and model the sequence-related machining time, deviation, and energy consumption (S-MT, S-MD, and S-MEC) while machining a part. Accordingly, a multi-objective feature sequencing problem, which optimises the trade-off among S-MT, S-MD, and S-MEC, is introduced. To solve it, two optimisation approaches, including Non-dominated Inserting Enumeration Algorithm (NIEA) and Non-dominated Sorting Genetic Algorithm II (NSGA-II), are proposed and employed. A case study was conducted to demonstrate the developed models and the optimisation approaches. The experiment results show that the optimal or near-optimal solution sets can be obtained for eight machine parts. By comparison, 20.51% S-MT, 5.29% S-MD, and 16.66% S-MEC can be reduced. Between the two algorithms, NIEA is recommended for the part that has fewer than 12 features. Finally, more optimisation approaches for the multi-objective problem are proposed and discussed. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Energy Conversion & Management is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Mar2018
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