IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v175y2019icp1021-1037.html
   My bibliography  Save this article

Integrated optimization of cutting tool and cutting parameters in face milling for minimizing energy footprint and production time

Author

Listed:
  • Chen, Xingzheng
  • Li, Congbo
  • Tang, Ying
  • Li, Li
  • Du, Yanbin
  • Li, Lingling

Abstract

Cutting tool and cutting parameters are important components in the process planning. Proper selection of cutting tool and cutting parameters can significantly reduce the energy consumption and production time of the machining process. In this paper, an integrated approach of cutting tool and cutting parameter optimization is proposed to minimize the energy footprint and production time of the face milling process. Firstly, the energy footprint characteristics are analyzed by considering multiple cutting tool flexibilities and cutting parameters. Then a multi-objective integrated optimization model for minimizing energy footprint and production time is proposed and solved via a multi-objective Cuckoo Search algorithm. Finally, case studies are conducted to verify the feasibility and validity of the proposed integrated optimization approach. From the results of the case studies, interaction effects between cutting tool and cutting parameters are revealed. Integrated optimization of cutting tool and cutting parameters can achieve more energy footprint savings than either cutting parameter optimization or cutting tool optimization. Moreover, it also can be found that the optimization results for minimum production time does not necessarily satisfy the optimization criterion of minimum energy footprint. The proposed multi-objective integrated optimization approach can strike a balance between minimum energy footprint and minimum production time.

Suggested Citation

  • Chen, Xingzheng & Li, Congbo & Tang, Ying & Li, Li & Du, Yanbin & Li, Lingling, 2019. "Integrated optimization of cutting tool and cutting parameters in face milling for minimizing energy footprint and production time," Energy, Elsevier, vol. 175(C), pages 1021-1037.
    • Handle: RePEc:eee:energy:v:175:y:2019:i:c:p:1021-1037
    DOI: 10.1016/j.energy.2019.02.157
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544219303561
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2019.02.157?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Aghbashlo, Mortaza & Hosseinpour, Soleiman & Tabatabaei, Meisam & Younesi, Habibollah & Najafpour, Ghasem, 2016. "On the exergetic optimization of continuous photobiological hydrogen production using hybrid ANFIS–NSGA-II (adaptive neuro-fuzzy inference system–non-dominated sorting genetic algorithm-II)," Energy, Elsevier, vol. 96(C), pages 507-520.
    2. Hu, Luoke & Peng, Chen & Evans, Steve & Peng, Tao & Liu, Ying & Tang, Renzhong & Tiwari, Ashutosh, 2017. "Minimising the machining energy consumption of a machine tool by sequencing the features of a part," Energy, Elsevier, vol. 121(C), pages 292-305.
    3. Cai, Wei & Liu, Fei & Xie, Jun & Liu, Peiji & Tuo, Junbo, 2017. "A tool for assessing the energy demand and efficiency of machining systems: Energy benchmarking," Energy, Elsevier, vol. 138(C), pages 332-347.
    4. Azaza, Maher & Wallin, Fredrik, 2017. "Multi objective particle swarm optimization of hybrid micro-grid system: A case study in Sweden," Energy, Elsevier, vol. 123(C), pages 108-118.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Yuanhui & Cai, Wei & He, Yan & Peng, Tao & Jia, Shun & Lai, Kee-hung & Li, Li, 2022. "Forward-and-reverse multidirectional turning: A novel material removal approach for improving energy efficiency, processing efficiency and quality," Energy, Elsevier, vol. 260(C).
    2. Mitali Sarkar & Biswajit Sarkar, 2019. "Optimization of Safety Stock under Controllable Production Rate and Energy Consumption in an Automated Smart Production Management," Energies, MDPI, vol. 12(11), pages 1-16, May.
    3. Zhang, Tao & Liu, Zhanqiang & Sun, Xiaodong & Xu, Jixiang & Dong, Longlong & Zhu, Genglei, 2020. "Investigation on specific milling energy and energy efficiency in high-speed milling based on energy flow theory," Energy, Elsevier, vol. 192(C).
    4. Xiangxin An & Guojin Si & Tangbin Xia & Qinming Liu & Yaping Li & Rui Miao, 2022. "Operation and Maintenance Optimization for Manufacturing Systems with Energy Management," Energies, MDPI, vol. 15(19), pages 1-19, October.
    5. Zhang, Jiaqi & Han, Xin & Li, Li & Jia, Shun & Jiang, Zhigang & Duan, Xiangmin & Lai, Kee-hung & Cai, Wei, 2023. "Multi-objective optimisation for energy saving and high efficiency production oriented multidirectional turning based on improved fireworks algorithm considering energy, efficiency and quality," Energy, Elsevier, vol. 284(C).
    6. Tangbin Xia & Xiangxin An & Huaqiang Yang & Yimin Jiang & Yuhui Xu & Meimei Zheng & Ershun Pan, 2023. "Efficient Energy Use in Manufacturing Systems—Modeling, Assessment, and Management Strategy," Energies, MDPI, vol. 16(3), pages 1-20, January.
    7. Zhao, Junhua & Li, Li & Li, Lingling & Zhang, Yunfeng & Lin, Jiang & Cai, Wei & Sutherland, John W., 2023. "A multi-dimension coupling model for energy-efficiency of a machining process," Energy, Elsevier, vol. 274(C).
    8. Muhammad Asif & Hang Shen & Chunlin Zhou & Yuandong Guo & Yibo Yuan & Pu Shao & Lan Xie & Muhammad Shoaib Bhutta, 2023. "Recent Trends, Developments, and Emerging Technologies towards Sustainable Intelligent Machining: A Critical Review, Perspectives and Future Directions," Sustainability, MDPI, vol. 15(10), pages 1-28, May.
    9. Hadhami Ben Slama & Raoudha Gaha & Mehdi Tlija & Sami Chatti & Abdelmajid Benamara, 2023. "Proposal of a Combined AHP-PROMETHEE Decision Support Tool for Selecting Sustainable Machining Process Based on Toolpath Strategy and Manufacturing Parameters," Sustainability, MDPI, vol. 15(24), pages 1-20, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shang, Zhendong & Gao, Dong & Jiang, Zhipeng & Lu, Yong, 2019. "Towards less energy intensive heavy-duty machine tools: Power consumption characteristics and energy-saving strategies," Energy, Elsevier, vol. 178(C), pages 263-276.
    2. Liu, Conghu & Cai, Wei & Dinolov, Ognyan & Zhang, Cuixia & Rao, Weizhen & Jia, Shun & Li, Li & Chan, Felix T.S., 2018. "Emergy based sustainability evaluation of remanufacturing machining systems," Energy, Elsevier, vol. 150(C), pages 670-680.
    3. Cai, Wei & Liu, Fei & Dinolov, Ognyan & Xie, Jun & Liu, Peiji & Tuo, Junbo, 2018. "Energy benchmarking rules in machining systems," Energy, Elsevier, vol. 142(C), pages 258-263.
    4. Hu, Luoke & Liu, Ying & Peng, Chen & Tang, Wangchujun & Tang, Renzhong & Tiwari, Ashutosh, 2018. "Minimising the energy consumption of tool change and tool path of machining by sequencing the features," Energy, Elsevier, vol. 147(C), pages 390-402.
    5. Jiaxin Lu & Weijun Wang & Yingchao Zhang & Song Cheng, 2017. "Multi-Objective Optimal Design of Stand-Alone Hybrid Energy System Using Entropy Weight Method Based on HOMER," Energies, MDPI, vol. 10(10), pages 1-17, October.
    6. Ahmad Alzahrani & Senthil Kumar Ramu & Gunapriya Devarajan & Indragandhi Vairavasundaram & Subramaniyaswamy Vairavasundaram, 2022. "A Review on Hydrogen-Based Hybrid Microgrid System: Topologies for Hydrogen Energy Storage, Integration, and Energy Management with Solar and Wind Energy," Energies, MDPI, vol. 15(21), pages 1-32, October.
    7. Zhu, Jiaoyiling & Hu, Weihao & Xu, Xiao & Liu, Haoming & Pan, Li & Fan, Haoyang & Zhang, Zhenyuan & Chen, Zhe, 2022. "Optimal scheduling of a wind energy dominated distribution network via a deep reinforcement learning approach," Renewable Energy, Elsevier, vol. 201(P1), pages 792-801.
    8. Jicheng Liu & Fangqiu Xu & Shuaishuai Lin & Hua Cai & Suli Yan, 2018. "A Multi-Agent-Based Optimization Model for Microgrid Operation Using Dynamic Guiding Chaotic Search Particle Swarm Optimization," Energies, MDPI, vol. 11(12), pages 1-22, November.
    9. Ren, Fukang & Wei, Ziqing & Zhai, Xiaoqiang, 2021. "Multi-objective optimization and evaluation of hybrid CCHP systems for different building types," Energy, Elsevier, vol. 215(PA).
    10. Davoudkhani, Iraj Faraji & Dejamkhooy, Abdolmajid & Nowdeh, Saber Arabi, 2023. "A novel cloud-based framework for optimal design of stand-alone hybrid renewable energy system considering uncertainty and battery aging," Applied Energy, Elsevier, vol. 344(C).
    11. Mei, Mei & Chen, Zhihua, 2021. "Evaluation and selection of sustainable hydrogen production technology with hybrid uncertain sustainability indicators based on rough-fuzzy BWM-DEA," Renewable Energy, Elsevier, vol. 165(P1), pages 716-730.
    12. Ussama Assad & Muhammad Arshad Shehzad Hassan & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods," Energies, MDPI, vol. 15(6), pages 1-36, March.
    13. Xu, Xiao & Hu, Weihao & Du, Yuefang & Liu, Wen & Liu, Zhou & Huang, Qi & Chen, Zhe, 2020. "Robust chance-constrained gas management for a standalone gas supply system based on wind energy," Energy, Elsevier, vol. 212(C).
    14. Naderipour, Amirreza & Ramtin, Amir Reza & Abdullah, Aldrin & Marzbali, Massoomeh Hedayati & Nowdeh, Saber Arabi & Kamyab, Hesam, 2022. "Hybrid energy system optimization with battery storage for remote area application considering loss of energy probability and economic analysis," Energy, Elsevier, vol. 239(PD).
    15. Ruifeng Shi & Yuqin Gao & Jin Ning & Keyi Tang & Limin Jia, 2023. "Research on Highway Self-Consistent Energy System Planning with Uncertain Wind and Photovoltaic Power Output," Sustainability, MDPI, vol. 15(4), pages 1-30, February.
    16. Cai, Wei & Liu, Fei & Xie, Jun & Liu, Peiji & Tuo, Junbo, 2017. "A tool for assessing the energy demand and efficiency of machining systems: Energy benchmarking," Energy, Elsevier, vol. 138(C), pages 332-347.
    17. Nan Wang & Quan Yang & Cuixia Zhang, 2022. "Data-Driven Low-Carbon Control Method of Machining Process—Taking Axle as an Example," Sustainability, MDPI, vol. 14(21), pages 1-10, October.
    18. Nemanja Mišljenović & Matej Žnidarec & Goran Knežević & Damir Šljivac & Andreas Sumper, 2023. "A Review of Energy Management Systems and Organizational Structures of Prosumers," Energies, MDPI, vol. 16(7), pages 1-32, March.
    19. Cai, Wei & Wang, Lianguo & Li, Li & Xie, Jun & Jia, Shun & Zhang, Xugang & Jiang, Zhigang & Lai, Kee-hung, 2022. "A review on methods of energy performance improvement towards sustainable manufacturing from perspectives of energy monitoring, evaluation, optimization and benchmarking," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    20. Sayyaadi, Hoseyn & Baghsheikhi, Mostafa, 2019. "Retrofit of a steam power plant using the adaptive neuro-fuzzy inference system in response to the load variation," Energy, Elsevier, vol. 175(C), pages 1164-1173.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:175:y:2019:i:c:p:1021-1037. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.