IDEAS home Printed from https://ideas.repec.org/r/eee/energy/v133y2017icp142-157.html
   My bibliography  Save this item

Energy consumption in machining: Classification, prediction, and reduction strategy

Citations

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


Cited by:

  1. Modawy Adam Ali Abdalla & Wang Min & Gehad Abdullah Amran & Amerah Alabrah & Omer Abbaker Ahmed Mohammed & Hussain AlSalman & Bassiouny Saleh, 2023. "Optimizing Energy Usage and Smoothing Load Profile via a Home Energy Management Strategy with Vehicle-to-Home and Energy Storage System," Sustainability, MDPI, vol. 15(20), pages 1-28, October.
  2. 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).
  3. Silviu Răileanu & Theodor Borangiu & Ionuț Lențoiu & Mihnea Constantinescu, 2024. "Optimizing Energy Consumption of Industrial Robots with Model-Based Layout Design," Sustainability, MDPI, vol. 16(3), pages 1-20, January.
  4. Shailendra Pawanr & Kapil Gupta, 2024. "A Review on Recent Advances in the Energy Efficiency of Machining Processes for Sustainability," Energies, MDPI, vol. 17(15), pages 1-21, July.
  5. Sylwester Kaczmarzewski & Dominika Matuszewska & Maciej Sołtysik, 2021. "Analysis of Selected Service Industries in Terms of the Use of Photovoltaics before and during the COVID-19 Pandemic," Energies, MDPI, vol. 15(1), pages 1-24, December.
  6. Agga, Ali & Abbou, Ahmed & Labbadi, Moussa & El Houm, Yassine, 2021. "Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models," Renewable Energy, Elsevier, vol. 177(C), pages 101-112.
  7. Qiao, Weibiao & Liu, Wei & Liu, Enbin, 2021. "A combination model based on wavelet transform for predicting the difference between monthly natural gas production and consumption of U.S," Energy, Elsevier, vol. 235(C).
  8. Mohammad Kanan & Sadaf Zahoor & Muhammad Salman Habib & Sana Ehsan & Mudassar Rehman & Muhammad Shahzaib & Sajawal Ali Khan & Hassan Ali & Zaher Abusaq & Allam Hamdan, 2023. "Analysis of Carbon Footprints and Surface Quality in Green Cutting Environments for the Milling of AZ31 Magnesium Alloy," Sustainability, MDPI, vol. 15(7), pages 1-18, April.
  9. Jessica Walther & Matthias Weigold, 2021. "A Systematic Review on Predicting and Forecasting the Electrical Energy Consumption in the Manufacturing Industry," Energies, MDPI, vol. 14(4), pages 1-24, February.
  10. Joanna Kossakowska & Sebastian Bombiński & Krzysztof Ejsmont, 2021. "Analysis of the Suitability of Signal Features for Individual Sensor Types in the Diagnosis of Gradual Tool Wear in Turning," Energies, MDPI, vol. 14(20), pages 1-23, October.
  11. Seok-Jun Bu & Sung-Bae Cho, 2020. "Time Series Forecasting with Multi-Headed Attention-Based Deep Learning for Residential Energy Consumption," Energies, MDPI, vol. 13(18), pages 1-16, September.
  12. Athar Ajaz Khan & János Abonyi, 2022. "Simulation of Sustainable Manufacturing Solutions: Tools for Enabling Circular Economy," Sustainability, MDPI, vol. 14(15), pages 1-40, August.
  13. Tuo, Junbo & Liu, Fei & Liu, Peiji & Zhang, Hua & Cai, Wei, 2018. "Energy efficiency evaluation for machining systems through virtual part," Energy, Elsevier, vol. 159(C), pages 172-183.
  14. Soheyl Khalilpourazari & Saman Khalilpourazary & Aybike Özyüksel Çiftçioğlu & Gerhard-Wilhelm Weber, 2021. "Designing energy-efficient high-precision multi-pass turning processes via robust optimization and artificial intelligence," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1621-1647, August.
  15. 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.
  16. Rüstem Binali & Abhishek Dhananjay Patange & Mustafa Kuntoğlu & Tadeusz Mikolajczyk & Emin Salur, 2022. "Energy Saving by Parametric Optimization and Advanced Lubri-Cooling Techniques in the Machining of Composites and Superalloys: A Systematic Review," Energies, MDPI, vol. 15(21), pages 1-37, November.
  17. 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.
  18. Cai, Wei & Li, Yanqi & Li, Li & Lai, Kee-hung & Jia, Shun & Xie, Jun & Zhang, Yuanhui & Hu, Luoke, 2022. "Energy saving and high efficiency production oriented forward-and-reverse multidirectional turning: Energy modeling and application," Energy, Elsevier, vol. 252(C).
  19. Hongyi Wu & Xuanyi Wang & Xiaolei Deng & Hongyao Shen & Xinhua Yao, 2024. "Review on Design Research in CNC Machine Tools Based on Energy Consumption," Sustainability, MDPI, vol. 16(2), pages 1-20, January.
  20. Gomez, William & Wang, Fu-Kwun & Lo, Shih-Che, 2024. "A hybrid approach based machine learning models in electricity markets," Energy, Elsevier, vol. 289(C).
  21. Ramya Kuppusamy & Srete Nikolovski & Yuvaraja Teekaraman, 2023. "Review of Machine Learning Techniques for Power Quality Performance Evaluation in Grid-Connected Systems," Sustainability, MDPI, vol. 15(20), pages 1-29, October.
  22. Wang, Yanxia & Li, Kang & Gan, Shaojun & Cameron, Ché, 2019. "Analysis of energy saving potentials in intelligent manufacturing: A case study of bakery plants," Energy, Elsevier, vol. 172(C), pages 477-486.
  23. do Carmo, Pedro R.X. & do Monte, João Victor L. & Filho, Assis T. de Oliveira & Freitas, Eduardo & Tigre, Matheus F.F.S.L. & Sadok, Djamel & Kelner, Judith, 2023. "A data-driven model for the optimization of energy consumption of an industrial production boiler in a fiber plant," Energy, Elsevier, vol. 284(C).
  24. Abdulgani Kahraman & Mehmed Kantardzic & Muhammet Mustafa Kahraman & Muhammed Kotan, 2021. "A Data-Driven Multi-Regime Approach for Predicting Energy Consumption," Energies, MDPI, vol. 14(20), pages 1-17, October.
  25. Xiao, Qinge & Li, Congbo & Tang, Ying & Pan, Jian & Yu, Jun & Chen, Xingzheng, 2019. "Multi-component energy modeling and optimization for sustainable dry gear hobbing," Energy, Elsevier, vol. 187(C).
  26. Pimenov, Danil Yu & Mia, Mozammel & Gupta, Munish K. & Machado, Álisson R. & Pintaude, Giuseppe & Unune, Deepak Rajendra & Khanna, Navneet & Khan, Aqib Mashood & Tomaz, Ítalo & Wojciechowski, Szymon &, 2022. "Resource saving by optimization and machining environments for sustainable manufacturing: A review and future prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
  27. Benjie Li & Hualin Zheng & Xiao Yang & Liang Guo & Binglin Li, 2020. "Energy Optimization for Motorized Spindle System of Machine Tools under Minimum Thermal Effects and Maximum Productivity Constraints," Energies, MDPI, vol. 13(22), pages 1-17, November.
  28. Lin Zheng & Wei Zhang & Fei Liang & Shuang Lin & Xiangyu Jin, 2017. "Experimental Studies of Phase Change and Microencapsulated Phase Change Materials in a Cold Storage/Transportation System with Solar Driven Cooling Cycle," Energies, MDPI, vol. 10(11), pages 1-11, November.
  29. Najafzad, Hamid & Davari-Ardakani, Hamed & Nemati-Lafmejani, Reza, 2019. "Multi-skill project scheduling problem under time-of-use electricity tariffs and shift differential payments," Energy, Elsevier, vol. 168(C), pages 619-636.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.