IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v31y2020i4d10.1007_s10845-019-01489-6.html
   My bibliography  Save this article

An effective and automatic approach for parameters optimization of complex end milling process based on virtual machining

Author

Listed:
  • Hengyuan Ma

    (Shanghai Jiao Tong University)

  • Wei Liu

    (Shanghai Jiao Tong University)

  • Xionghui Zhou

    (Shanghai Jiao Tong University)

  • Qiang Niu

    (Shanghai Jiao Tong University)

  • Chuipin Kong

    (Shanghai Jiao Tong University)

Abstract

The demand for optimization of manufacturing processes rises as a reflection of the highly competitive market environment that requires shorter lead time and lower production costs. Although some approaches to milling process optimization have been developed based on analytical model using average cutting parameters, they are not available for complex workpieces when cutting parameters are time-varying and instantaneous cutting conditions need to be considered. In order to automate the optimization process and avoid costly machining tests, in this paper, an effective approach for parameters optimization of complex end milling process based on virtual machining is proposed. A computer-aided design (CAD)/computer-aided manufacturing (CAM) application is integrated for actual tool path generation and feedrate scheduling based on material removal rate. Then, a machining simulator based on octree and instantaneous force model is developed to evaluate feasibility of the given numerical control (NC) program, and the correctness of this simulator is verified by machining tests. The optimization process is controlled by the efficient global optimization method to find global optimal solution with fewer simulations and less computation time. During each iteration of the optimization process, NC programs are generated and evaluated automatically by the CAD/CAM application and the simulator, respectively. The effectiveness and efficiency of the proposed approach are proved by comparing the generated optimal solution (has reduced machining time and production cost) with the recommended cutting parameters from machining experts when machining an impeller.

Suggested Citation

  • Hengyuan Ma & Wei Liu & Xionghui Zhou & Qiang Niu & Chuipin Kong, 2020. "An effective and automatic approach for parameters optimization of complex end milling process based on virtual machining," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 967-984, April.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:4:d:10.1007_s10845-019-01489-6
    DOI: 10.1007/s10845-019-01489-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-019-01489-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-019-01489-6?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. Congbo Li & Lingling Li & Ying Tang & Yantao Zhu & Li Li, 2019. "A comprehensive approach to parameters optimization of energy-aware CNC milling," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 123-138, January.
    2. Mahdi S. Alajmi & Fawzan S. Alfares & Mohamed S. Alfares, 2019. "Selection of optimal conditions in the surface grinding process using the quantum based optimisation method," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1469-1481, March.
    3. Jack P. C. Kleijnen, 2015. "Response Surface Methodology," International Series in Operations Research & Management Science, in: Michael C Fu (ed.), Handbook of Simulation Optimization, edition 127, chapter 0, pages 81-104, Springer.
    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. Jiyoung Jung & Kundo Park & Byungjin Cho & Jinkyoo Park & Seunghwa Ryu, 2023. "Optimization of injection molding process using multi-objective bayesian optimization and constrained generative inverse design networks," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3623-3636, December.
    2. Roman Stryczek & Kamil Wyrobek, 2021. "Heuristic techniques for modelling machine spinning processes," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1189-1206, April.

    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. Shen-Tsu Wang, 2016. "Integrating grey sequencing with the genetic algorithm--immune algorithm to optimise touch panel cover glass polishing process parameter design," International Journal of Production Research, Taylor & Francis Journals, vol. 54(16), pages 4882-4893, August.
    2. Yek, Peter Nai Yuh & Cheng, Yoke Wang & Liew, Rock Keey & Wan Mahari, Wan Adibah & Ong, Hwai Chyuan & Chen, Wei-Hsin & Peng, Wanxi & Park, Young-Kwon & Sonne, Christian & Kong, Sieng Huat & Tabatabaei, 2021. "Progress in the torrefaction technology for upgrading oil palm wastes to energy-dense biochar: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    3. Qin, Caiyan & Kim, Joong Bae & Lee, Bong Jae, 2019. "Performance analysis of a direct-absorption parabolic-trough solar collector using plasmonic nanofluids," Renewable Energy, Elsevier, vol. 143(C), pages 24-33.
    4. Ramos, Ana & Monteiro, Eliseu & Rouboa, Abel, 2019. "Numerical approaches and comprehensive models for gasification process: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 188-206.
    5. M'Arimi, M.M. & Mecha, C.A. & Kiprop, A.K. & Ramkat, R., 2020. "Recent trends in applications of advanced oxidation processes (AOPs) in bioenergy production: Review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 121(C).
    6. Renzi, Massimiliano & Bietresato, Marco & Mazzetto, Fabrizio, 2016. "An experimental evaluation of the performance of a SI internal combustion engine for agricultural purposes fuelled with different bioethanol blends," Energy, Elsevier, vol. 115(P1), pages 1069-1080.
    7. Chamberlin Stéphane Azebaze Mboving & Zbigniew Hanzelka & Andrzej Firlit, 2022. "Analysis of the Factors Having an Influence on the LC Passive Harmonic Filter Work Efficiency," Energies, MDPI, vol. 15(5), pages 1-51, March.
    8. Lu Chen & Qincheng Chen & Pinhua Rao & Lili Yan & Alghashm Shakib & Guoqing Shen, 2018. "Formulating and Optimizing a Novel Biochar-Based Fertilizer for Simultaneous Slow-Release of Nitrogen and Immobilization of Cadmium," Sustainability, MDPI, vol. 10(8), pages 1-14, August.
    9. Biranchi Panda & K. Shankhwar & Akhil Garg & M. M. Savalani, 2019. "Evaluation of genetic programming-based models for simulating bead dimensions in wire and arc additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 809-820, February.
    10. Zahedi, Ali Reza & Mirnezami, Seyed Abolfazl, 2020. "Experimental analysis of biomass to biodiesel conversion using a novel renewable combined cycle system," Renewable Energy, Elsevier, vol. 162(C), pages 1177-1194.
    11. Ahmad Abbaszadeh-Mayvan & Barat Ghobadian & Gholamhassan Najafi & Talal Yusaf, 2018. "Intensification of Continuous Biodiesel Production from Waste Cooking Oils Using Shockwave Power Reactor: Process Evaluation and Optimization through Response Surface Methodology (RSM)," Energies, MDPI, vol. 11(10), pages 1-13, October.
    12. de Oliveira, Lucas Guedes & Aquila, Giancarlo & Balestrassi, Pedro Paulo & de Paiva, Anderson Paulo & de Queiroz, Anderson Rodrigo & de Oliveira Pamplona, Edson & Camatta, Ulisses Pessin, 2020. "Evaluating economic feasibility and maximization of social welfare of photovoltaic projects developed for the Brazilian northeastern coast: An attribute agreement analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 123(C).
    13. Gorji, Tahereh B. & Ranjbar, A.A., 2017. "Thermal and exergy optimization of a nanofluid-based direct absorption solar collector," Renewable Energy, Elsevier, vol. 106(C), pages 274-287.
    14. Trung-Thanh Nguyen & Mozammel Mia, 2020. "Modeling and Evaluation of Energy Efficiency of New Hybrid Turning-Burnishing Process in Terms of Surface Properties," Energies, MDPI, vol. 13(18), pages 1-20, September.
    15. Masooma Munir & Muhammad Nadeem & Barkat Ali & Muhammad Sultan & Rabia Kanwal & Huda Abdalrahman Al-Jumayi & Eman Hassan Ahmed Algarni & Maged B. Alnofeai & Samy F. Mahmoud, 2022. "Investigating the Impact of Ultrasound, Microwave, and High-Pressure Processing of Milk on the Volatile Compounds and Sensory Properties of Cheddar Cheese," Agriculture, MDPI, vol. 12(5), pages 1-17, April.
    16. Najafi, Gholamhassan & Ghobadian, Barat & Yusaf, Talal & Safieddin Ardebili, Seyed Mohammad & Mamat, Rizalman, 2015. "Optimization of performance and exhaust emission parameters of a SI (spark ignition) engine with gasoline–ethanol blended fuels using response surface methodology," Energy, Elsevier, vol. 90(P2), pages 1815-1829.
    17. Iftikhar Ahmad & Adil Sana & Manabu Kano & Izzat Iqbal Cheema & Brenno C. Menezes & Junaid Shahzad & Zahid Ullah & Muzammil Khan & Asad Habib, 2021. "Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions," Energies, MDPI, vol. 14(16), pages 1-27, August.
    18. Xing Yang & Hailong Wang & Peter James Strong & Song Xu & Shujuan Liu & Kouping Lu & Kuichuan Sheng & Jia Guo & Lei Che & Lizhi He & Yong Sik Ok & Guodong Yuan & Ying Shen & Xin Chen, 2017. "Thermal Properties of Biochars Derived from Waste Biomass Generated by Agricultural and Forestry Sectors," Energies, MDPI, vol. 10(4), pages 1-12, April.
    19. Azman, Nadia Farhana & Abdeshahian, Peyman & Kadier, Abudukeremu & Shukor, Hafiza & Al-Shorgani, Najeeb Kaid Nasser & Hamid, Aidil Abdul & Kalil, Mohd Sahaid, 2016. "Utilization of palm kernel cake as a renewable feedstock for fermentative hydrogen production," Renewable Energy, Elsevier, vol. 93(C), pages 700-708.
    20. Ibrahim, Ayooluwa Paul & Omilakin, Ropo Oluwasesan & Betiku, Eriola, 2019. "Optimization of microwave-assisted solvent extraction of non-edible sandbox (Hura crepitans) seed oil: A potential biodiesel feedstock," Renewable Energy, Elsevier, vol. 141(C), pages 349-358.

    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:spr:joinma:v:31:y:2020:i:4:d:10.1007_s10845-019-01489-6. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.