IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v30y2019i2d10.1007_s10845-016-1282-2.html
   My bibliography  Save this article

Evaluation of genetic programming-based models for simulating bead dimensions in wire and arc additive manufacturing

Author

Listed:
  • Biranchi Panda

    (Universidade de Lisboa)

  • K. Shankhwar

    (Kalinga Institute of Industrial Technology)

  • Akhil Garg

    (Shantou University)

  • M. M. Savalani

    (HongKong Polytechnic University)

Abstract

Wire and arc additive manufacturing (WAAM) is a novel rapid prototyping process that employs gas tungsten arc welding, controlled by a robot, to build complex 3D parts by successive layer deposition technique. Experimental studies on WAAM are useful for understanding the physics of the process however the quantification and optimization of process parameters is difficult due to complex mechanisms involved in WAAM process. In this present work, the measurement of two bead dimensions (bead height and bead width) based on the three inputs (peak current, wire feed speed, and travel speed) is done using the gas tungsten arc welding machine. Experimental study is followed by proposition of two variants of advanced evolutionary algorithms (gene expression programming and multi-gene genetic programming) in formulation of the functional expressions for the two bead dimensions based on the three inputs. The performance analysis of the two proposed models is conducted based on the four statistical error metrics, hypothesis tests and cross-validation. The relationships extracted between the bead dimensions and the three inputs reveals that the peak current influences both the bead height and bead width simultaneously. The findings reported will have a positive implication on the industry in predictive monitoring of the bead dimensions during the WAAM process.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:2:d:10.1007_s10845-016-1282-2
    DOI: 10.1007/s10845-016-1282-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-016-1282-2
    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/s10845-016-1282-2?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. 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.
    2. Siba Sankar Mahapatra & Biranchi Narayan Panda, 2013. "Benchmarking of rapid prototyping systems using grey relational analysis," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 16(4), pages 460-477.
    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. Hong Seok Park & Dinh Son Nguyen & Thai Le-Hong & Xuan Tran, 2022. "Machine learning-based optimization of process parameters in selective laser melting for biomedical applications," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1843-1858, August.
    2. Thai Le-Hong & Pai Chen Lin & Jian-Zhong Chen & Thinh Duc Quy Pham & Xuan Tran, 2023. "Data-driven models for predictions of geometric characteristics of bead fabricated by selective laser melting," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1241-1257, March.
    3. A. Chabot & N. Laroche & E. Carcreff & M. Rauch & J.-Y. Hascoët, 2020. "Towards defect monitoring for metallic additive manufacturing components using phased array ultrasonic testing," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1191-1201, June.
    4. Konstantinos S. Boulas & Georgios D. Dounias & Chrissoleon T. Papadopoulos, 2023. "A hybrid evolutionary algorithm approach for estimating the throughput of short reliable approximately balanced production lines," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 823-852, February.
    5. Zeqi Hu & Xunpeng Qin & Yifeng Li & Jiuxin Yuan & Qiang Wu, 2020. "Multi-bead overlapping model with varying cross-section profile for robotic GMAW-based additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1133-1147, June.

    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. 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.
    10. 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.
    11. 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).
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. David M. Goldberg & Jason K. Deane & Terry R. Rakes & Loren Paul Rees, 2022. "3D Printing Technology and the Market Value of the Firm," Information Systems Frontiers, Springer, vol. 24(4), pages 1379-1392, August.

    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:30:y:2019:i:2:d:10.1007_s10845-016-1282-2. 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.