Advertisement

Minimization of bead geometry by optimization of regression equations for laser-beam bead-on-plate welded Hastelloy C-276 sheet

  • Kalinga Simant Bal
  • Jyotsna Dutta Majumdar
  • Asimava Roy Choudhury
Technical Paper
  • 133 Downloads

Abstract

The present study is aimed at selection of laser beam welding parameters that would produce through-penetrated weld zone with minimum cross-sectional area in 2.7-mm-thick Hastelloy C-276 sheet. Weld zone area was found to be comprised of geometrical features such as total throat, crown width, root width, neck width. A new, fast and relatively simple method of optimization has been proposed in the present study. Regression equation for each geometrical feature was generated from the full factorial experimental design data. Optimization of welding parameters to minimize weld zone area was carried out by solving regression equations using experimentally obtained values of geometrical features as the boundary condition. Once the optimized value of parametric combination was obtained, experiment was conducted to confirm the minimization of weld bead cross-sectional geometry.

Keywords

Hastelloy Fiber laser Full factorial experimental design Regression equation Laser beam welding Weld bead optimization 

Notes

Acknowledgements

The authors are grateful to all the faculty members, technical staffs and research scholars of Department of Mechanical Engineering, Department of Metallurgical and Materials Engineering and Central Research Facility, IIT Kharagpur, for extending their support to carry out various experiments. The authors want to thank Mr. Tanmoy Baram, Department of Mechanical Engineering, IIT Kharagpur, for his invaluable support.

References

  1. 1.
    Wu DJ, Ma GY, Niu FY, Guo DM (2012) Pulsed laser welding of Hastelloy C-276: high temperature mechanical properties and microstructure. Mater Manuf Process 28:524–528CrossRefGoogle Scholar
  2. 2.
    Ma G, Niu F, Wu D, Qu Y (2013) Electrochemistry corrosion properties of pulsed laser welding Hastelloy C-276. Phys Procedia 41:31–37CrossRefGoogle Scholar
  3. 3.
    Ma G, Wu D, Guo D (2011) Segregation characteristics of pulsed laser butt welding of Hastelloy C-276. Mater Trans A 42A:3853–3857CrossRefGoogle Scholar
  4. 4.
    Ventrellaa VA, Berrettab JR, Rossib WD (2012) Pulsed Nd:YAG laser welding of Ni-alloy Hastelloy C-276 foils. Phys Procedia 39:569–576CrossRefGoogle Scholar
  5. 5.
    Wu D, Ma G, Guo Y, Guo D (2010) Study of weld morphology on thin Hastelloy C-276 sheet of pulsed laser welding. Phys Procedia 5:99–105CrossRefGoogle Scholar
  6. 6.
    Manikandan M, Hari PR, Vishnu G, Arivarasu M, Devendranath RK, Arivazhagan N, Nageswara RM, Reddy GM (2014) Investigation of microstructure and mechanical properties of super alloy C-276 by continuous Nd:YAG laser welding. Proc Mater Sci 5:2233–2241CrossRefGoogle Scholar
  7. 7.
    Benyounis KY, Olabi AG, Hashmi MSJ (2005) Optimizing the laser-welded butt joints of medium carbon steel using RSM. J Mater Process Technol 164:986–989CrossRefGoogle Scholar
  8. 8.
    Benyounis KY, Olabi AG, Hashmi MSJ (2008) Multi-response optimization of CO2 laser-welding process of austenitic stainless steel. Opt Laser Technol 40:76–87CrossRefGoogle Scholar
  9. 9.
    Pan LK, Wang CC, Wei SL, Sher HF (2007) Optimizing multiple quality characteristics via Taguchi method-based Grey analysis. J Mater Process Technol 182:107–116CrossRefGoogle Scholar
  10. 10.
    Kannan T, Murugan N (2006) Effect of flux cored arc welding process parameters on duplex stainless steel clad quality. J Mater Process Technol 176:230–239CrossRefGoogle Scholar
  11. 11.
    Las-Casas MS, Diniz de Àvila TL, Bracarense AQ, Lima EJ II (2018) Weld parameter prediction using artificial neural network: FN and geometric parameter prediction of austenitic stainless steel welds. J Braz Soc Mech Sci Eng 26:1–9Google Scholar
  12. 12.
    Cruz JG, Torres EM, Alfaro SC (2015) A methodology for modeling and control of weld bead width in the GMAW process. J Braz Soc Mech Sci Eng 37:1529–1541CrossRefGoogle Scholar
  13. 13.
    Rocha LC, Paiva AP, Paiva EJ, Balestrassi PP (2016) Comparing DEA and principal component analysis in the multiobjective optimization of P-GMAW process. J Braz Soc Mech Sci Eng 38:2513–2526CrossRefGoogle Scholar
  14. 14.
    Adalarasan R, Sundaram AS (2015) Parameter design in friction welding of Al/SiC/Al2O3 composite using grey theory based principal component analysis (GT-PCA). J Braz Soc Mech Sci Eng 37:1515–1528CrossRefGoogle Scholar
  15. 15.
    Palanivel R, Laubscher RF, Dinaharan I, Murugan N (2016) Tensile strength prediction of dissimilar friction stir-welded AA6351–AA5083 using artificial neural network technique. J Braz Soc Mech Sci Eng 38:1647–1657CrossRefGoogle Scholar
  16. 16.
    Kumar S, Kumar S (2015) Multi-response optimization of process parameters for friction stir welding of joining dissimilar Al alloys by gray relation analysis and Taguchi method. J Braz Soc Mech Sci Eng 37:665–674CrossRefGoogle Scholar
  17. 17.
    Ramkumar R, Ragupathy A (2015) Optimization of cooling tower performance with different types of packings using Taguchi approach. J Braz Soc Mech Sci Eng 37:929–936CrossRefGoogle Scholar
  18. 18.
    Unal H, Ficici F, Mimaroglu A, Demirkol A, Kırdar A (2016) Prediction and optimization of tribological behavior of nylon composites using Taguchi analysis method. J Braz Soc Mech Sci Eng 38:2097–2104CrossRefGoogle Scholar
  19. 19.
    Khazaali H, Fereshteh-Saniee F (2018) Application of the Taguchi method for efficient studying of elevated-temperature incremental forming of a titanium alloy. J Braz Soc Mech Sci Eng 40:43CrossRefGoogle Scholar
  20. 20.
    León D, Arzola N, Tovar A (2015) Statistical analysis of the influence of tooth geometry in the performance of a harmonic drive. J Braz Soc Mech Sci Eng 37:723–735CrossRefGoogle Scholar
  21. 21.
    Myers R, Montgomery DC (2002) Response surface methodology. Wiley, New YorkzbMATHGoogle Scholar
  22. 22.
    Benyounis KY, Olabi AG (2008) Optimization of different welding processes using statistical and numerical approaches—a reference guide. Adv Eng Softw 39:483–496CrossRefGoogle Scholar
  23. 23.
    Venter GS, Beck AT, Da Silva MM (2016) A simple hierarchical procedure for parameter identification in robust topology optimization. J Braz Soc Mech Sci Eng 38:679–689CrossRefGoogle Scholar
  24. 24.
    Prasad KS, Rao C, Rao DN (2012) Application of design of experiments to plasma arc welding process: a review. J Braz Soc Mech Sci Eng 34:75–81Google Scholar
  25. 25.
    Arora VK, Bhushan G, Aggarwal ML (2017) Enhancement of fatigue life of multi-leaf spring by parameter optimization using RSM. J Braz Soc Mech Sci Eng 39:1333–1349CrossRefGoogle Scholar
  26. 26.
    Valle PD, Amorim FL, Da Costa DD, Marcondes PV (2018) Experimental investigations on the incremental sheet forming of commercial steel ASTM A653 CS-A G90 to predict maximum bending effort. J Braz Soc Mech Sci Eng 40:322CrossRefGoogle Scholar
  27. 27.
    Balaji S, Mahapatra MM (2013) Experimental study and modeling of friction stir welding process to produce optimized AA2219 butt welds for aerospace application. Proc Inst Mech En Part B J Eng Manuf 227:132–143CrossRefGoogle Scholar
  28. 28.
    Askari-Paykani M, Shayan M, Shamanian M (2014) Weldability of ferritic ductile cast iron using full factorial design of experiment. J Iron Steel Res Int 21:252–263CrossRefGoogle Scholar
  29. 29.
    Ganjigatti JP, Pratihar DK, Roy Choudhury A (2008) Modeling of the MIG welding process using statistical approaches. Int J Adv Manuf Technol 35:1166–1190CrossRefGoogle Scholar
  30. 30.
    Ganjigatti JP, Pratihar DK, Roy Choudhury A (2007) Global versus cluster-wise regression analyses for prediction of bead geometry in MIG welding process. J Mater Process Technol 189:352–366CrossRefGoogle Scholar
  31. 31.
    Koganti R, Karas C, Joaquin A, Henderson D, Zaluzec M, Caliskan A (2003) Metal inert gas (MIG) welding process optimization for joining aluminum 5754 sheet material using OTC/Daihen equipment. In: ASME international mechanical engineering congress and exposition, pp 409–425Google Scholar
  32. 32.
    Prasad KS, Rao CS, Rao DN (2011) Prediction of weld pool geometry in pulsed current micro plasma arc welding of SS304L stainless steel sheets. ITJEMAST 2:325–336Google Scholar
  33. 33.
    Kim IS, Son KJ, Yang YS, Yaragada PK (2003) Sensitivity analysis for process parameters in GMA welding processes using a factorial design method. Int J Mach Tool Manuf 43:763–769CrossRefGoogle Scholar
  34. 34.
    Suresh VS, Regalla SP, Gupta AK (2018) Optimum parameters to minimize weld line movement in the warm forming of tailor-welded blanks. J Braz Soc Mech Sci Eng 40:234CrossRefGoogle Scholar
  35. 35.
    Nele L, Sarno E, Keshari A (2013) Modeling of multiple characteristics of an arc weld joint. Int J Adv Manuf Technol 69:1331–1341CrossRefGoogle Scholar
  36. 36.
    Anawa EM, Olabi AG (2008) Using Taguchi method to optimize welding pool of dissimilar laser-welded components. Opt Laser Technol 40:379–388CrossRefGoogle Scholar
  37. 37.
    Benyounis KY, Olabi AG, Hashmi MS (2005) Effect of laser welding parameters on the heat input and weld-bead profile. J Mater Process Technol 164:978–985CrossRefGoogle Scholar
  38. 38.
    Kim IS, Son JS, Kim IG, Kim JY, Kim OS (2003) A study on relationship between process variables and bead penetration for robotic CO2 arc welding. J Mater Process Technol 136:139–145CrossRefGoogle Scholar
  39. 39.
    Patel T, Sheth S, Modi BS, Patel P (2015) Experimental investigation and comparison of regression model and artificial neural network to predict weld height in MIG welding for dual plate check valve. In: International conference on AMPDGoogle Scholar
  40. 40.
    Selvamani ST, Vigneshwar M, Palanikumar K, Jayaperumal D (2018) The corrosion behavior of fully deformed zone of friction welded low chromium plain carbon steel joints. J Braz Soc Mech Sci Eng 40:246CrossRefGoogle Scholar
  41. 41.
    Davis JR (2000) ASM specialty handbook: nickel, cobalt, and their alloys. ASM International, Materials ParkGoogle Scholar
  42. 42.
    Montgomery DC (2014) Design and analysis of experiments. Wiley, New DelhiGoogle Scholar
  43. 43.
    What is the difference between coded units and uncoded units (2018) Minitab® 18 support. https://support.minitab.com/en-us/minitab/18/help-and-how-to/modeling-statistics/doe/supporting-topics/basics/coded-units-and-uncoded-units/. Accessed 12 Jan 2018
  44. 44.
    AWS B3.0 (1977) Standard qualification procedure. American Welding Society, MiamiGoogle Scholar

Copyright information

© The Brazilian Society of Mechanical Sciences and Engineering 2018

Authors and Affiliations

  • Kalinga Simant Bal
    • 1
  • Jyotsna Dutta Majumdar
    • 2
  • Asimava Roy Choudhury
    • 1
  1. 1.Department of Mechanical EngineeringIndian Institute of TechnologyKharagpurIndia
  2. 2.Department of Metallurgical and Materials EngineeringIndian Institute of TechnologyKharagpurIndia

Personalised recommendations