Skip to content
2000
  1. Home
  2. A-Z Publications
  3. Recent Patents on Mechanical Engineering
  4. Fast Track Articles
  5. Fast Track Article
image of Combinatorial Method for Quality Improvement of the Thrust Plate – A Case Study

Abstract

Aim

This research aims to mitigate defects in the turning operation of thrust plates used in fighter jet fuel tank assemblies, thereby reducing the rejection rate and improving overall quality. This aligns with the aerospace industry's reliability goals.

Background

The thrust plate is a critical component in fighter jet fuel tank assembly, transmitting engine thrust to the airframe. Quality compromises in this component can impair jet performance. It was observed that the thrust plate had a rejection rate of about 2.9% due to various defects. This real-world scenario underscores the importance of our study on the thrust plate and its potential impact on the aerospace industry. The rejection rate underscores its significance and potential for patent by quality improvement in turning of the thrust plate.

Objective

The objective is to mitigate turning operation defects on the thrust plate to reduce rejection rates, aligning with aerospace industry reliability goals.

Methods

Experimentation encompassed four pivotal factors: turning speed, feed rate, cutting depth, and tool inserts, implemented through Taguchi's Orthogonal Array technique. Grey Relational Analysis was utilized to optimize parameters in thrust plate turning. Specifically, this paper targeted the enhancement of its diameter, surface roughness, and tool life.

Results

A single coefficient for the multiple responses, ., grey relational grade, has been determined, and optimum levels for the parameters have been identified. Confirmation experiments with the optimal factor level combination were carried out on a sample of thrust plates, and no rejections were observed.

Conclusion

An experimental design based on Taguchi’s orthogonal array approach was used to conduct the experiments. The Grey Relational Analysis has been applied to analyze the experimental results and optimize the turning operation process parameters for the responses thrust plate diameter, tool life, and surface roughness. With this, the rejection of the thrust plate has been considerably reduced.

© 2024 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/meng/10.2174/0122127976331438241001083014
2024-10-15
2024-11-23

  • From This Site

    /content/journals/meng/10.2174/0122127976331438241001083014
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Yang J.T. Liu S.F. Grey systems: Theory and application. Grey Syst. Theory Appl. 2011 4883 44 45
    [Google Scholar]
  2. Ju-Long D. Control problems of grey systems. Syst. Control Lett. 1982 1 5 288 294 10.1016/S0167‑6911(82)80025‑X
    [Google Scholar]
  3. Song J. Dang Y.G. Hua Z.M. Study on group decision-making method based on grey cluster model. Control Decis. 2010 25 10 1593 1597
    [Google Scholar]
  4. Wu L.F. Liu S.F. Yao L.G. Yan S.L. Grey convex relational degree and its application to evaluate regional economic sustainability. Sci. Iran. 2013 20 1 44 49
    [Google Scholar]
  5. Jian L.R. Liu S.F. Definition of grey degree in set theory and construction of grey rough set models. Control Decis. 2013 28 5 721 725
    [Google Scholar]
  6. Shivakumar M.R. Naidu N.V. Taguchi’s orthogonal array approach to study the wear behavior of aluminium alloy/soda lime glass composites. Int. J. Eng. Res. Technol. (Ahmedabad) 2013 2 08 2328 2334
    [Google Scholar]
  7. Phadke MS. Quality Engineering Using Robust Design Prentice Hall PTR United States 1995
    [Google Scholar]
  8. Taguchi G. Introduction to Quality Engineering: Designing Quality Into Products and Processes Asian Productivity Organization Tokyo 1986
    [Google Scholar]
  9. Shilpa M. Shivakumar T. Application of Taguchi’s parameter design for website compatibility testing. Int. J. Eng. Res. Technol. (Ahmedabad) 2016 5 16 28 32
    [Google Scholar]
  10. Chakradhar D Gopal AV Multi-objective optimization of electrochemical machining of EN31 steel by Grey Relational Analysis. Int. J. Model. Optim. 2011 1 2 113 117 10.7763/IJMO.2011.V1.20
    [Google Scholar]
  11. Gerçekcioğlu E. Albaşkara M. Multi-response optimization of electrical discharge machining of 17-4 PH SS using taguchi-based grey relational analysis. Arch. Metall. Mater. 2023 68 3 861 868 10.24425/amm.2023.145448
    [Google Scholar]
  12. Kumar A. Gulati V. Experimental investigation and optimization of surface roughness in negative incremental forming. Measurement 2019 131 419 430 10.1016/j.measurement.2018.08.078
    [Google Scholar]
  13. Yang YK. Lin SS. Wen JL. Optimization of 6061T6 CNC boring process using the taguchi method and grey relational analysis. Open Ind. Manuf. Eng. 2009 2 14 20 10.2174/1874152500902010014
    [Google Scholar]
  14. Sadasiva Rao T. Rajesh V. Venu Gopal A. Taguchi based grey relational analysis to optimize face milling process with multiple performance characteristics. InInternational Conference on Trends in Industrial and Mechanical Engineering 2012 166 170
    [Google Scholar]
  15. Kumar A. Gulati V. Experimental investigations and optimization of forming force in incremental sheet forming. Sadhana 2018 43 10 159 10.1007/s12046‑018‑0926‑7
    [Google Scholar]
  16. Balasubramanian S. Ganapathy S. Grey relational analysis to determine optimum process parameters for wire electro discharge machining (WEDM). Int. J. Eng. Sci. Technol. 2011 3 1 95 101
    [Google Scholar]
  17. Lu H.S. Chang C.K. Hwang N.C. Chung C.T. Grey relational analysis coupled with principal component analysis for optimization design of the cutting parameters in high-speed end milling. J. Mater. Process. Technol. 2009 209 8 3808 3817 10.1016/j.jmatprotec.2008.08.030
    [Google Scholar]
  18. Lin C.L. Use of the Taguchi method and grey relational analysis to optimize turning operations with multiple performance characteristics. Mater. Manuf. Process. 2004 19 2 209 220 10.1081/AMP‑120029852
    [Google Scholar]
  19. Fung CP, Manufacturing process optimization for wear property of fiber reinforced polybutylene tereph- thalate composites with grey relational analysis. Wear 254 3 2003 298 306 10.1016/S0043‑1648(03)00013‑9
    [Google Scholar]
  20. Kumar A. Gulati V. Optimization and investigation of process parameters in single point incremental forming. Indian J. Eng. Mater. Sci. 2021 27 2 246 255 [IJEMS].
    [Google Scholar]
  21. Tzeng C.J. Lin Y.H. Yang Y.K. Jeng M.C. Optimization of turning operations with multiple performance characteristics using the Taguchi method and Grey relational analysis. J. Mater. Process. Technol. 2009 209 6 2753 2759 10.1016/j.jmatprotec.2008.06.046
    [Google Scholar]
  22. Çaydaş U. Hasçalık A. Use of the grey relational analysis to determine optimum laser cutting parameters with multi-performance characteristics. Opt. Laser Technol. 2008 40 7 987 994 10.1016/j.optlastec.2008.01.004
    [Google Scholar]
  23. Kumar K. Kumar A. Singh V. Optimization of process parameters for erosion wear in slurry pipeline. Advances in Engineering Design Singapore Springer 2019 131 140 10.1007/978‑981‑13‑6469‑3_12
    [Google Scholar]
  24. Ertuğrul İ. Öztaş T. Özçil A. Öztaş GZ. Grey relational analysis approach in academic performance comparison of university a case study of Turkish universities. Eur. Sci. J. 2016 128 139
    [Google Scholar]
  25. Sarı T. Baynal K. Ergül Ö. Supplier selection with grey relational analysis. Int. J. Emerg. Res. Manag. Technol. 2016 5 4 61 70
    [Google Scholar]
  26. Ghule G.S. Sanap S. Chinchanikar S. Cep R. Kumar A. Bhave S.Y. Kumar R. Altarazi F. Investigation of conventional and ultrasonic vibration-assisted turning of hardened steel using a coated carbide tool. Front. Mech. Eng. 2024 10 1391315 10.3389/fmech.2024.1391315
    [Google Scholar]
  27. Kumar A. Shrivastava V.K. Kumar P. Kumar A. Gulati V. Predictive and experimental analysis of forces in die-less forming using artificial intelligence techniques. Proc. Inst. Mech. Eng., Part E 2024 10.1177/09544089241235473
    [Google Scholar]
  28. Jayaraman P. kumar L.M. Multi-response optimization of machining parameters of turning AA6063 T6 aluminium alloy using grey relational analysis in Taguchi method. Procedia Eng. 2014 97 197 204 10.1016/j.proeng.2014.12.242
    [Google Scholar]
  29. Xianzhen H. Mingwei H. Yimin Z. Changyou L. Lisha Z. Turning machining parameter optimization method considering uncertain parameters. 2017 CN Patent 107480354A
  30. Jordan N. Real-Time Adaptive Control of Manufacturing Processes Using Machine Learning. 2020 US Patent 2020166909A1
  31. Ando H. Igou H. Method of manufacturing thrust plate, method of manufacturing shaft for dynamic pressure bearing, dynamic pressure bearing, spindle motor and recording disc driving apparatus. 2004 US Patent 20050286166A1
  32. Ross P.J. Education, training, and implementation of design of experiments. Target 1988 18 21
    [Google Scholar]
  33. Li P. Liu S.F. Intuitionistic fuzzy numbers decision-making methods based on grey incidence analysis and DS theory of evidence. Proceedings of 2011 IEEE International Conference on Grey Systems and Intelligent Services Nanjing, China 2011 544 547 10.1109/GSIS.2011.6044099
    [Google Scholar]
  34. Liu S.F. Hu M.L. Forrest J. Yang Y.J. Progress of grey system models. J. Nanjing Univ. Aeronaut. Astronaut. 2012 29 2 103 111
    [Google Scholar]
  35. Wu L.F. Liu S.F. Panel data clustering method based on grey convex relation and its application. Control Decis. 2013 28 7 1033 1036
    [Google Scholar]
  36. Paramasivam S.S. Loganathan G.B. Kumaran D. Saravanan K. Rajendran R. Sriram H. Function of taguchi grey relation analysis for influencing the process parameter for getting better product quality and minimize the industrial pollution by coolants in turning of Ti-6Al-4V alloy. International Conference on Advances in Design, Materials, Manufacturing and Surface Engineering for Mobility 11-10-2019 2019 10.4271/2019‑28‑0065
    [Google Scholar]
  37. Osuchukwu O.A. Salihi A. Abdullahi I. Obada D.O. Taguchi grey relational optimization of sol–gel derived hydroxyapatite from a novel mix of two natural biowastes for biomedical applications. Sci. Rep. 2022 12 1 17968 10.1038/s41598‑022‑22888‑5 36289402
    [Google Scholar]
  38. Zhujani F. Abdullahu F. Todorov G. Kamberov K. Optimization of multiple performance characteristics for CNC turning of inconel 718 using Taguchi–grey relational approach and analysis of variance. Metals (Basel) 2024 14 2 186 10.3390/met14020186
    [Google Scholar]
  39. Chengal Reddy V. Keerthi T. Nishkala T. Maruthi Prasad Yadav G. Analysis and optimization of laser drilling process during machining of AISI 303 material using grey relational analysis approach. SN Appl. Sci. 2021 3 3 335 10.1007/s42452‑021‑04337‑6
    [Google Scholar]
/content/journals/meng/10.2174/0122127976331438241001083014
Loading
Combinatorial Method for Quality Improvement of the Thrust Plate – A Case Study
Bentham Science Publishers ; https://doi.org/10.2174/0122127976331438241001083014
/content/journals/meng/10.2174/0122127976331438241001083014
/content/journals/meng/10.2174/0122127976331438241001083014
Loading

Data & Media loading...


  • Article Type:     Research Article
Keywords: Thrust plate ; Tool life ; Surface roughness ; Diameter ; Grey Relational Analysis ; Taguchi method
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test