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Optimizing cutting parameters in hard turning of AISI 52100 steel using TOPSIS approach

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In the present work optimization of cutting parameters is performed while hard turning of AISI 52100 steel with polycrystalline cubic boron nitride (PCBN) tools using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Experiments are planned and conducted based on Center Composite Rotatable Design (CCD) of the Response Surface Method (RSM). Cutting speed, feed, depth of cut, nose radius and negative rake angle are considered as input parameters. In this study machining force (F) and surface roughness (Ra) are measured during the experiment. Analysis of variance (ANOVA) is deployed to determine the influence of process parameters. Obtained optimal parameters are speed 200 rpm, feed 0.1 mm/rev, depth of cut 0.8 mm, nose radius 1.2 mm and negative rake angle 45º.
Rocznik
Strony
227--232
Opis fizyczny
Bibliogr. 20 poz., rys., tab., wykr.
Twórcy
  • Research Scholar, Department of Mechanical Engineering, Andhra University College of Engineering, Visakhapatnam, A.P. India
  • Department of Mechanical Engineering, Bapatla Engineering College, Bapatla, A.P. India
  • Department of Mechanical Engineering, Srinivasa Institute of Engineering and Technology, Amalapuram, A.P. India
  • Department of Mechanical Engineering, Andhra University College of Engineering, Visakhapatnam, A.P. India
  • Department of Mechanical Engineering, Bapatla Engineering College, Bapatla, A.P. India
Bibliografia
  • 1. König W., Hochschule T., Komanduri R., Schenectady D., Tönshoff H.K. (1984). Machining of hard materials. Ann CIRP, Vol. 33, No. 2, pp. 417–427.
  • 2. Tönshoff H.K., Arendt C., Amor R.B. (2000). Cutting of hardened steel. Ann CIRP, Vol. 49, No. 2, pp. 547-566.
  • 3. Mark Velasquez., Patrick T. Hester. (2013). An Analysis of Multi-Criteria Decision Making Methods. International Journal of Operations Research, Vol. 10, No. 2, pp. 56-66.
  • 4. Balasubramaniyan Singaravel., Thangiah Selvaraj. (2015). Optimization of machining parameters in turning operation using combined TOPSIS and AHP method. Tehnički vjesnik, Vol. 22, No. 6, pp. 1475-1480.
  • 5. Himadri Majumder., Abhijit Saha. (2018). Application of MCDM based hybrid optimization tool during turning of ASTM A588. Decision Science Letters, Vol. 7, pp. 143-156.
  • 6. Tian Syung Lan. (2009). Taguchi optimization of Multi objective CNC Machining using TOPSIS. Information technology journal, Vol. 8, No. 6, pp. 917-922.
  • 7. Palanisamy D., Senthil P. (2016). Optimization on Turning Parameters of 15-5PH Stainless Steel Using Taguchi Based Grey Approach and TOPSIS. Archive of Mechanical Engineering, LXIII (3), pp. 397-412.
  • 8. Maheswararao Ch., Venkata subbaiah K. (2016). Application of WSM, WPM and TOPSIS methods for the optimization of multiple responses. International journal of hybrid information technology, Vol. 9, No. 10, pp. 59-72.
  • 9. Sagar Bhise P., Pantanwane P.D., Rajiv B. (2014). Optimisation of Hard Turning of M42 Tool Steel Using PCR-TOPSIS Method. 5th International & 26th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) IIT Guwahati, Assam, India, 12th–14th December.
  • 10. Maity K., Khan A. (2017). Application of MCDM-based TOPSIS method for the selection of Optimal Process Parameter in Turning of Pure Titanium. Benchmarking: An International Journal, Vol. 24, No. 7, pp. 2009-2021.
  • 11. Singaravel B., Selvaraj T., Vinodh S. (2016). Multiobjective optimization of turning parameters using the combined MOORA and entropy method. Transactions of the Canadian Society for Mechanical Engineering, Vol. 40, No.1, pp. 101-111.
  • 12. Singaravel B., Prabhu Shankar D., Lakshmi Prasanna. (2018). Application of MCDM Method for the Selection of Optimum Process Parameters in Turning Process. Materials Today: Proceedings, Vol. 5, No. 5, pp. 13464-13471.
  • 13. Umamaheswarrao P., Ranga Raju D., NS Suman K.., Ravi Sankar B. (2018). Multiobjective optimization of Process parameters for hard turning of AISI 52100 steel using Hybrid GRA-PCA. Procedia Computer Science, Vol. 133, pp. 703-710.
  • 14. Umamaheswarrao P., Ranga Raju D., NS Suman K., Ravi Sankar B. (2019). Parametric optimization of surface roughness and workpiece surface temperature during hard turning of AISI 52100 steel using Hybrid GRA-PCA. In: 2nd International Conference on Computational Methods in Manufacturing, (ICCMM 2019) Indian Institute of Technology Guwahati, India, 89 March.
  • 15. Umamaheswarrao P., Ranga Raju D., NS Suman K., Ravi Sankar B. (2019). Achieving optimal process parameters during Hard Turning of AISI 52100 Bearing Steel using Hybrid GRA‐PCA. Key Engineering Materials, Vol. 818, pp. 87-91.
  • 16. Serra R., Chibane H., and Duchosal A. (2018). Multiobjective optimization of cutting parameters for turning AISI 52100 hardened steel. International Journal of Advanced Manufacturing Technology, Vol. 99, pp. 20252034.
  • 17. UmaMaheshwera Reddy P., Harish D., Suresh Kumar Reddy N. (2018). Application of Regression and Artificial Neural Network Analysis in Modelling of Surface Roughness in Hard Turning of AISI 52100 Steel. Materials Today: Proceedings, Vol. 5, No. 1, pp. 4766-4777.
  • 18. Swapna D., Srinivasa Rao Ch., Kumar S., Radhika S. (2019). AHP and TOPSIS Based Selection of Aluminium Alloy for Automobile Panels. Journal of Mechanical and Energy Engineering, Vol. 3(43), No. 1, pp. 43-50.
  • 19. Umamaheswarrao P., Ranga Raj D., NS Suman K., Ravi Sankar B. (2019). TOPSIS Based Optimization of Process Parameters While Hard Turning of AISI 52100 Steel. Acta Mechanica Malaysia, Vol. 2, Issue. 2, pp. 28-31.
  • 20. Yoon K Paul, and Ching-Lai Hwang. (1995). Multiple attribute decision making: an introduction. Sage publications.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-32eb2ecf-5196-4952-b16d-aafbd24d8228
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