Research Article
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Year 2024, Volume: 42 Issue: 2, 516 - 528, 30.04.2024

Fuat Kartal Arslan Kaptan

Abstract

References

  • REFERENCES
  • [1] Kumar SN, Sasidhar P, Rajyalakshmi M, Vandana KV. Experimental ınvestigation of optimization of machining parameters in abrasive water jet machining. Adv Sci Technol 2022;120:101109. [CrossRef]
  • [2] Bañon F, Sambruno A, González-Rovira L, Vazquez-Martinez JM, Salguero J. A review on the abrasive water-jet machining of metal–carbon fiber hybrid materials. Metals 2021;11:164. [CrossRef]
  • [3] Kumaran ST, Ko TJ, Uthayakumar M, Islam MM. Prediction of surface roughness in abrasive water jet machining of CFRP composites using regression analysis. J Alloys Compd 2017;724:10371045. [CrossRef]
  • [4] Çaydaş U, Hascalık A. A study on surface roughness in abrasive waterjet machining process using artificial neural networks and regression analysis method. J Mater Process Technol 2008;202:574582. [CrossRef]
  • [5] Parikh PJ, Lam SS. Parameter estimation for abrasive water jet machining process using neural networks. Int J Adv Manuf Technol 2009;40:497502. [CrossRef]
  • [6] Kartal F. A review of the current state of abrasive water-jet turning machining method. Int J Adv Manuf Technol 2017;88:495505. [CrossRef]
  • [7] Minnicino M, Gray D, Moy P. Aluminum alloy 7068 mechanical characterization. Army Res Lab Aberdeen Proving Ground Md Weapons And Materials Research Directorate, 2009. [CrossRef]
  • [8] Gunamgari BR, Kharub M. Experimental investigation on abrasive water jet cutting of high strength aluminium 7068 alloy. Mater Today Proc 2022;69:488493. [CrossRef]
  • [9] Agatonovic-Kustrin S, Beresford R. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal 2000;22:717727. [CrossRef]
  • [10] Dongare AD, Kharde RR, Kachare AD. Introduction to artificial neural network. Int J Eng Innov Technol 2012;2:189194.
  • [11] Livingstone DJ, editor. Artificial neural networks: methods and applications. Totowa, NJ, USA: Humana Press; 2008. p. 185202.
  • [12] Wang S, Hu D, Yang F, Tang C, Lin P. Exploring cutting front profile in abrasive water jet machining of aluminum alloys. Int J Adv Manuf Technol 2021;112:845851. [CrossRef]
  • [13] Kumar SP, Shata AS, Kumar KP, Sharma R, Munnur H, Rinawa ML, Kumar SS. Effect on abrasive water jet machining of aluminum alloy 7475 composites reinforced with CNT particles. Mater Today Proc 2022;59:14631471. [CrossRef]
  • [14] Wang S, Hu D, Yang F, Lin P. Investigation on kerf taper in abrasive waterjet machining of aluminum alloy 6061-T6. J Mater Res Technol 2021;15:427433. [CrossRef]
  • [15] Ahmed TM, El Mesalamy AS, Youssef A, El Midany TT. Improving surface roughness of abrasive waterjet cutting process by using statistical modeling. CIRP J Manuf Sci Technol 2018;22:3036. [CrossRef]
  • [16] Akkurt A, Kulekci MK, Seker U, Ercan F. Effect of feed rate on surface roughness in abrasive waterjet cutting applications. J Mater Process Technol 2004;147:389396. [CrossRef]
  • [17] Bañon F, Sambrun A, Mayuet PF, Gómez-Parra Á. Study of abrasive water jet machining as a texturing operation for thin aluminum alloy UNS A92024. Materials 2023;16:3843. [CrossRef] [18] Lv Z, Hou R, Cui H, Zhang M, Yun H. Numerical study on fatigue crack behavior of 2024 Al alloy in abrasive waterjet peening. Int J Adv Manuf Technol. 2023;127: 2979–2988. [CrossRef]
  • [19] Sun S, Qian YN, Lu W, Wu S, Kang Y, Tan A, Li D. Improving the cutting quality of aluminum alloy machined by abrasive waterjet with a relatively low pressure. J Braz Soc Mech Sci Eng. 2023;45:377. [CrossRef]
  • [20] AZO Maerials. Available from: https://www.azom.com/article.aspx?ArticleID=8758. Access date: 15 Feb 2023.
  • [21] Ficko M, Begic-Hajdarevic D, Cohodar Husic M, Berus L, Cekic A, Klancnik S. Prediction of surface roughness of an abrasive water jet cut using an artificial neural network. Materials 2021;14:3108. [CrossRef]
  • [22] Joel C, Jeyapoovan T, Kumar PP. Experimentation and optimization of cutting parameters of abrasive jet cutting on AA6082 through response surface methodology. Mater Today Proc 2021;44:35643570. [CrossRef]
  • [23] Ćojbašić Ž, Petković D, Shamshirband S, Tong CW, Ch S, Janković P, et al. Surface roughness prediction by extreme learning machine constructed with abrasive water jet. Precis Eng. 2016;43:8692. [CrossRef]
  • [24] Ramakrishnan S, Singaravelu DL, Senthilkumar V. Optimization of AWJC Parameters for Ti-6Al-4V Alloy Using ANN-Based GA and PSO. In: Recent Advances in Materials Technologies: Select Proceedings of ICEMT 2022. Singapore: Springer Nature Singapore; 2022. p. 591606. [CrossRef]
  • [25] Maneiah D, Shunmugasundaram M, Reddy AR, Begum Z. Optimization of machining parameters for surface roughness during abrasive water jet machining of aluminium/magnesium hybrid metal matrix composites. Mater Today Proc 2020;27:12931298. [CrossRef]

Artificial neural network and multiple regression analysis for predicting abrasive water jet cutting of Al 7068 aerospace alloy

Year 2024, Volume: 42 Issue: 2, 516 - 528, 30.04.2024

Fuat Kartal Arslan Kaptan

Abstract

This study aims to predict machinability and high performance optimum surface roughness
(Ra) by developing multiple regression models and artificial neural network (ANN) model
for abrasive water jet cutting (AWJC) of Aluminum 7068 alloy. Important basic processing parameters such as pump pressure (3500-4000 Bar), nozzle distance (2-5 mm), abrasive flow rate (200-350 g/min), abrasive grain size (100-110 mesh), and nozzle traverse speed (240-
300 mm/min) were selected in the study. To examine the effects of these parameters on Ra, 32 experiments were conducted using the L32 orthogonal array, and data was collected. Ad-
ditionally, the most important factors and interactions affecting Ra were determined using multiple regression analysis and analysis of variance (ANOVA). The Artificial Neural Network (ANN) model was designed to have multiple hidden layers using MATLAB. The model was trained and evaluated using experimental data, and its performance was measured using mean squared error (MSE) and mean absolute error (MAE). The model was optimized using hyper parameter tuning and cross-validation techniques. As a result, it was determined that the best R2 value of 95.65% from the multiple regression models created to estimate the surface rough-ness could be obtained from the linear regression model. While selecting the optimum process parameters for AWJC, it was determined that nozzle rotation speed, abrasive grain size and flow rate had the greatest effect by 35.5%, 25.4% and 21.9%, respectively. The optimized ANN
model showed high accuracy in predicting Ra for different input parameter combinations. This study provides a reliable and efficient tool for predicting Ra in AWJC, which can contrib-ute to improving process planning and control.

Keywords

Abrasive Water Jet Cutting Artificial Neural Network Aerospace Alloy Al 7068 Surface Roughness ANOVA

References

  • REFERENCES
  • [1] Kumar SN, Sasidhar P, Rajyalakshmi M, Vandana KV. Experimental ınvestigation of optimization of machining parameters in abrasive water jet machining. Adv Sci Technol 2022;120:101109. [CrossRef]
  • [2] Bañon F, Sambruno A, González-Rovira L, Vazquez-Martinez JM, Salguero J. A review on the abrasive water-jet machining of metal–carbon fiber hybrid materials. Metals 2021;11:164. [CrossRef]
  • [3] Kumaran ST, Ko TJ, Uthayakumar M, Islam MM. Prediction of surface roughness in abrasive water jet machining of CFRP composites using regression analysis. J Alloys Compd 2017;724:10371045. [CrossRef]
  • [4] Çaydaş U, Hascalık A. A study on surface roughness in abrasive waterjet machining process using artificial neural networks and regression analysis method. J Mater Process Technol 2008;202:574582. [CrossRef]
  • [5] Parikh PJ, Lam SS. Parameter estimation for abrasive water jet machining process using neural networks. Int J Adv Manuf Technol 2009;40:497502. [CrossRef]
  • [6] Kartal F. A review of the current state of abrasive water-jet turning machining method. Int J Adv Manuf Technol 2017;88:495505. [CrossRef]
  • [7] Minnicino M, Gray D, Moy P. Aluminum alloy 7068 mechanical characterization. Army Res Lab Aberdeen Proving Ground Md Weapons And Materials Research Directorate, 2009. [CrossRef]
  • [8] Gunamgari BR, Kharub M. Experimental investigation on abrasive water jet cutting of high strength aluminium 7068 alloy. Mater Today Proc 2022;69:488493. [CrossRef]
  • [9] Agatonovic-Kustrin S, Beresford R. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal 2000;22:717727. [CrossRef]
  • [10] Dongare AD, Kharde RR, Kachare AD. Introduction to artificial neural network. Int J Eng Innov Technol 2012;2:189194.
  • [11] Livingstone DJ, editor. Artificial neural networks: methods and applications. Totowa, NJ, USA: Humana Press; 2008. p. 185202.
  • [12] Wang S, Hu D, Yang F, Tang C, Lin P. Exploring cutting front profile in abrasive water jet machining of aluminum alloys. Int J Adv Manuf Technol 2021;112:845851. [CrossRef]
  • [13] Kumar SP, Shata AS, Kumar KP, Sharma R, Munnur H, Rinawa ML, Kumar SS. Effect on abrasive water jet machining of aluminum alloy 7475 composites reinforced with CNT particles. Mater Today Proc 2022;59:14631471. [CrossRef]
  • [14] Wang S, Hu D, Yang F, Lin P. Investigation on kerf taper in abrasive waterjet machining of aluminum alloy 6061-T6. J Mater Res Technol 2021;15:427433. [CrossRef]
  • [15] Ahmed TM, El Mesalamy AS, Youssef A, El Midany TT. Improving surface roughness of abrasive waterjet cutting process by using statistical modeling. CIRP J Manuf Sci Technol 2018;22:3036. [CrossRef]
  • [16] Akkurt A, Kulekci MK, Seker U, Ercan F. Effect of feed rate on surface roughness in abrasive waterjet cutting applications. J Mater Process Technol 2004;147:389396. [CrossRef]
  • [17] Bañon F, Sambrun A, Mayuet PF, Gómez-Parra Á. Study of abrasive water jet machining as a texturing operation for thin aluminum alloy UNS A92024. Materials 2023;16:3843. [CrossRef] [18] Lv Z, Hou R, Cui H, Zhang M, Yun H. Numerical study on fatigue crack behavior of 2024 Al alloy in abrasive waterjet peening. Int J Adv Manuf Technol. 2023;127: 2979–2988. [CrossRef]
  • [19] Sun S, Qian YN, Lu W, Wu S, Kang Y, Tan A, Li D. Improving the cutting quality of aluminum alloy machined by abrasive waterjet with a relatively low pressure. J Braz Soc Mech Sci Eng. 2023;45:377. [CrossRef]
  • [20] AZO Maerials. Available from: https://www.azom.com/article.aspx?ArticleID=8758. Access date: 15 Feb 2023.
  • [21] Ficko M, Begic-Hajdarevic D, Cohodar Husic M, Berus L, Cekic A, Klancnik S. Prediction of surface roughness of an abrasive water jet cut using an artificial neural network. Materials 2021;14:3108. [CrossRef]
  • [22] Joel C, Jeyapoovan T, Kumar PP. Experimentation and optimization of cutting parameters of abrasive jet cutting on AA6082 through response surface methodology. Mater Today Proc 2021;44:35643570. [CrossRef]
  • [23] Ćojbašić Ž, Petković D, Shamshirband S, Tong CW, Ch S, Janković P, et al. Surface roughness prediction by extreme learning machine constructed with abrasive water jet. Precis Eng. 2016;43:8692. [CrossRef]
  • [24] Ramakrishnan S, Singaravelu DL, Senthilkumar V. Optimization of AWJC Parameters for Ti-6Al-4V Alloy Using ANN-Based GA and PSO. In: Recent Advances in Materials Technologies: Select Proceedings of ICEMT 2022. Singapore: Springer Nature Singapore; 2022. p. 591606. [CrossRef]
  • [25] Maneiah D, Shunmugasundaram M, Reddy AR, Begum Z. Optimization of machining parameters for surface roughness during abrasive water jet machining of aluminium/magnesium hybrid metal matrix composites. Mater Today Proc 2020;27:12931298. [CrossRef]
There are 25 citations in total.

Details

Primary Language English
Subjects Biochemistry and Cell Biology (Other)
Journal Section Research Articles
Authors

Fuat Kartal 0000-0002-2567-9705

Arslan Kaptan 0000-0002-2431-9329

Publication Date April 30, 2024
Submission Date May 11, 2023
Published in Issue Year 2024 Volume: 42 Issue: 2

Cite

Vancouver Kartal F, Kaptan A. Artificial neural network and multiple regression analysis for predicting abrasive water jet cutting of Al 7068 aerospace alloy. SIGMA. 2024;42(2):516-28.

IMPORTANT NOTE: JOURNAL SUBMISSION LINK https://eds.yildiz.edu.tr/sigma/