This paper introduces a modeling and optimization study on declineation angle (DA) produced by abrasive waterjet (AWJ) multi-pass and forward angling cuttings with machine learning algorithms (MLAs) for the first time. In this regards, several rock samples such as marble, travertine, basalt, onyx, and tuffs were pre-dimensioned and subjected to AWJ cutting tests. Then, the DA was measured for each rock type and the recorded data was utilized for modeling and optimization by various MLAs. Additionally, the proposed models’ robustness was evaluated using several statistical indices. Moreover, a random forest regressor was employed to determine the relative importance of predictor variables influencing the DA. It was found that the multi-pass and forward angling cuttings have superiorities for reducing the DA. In addition, the artificial neural network, gradient boosted decision trees, and Gaussian process regression were determined as the first, second, and third best models in modeling the DA respectively. Furthermore, the most significant variables influencing the DA were identified as the number of pass and traverse speed. Finally, it was found that the particle swarm optimization algorithm is a successful tool for establishing the optimum variable design.
AydinGKarakurtIHamzacebiC. Artificial neural network and regression models for performance prediction of abrasive waterjet in rock cutting. J Adv Manuf Technol2014; 75: 1321–1330.
2.
YangTZhaoWZhuX, et al.Experimental investigation of the distribution patterns of micro-scratches in abrasive waterjet cutting surface. Sci Rep2024; 14: 18731.
3.
WangHYuanRZhangX, et al.Research progress in abrasive water jet processing technology. Micromachines2023; 14: 1526.
4.
MorissetRGillesPCohenG, et al.Characterization of abrasion and erosion mechanisms during abrasive waterjet machining of hard metals. Key Eng Mater2022; 926: 1575–1580.
5.
LemmaEChenLSioresE, et al.Optimising the AWJ cutting process of ductile materials using nozzle oscillation technique. Int J Mach Tools Manuf2002; 42: 781–789.
6.
WangJGuoDM. The cutting performance in multipass abrasive waterjet machining of industrial ceramics. J Mater Process Technol2003; 133: 371–377.
7.
HashishM.Precision cutting of thick materials with AWJ. In: 17th International conference on water jetting (ed. GeeC.), Mainz, Germany, 7–9 September 2004, pp.33–45.
8.
WangJZhongY. Enhancing the depth of cut in abrasive waterjet cutting of alumina ceramics by using multipass cutting with nozzle oscillation. Mach Sci Technol2009; 13: 76–91.
9.
WangJ. Predictive depth of jet penetration models for abrasive waterjet cutting of alumina ceramics. Int J Mech Sci2007; 49: 306–316.
10.
ShanmugamDKWangJLiuH. Minimisation of kerf tapers in abrasive waterjet machining of alumina ceramics using a compensation technique. Int J Mach Tools Manuf2008; 48: 1527–1534.
11.
WangJ. Depth of cut models for multipass abrasive waterjet cutting of alumina ceramics with nozzle oscillation. Front Mech Eng2010; 5: 19–32.
12.
DeksterLKarkalosENObratanskiKP, et al.Evaluation of the machinability of Ti-6Al-4V titanium alloy by AWJM using a multipass strategy. Appl Sci2023; 13: 3774.
13.
OhTChoG. Rock cutting depth model based on kinetic energy of abrasive waterjet. Rock Mech Rock Eng2016; 49: 1059–1072.
14.
De AbreuELimaCELebronR, et al.Study of influence of traverse speed and abrasive mass flowrate in abrasive water jet machining of gemstones. J Adv Manuf Technol2016; 83: 77–87.
15.
AlsoufiSMSukerKDAlhazmiWM, et al.Abrasive waterjet machining of thick carrara marble: cutting performance vs profile, lagging and waterjet angle assessments. Mater Sci Appl2017; 8: 361–375.
16.
ZhaoJZhangGXuY, et al.Mechanism and effect of jet parameters on particle waterjet rock breaking. Powder Technol2017; 313: 231–244.
17.
AydinGKayaSKarakurtI. Utilization of solid-cutting waste of granite as an alternative abrasive in abrasive waterjet cutting of marble. J Clean Prod2017; 159: 241–247.
18.
JiangHLiuaZGaodK. Numerical simulation on rock fragmentation by discontinuous water-jet using coupled SPH/FEA method. Powder Technol2017; 312: 248–259.
19.
TripathiRHlochSChattopadhyayaS, et al.Application of the pulsating and continous water jet for granite erosion. Int J Rock Mech Min Sci2020; 126: e104209.
20.
LiBZhangBHuMM, et al.Full-scale linear cutting tests to study the influence of pre-groove depth on rock-cutting experimen performance by TBM disc cutter. Tunn Undergr Space Technol2022; 122: e104366.
21.
OhMTJooWGChaY, et al.Effect of garnet characteristics on abrasive waterjet cutting of hard granite rock. Adv Civ Eng2019; 2019: Article ID 5732649, 12 pages. DOI: 10.1155/2019/5732649.
22.
AydinGKayaSKarakurtI. Effect of abrasive type on marble cutting performance of abrasive waterjet. Arab J Geosci2019; 2: 356–363.
23.
KayaSAydinGKarakurtI. An experimental study on the cutting depth produced by abrasive waterjet: how do abrasive and rock properties affect the cutting process?J Adv Manuf Technol2023; 123: 4811–4823.
24.
ChaYKimWJKimSJ, et al.Assessment of abrasive impact frequency depending on the traverse rate in waterjet rock cutting. In: 2021 World congress on advances in structural engineering and mechanics (eds de Oliveira CorreiaJAFChoudhuryvSDuttaS), Gece-Seoul, Korea, 23–26 August 2021a, pp. 1–9.
25.
AydinGKarakurtIAmiriRM, et al.Improvement of rock cutting performance through two pass abrasive waterjet cutting. Sustainability2022; 14: e12704.
26.
GeZShangguanJZhouZ, et al.Investigation of fracture damage and breaking energy consumption of hard rock repeatedly cut by abrasive waterjet. Rock Mech Rock Eng2023; 56: 3215–3230.
27.
ChaYLeeLHKimSJ, et al.Granite cutting performance depending on the traverse speed in abrasive waterjet multi-cutting. J Adv Manuf Technol2024; 132: 3771–3783.
28.
ChaYOhMTHwangJH, et al.Simple approach for evaluation of abrasive mixing efficiency for abrasivewaterjet rock cutting. Appl Sci2021b; 11: 1543.
29.
LiLWangFLiT, et al.The effects of inclined particle water jet on rock failure mechanism: experimental and numerical study. J Petrol Sci Eng2020; 185: e106639.
30.
LiuJZhuYDuS. Failure mechanism and effect of nozzle parameters on abrasive water jet rock breaking. Shock Vib2021: 1–14.
31.
LiXFLiHBZhangGK, et al.Rate dependency mechanism of crystalline rocks induced by impacts: insights from grain-scale fracturing and micro heterogeneity. Int J Impact Eng2021; 155: 103855.
32.
BarabasSFlorescuA. Reduction of cracks in marble appeared at hydro-abrasive jet cutting using the Taguchi method. Materials2022; 15: 486.
33.
ChomkaGKasperowiczMChodórJ, et al.Possibilities of rock processing with a high-pressure abrasive waterjet with an aspect terms to minimizing energy consumption. Materials2023; 16: 647.
34.
FanCZhangHKangY, et al.Rock breaking performance of the newly proposed unsubmerged cavitating abrasive waterjet. Int J Min Sci Technol2023; 33: 843–853.
35.
LiuFWangYHuangX. Cutting efficiency of extremely hard granite by high-pressure water jet and prediction model of cutting depth based on energy method. Bull Eng Geol Environ2024; 83: 95.
36.
EN 14157. Natural stone test methods – determination of the abrasion resistance. Brussels, Belgium: CEN European Committee for Standardization, 2017.
37.
ISRM. The complete ISRM suggested methods for rock characterization, testing and monitoring: 1974–2006 (eds UlusayRHudsonJA). Kozan Ofset, Ankara, 2017, 628p.
38.
WangSYangFHuD, et al.Modelling and analysis of abrasive water jet cutting front profile. J Adv Manuf Technol2021; 114: 2829–2837.
39.
OrbanicHJunkarM. Analysis of striation formation mechanism in abrasive water jet cutting. Wear2008; 265: 821–830.
40.
AliramezaniMKochCRShahbakhtiM. Modeling, diagnostics, optimization, and control of internal combustion engines via modern machine learning techniques: a review and future directions. Prog Energy Combust Sci2022; 88: e100967.
41.
GongWXuHLuJ, et al.Gradient boosting decision tree algorithms for accelerating nanofiltration membrane design and discovery. Desalination2024; 592: 118072.
42.
WangYZhangKShiS, et al.Measurement of particle size in suspension based on VIS-NIR-RGB sensors and gradient-boosted regression tree. Measurement2025; 240: e115570.
43.
XuCFuLLinT, et al.Machine learning in petrophysics: advantages and limitations. Artif Intell Geosci2022; 3: 157–161.
44.
ChenJZhaoZZhangJ. Predicting peak shear strength of rock fractures using tree-based models and convolutional neural network. Comput Geotech2024; 166: 105965.
45.
YuanZZhengWQiaoH. Machine learning based optimization for mix design of manufactured sand concrete. Constr Build Mater2025; 467: e140256.
46.
WuYCFengJW. Development and application of artificial neural network. Wirel Pers Commun2018; 102: 1645–1656.
47.
XuNWangZDaiY, et al.Prediction of higher heating value of coal based on gradient boosting regression tree model. Int J Coal Geol2023; 274: e104293.
48.
ÇaydaşUHasçalikA. A study on surface roughness in abrasive waterjet machining process using artificial neural networks and regression analysis method. J Mater Process Technol2008; 202: 574–582.
49.
XieCNguyenHBuiNX, et al.Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms. Geosci Front2021; 12: 101108.
50.
WuJZhaoGWangM, et al.Concrete carbonation depth prediction model based on a gradient-boosting decision tree and different metaheuristic algorithms. Case Stud Constr Mater2024; 21: e03864.
51.
GuangxingGWeijunZZhenyeS, et al.An aero-structure-acoustics evaluation framework of wind turbine blade cross-section based on Gradient Boosting regression tree. Compos Struct2024; 337: e118055.
52.
KongDChenYLiN. Gaussian process regression for tool wear prediction. Mech Syst Signal Process2018; 104: 556–574.
53.
KarabacakEY. Intelligent milling tool wear estimation based on machine learning algorithms. J Mech Sci Technol2024; 38: 835–850.
54.
FangDZhangXYuQ, et al.A novel method for carbon dioxide emission forecasting based on improved Gaussian processes regression. J Clean Prod2018; 173: 143–150.
LuhCGLinYC. Optimal design of truss-structures using particle swarm optimization. Comput Struct2011; 89: 2221–2232.
57.
AzadehASaberiMAsadzadehSM, et al.A hybrid fuzzy mathematical programming-design of experiment framework for improvement of energy consumption estimation with small data sets and uncertainty: the cases of USA, Canada, Singapore, Pakistan and Iran. Energy2011; 36: 6981–6992.
58.
KimSKimH. A new metric of absolute percentage error for intermittent demand forecasts. Int J Forecast2016; 32: 669–679.
59.
ByrneRF. Beyond traditional time-series: using demand sensing to improve forecasts in volatile times. J Bus Forecast: Meth Syst2012; 31: 13–20.
60.
PaivaHAfonsoRJMCaldeiraFMSLA, et al.A computational tool for trend analysis and forecast of the COVID-19 pandemic. Appl Soft Comput2021; 105: e107289.
61.
LewisCD. Industrial and business forecasting methods. Butterworths, London, 1982, p.143.
62.
AydinGKarakurtIAydinerK. Prediction of the cut depth of granitic rocks machined by abrasive waterjet (AWJ). Rock Mech Rock Eng2013; 46: 1223–1235.
63.
ArabBPCelestinoBT. A microscopic study on kerfs in rocks subjected to abrasive waterjet cutting. Wear2020; 448-449: e203210.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
