Abrasive waterjet machining characteristics of nylon 6 polymer matrix reinforced with seashell biofillers: experimental analysis, optimization, and prediction

被引:0
作者
Vasanthkumar, P. [1 ]
Perumal, G. [2 ]
Deepanraj, B. [3 ]
Senthilkumar, N. [4 ]
机构
[1] SRM Inst Sci & Technol, Ramapuram Campus, Chennai 600089, Tamil Nadu, India
[2] VRS Coll Engn & Technol, Arasur 607107, Tamil Nadu, India
[3] Prince Mohammad Bin Fahd Univ, Coll Engn, Dept Mech Engn, Al Khobar 31952, Saudi Arabia
[4] SIMATS, Saveetha Inst Med & Tech Sci, Saveetha Sch Engn, Dept Mech Engn, Chennai 602105, Tamil Nadu, India
来源
INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM | 2025年
关键词
Polymer composite; Sea shells; Abrasive waterjet machining; Desirability analysis; Support vector regression; Random forest regression; PERFORMANCE; BEHAVIOR; FILLER;
D O I
10.1007/s12008-025-02331-5
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The influence of abrasive waterjet machining parameters viz., water jet pressure (WJP), stand-off distance (SoD), nozzle diameter (ND), and traverse speed (TS) and garnet particle (GP) size towards the kerf angle (KA), average surface roughness (SR), and material removal rate (MRR), are investigated during machining a novel polymer composite made of Nylon-6 matrix, reinforced with seashell biofiller (15 wt.%). The novel polymer composite is fabricated using a twin-screw extruder and injection moulding. Experiments are designed with an L18 mixed-level orthogonal array of Taguchi's technique, and the outputs are simultaneously optimized using desirability analysis. Observation shows that as the GP size increases, the KA and SR tend to lower with higher MRR. The least SoD shows better results, whereas higher WJP and moderate TS produces higher MRR and lower SR. The ideal conditions of GP of 75 mu m, SoD of 1.03 mm, WJP of 225 MPa, TS of 40 mm/min, and ND of 2.5 mm, which predicts a SR of 1.705 mu m, KA of 1.449 degrees, and MRR of 77.106 g/min with a unified value of desirability is 0.883. The influence of GP size and WJP on the outputs is higher. An experiment confirms a maximum deviation of 12.61% between the predicted and experimental SR, whereas, for KA and MRR, it is about 4.76% and 1.28%. Prediction of outputs using machine learning approaches viz., support vector regression (SVR) and random forest regression (RFR) shows that higher prediction accuracy is achieved with RFR than SVR and regression model.
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页数:23
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