Optimization of machining parameters for surface roughness during abrasive water jet machining of aluminium/magnesium hybrid metal matrix composites

Maneiah, D.; Shunmugasundaram, M.; Reddy, A. Raji; Begum, Zareena

doi:10.1016/j.matpr.2020.02.264

Cited by 33 publications

(13 citation statements)

References 10 publications

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“…ANN and the regression model were used for surface roughness prediction in the AWJ cutting of AA 7075 aluminum alloy [ 9 ]. Additionally, different approaches have been applied for the investigation of surface roughness in the AWJ process, such as fuzzy logic in [ 10 ], the Taguchi-based analysis of variance method in [ 11 , 12 , 13 ], the multi-objective genetic algorithm (MOGA) in [ 14 ], and the regression method in [ 10 , 15 ]. Liu et al [ 16 ] developed quadratic regression models to predict the penetration depth and surface roughness in abrasive water jet turning of alumina ceramics using a response surface methodology with a Box–Behnken design.…”

Section: Introductionmentioning

confidence: 99%

“…Liu et al [ 16 ] developed quadratic regression models to predict the penetration depth and surface roughness in abrasive water jet turning of alumina ceramics using a response surface methodology with a Box–Behnken design. Different kinds of materials were subjected to the AWJ process, among them, carbon steel S235 [ 14 ], Hardox steel [ 15 ], magnesium alloy [ 10 ], aluminum alloy [ 17 , 18 ], titanium alloy [ 19 ], marble [ 20 ], aluminum/magnesium hybrid metal matrix composites [ 11 ], a lanthanum phosphate/yttria composite [ 12 ], and Nimonic C236 superalloy [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Surface Roughness of an Abrasive Water Jet Cut Using an Artificial Neural Network

et al. 2021

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The study’s primary purpose was to explore the abrasive water jet (AWJ) cut machinability of stainless steel X5CrNi18-10 (1.4301). The study analyzed the effects of such process parameters as the traverse speed (TS), the depth of cut (DC), and the abrasive mass flow rate (AR) on the surface roughness (Ra) concerning the thickness of the workpiece. Three different thicknesses were cut under different conditions; the Ra was measured at the top, in the middle, and the bottom of the cut. Experimental results were used in the developed feed-forward artificial neural network (ANN) to predict the Ra. The ANN’s model was validated using k-fold cross-validation. A lowest test root mean squared error (RMSE) of 0.2084 was achieved. The results of the predicted Ra by the ANN model and the results of the experimental data were compared. Additionally, as TS and DC were recognized, analysis of variance at a 95% confidence level was used to determine the most significant factors. Consequently, the ANN input parameters were modified, resulting in improved prediction; results show that the proposed model could be a useful tool for optimizing AWJ cut process parameters for predicting Ra. Its main advantage is the reduced time needed for experimentation.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of Surface Roughness of an Abrasive Water Jet Cut Using an Artificial Neural Network

et al. 2021

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“…Typically jewels are created from ruby, diamond, or sapphire, a "jewel" mounted in a steel insert. Its diameter ranges from 0.007" to 0.020" (0.178 -0.51) mm [16], notice figure 3. Secondary nozzle Sometimes refers to as mixing tube or Focusing tube.…”

Section: Methodsmentioning

confidence: 99%

“…This is a tube manufactured from a tough material that concentrates the water and abrasive into a coherent beam for cutting. Typically, a mixing tube has a diameter of 0.030″ (0.76 mm) [16], notice figure 3, to the abrasive water jet nozzle to cut efficiently and improve the life of components . The jewel orifice must be accurately aligned in the nozzle body.…”

Section: Methodsmentioning

confidence: 99%

Influence of Abrasive Water Jet (AWJ) on Surface Roughness

Nader

Saad

2021

IJEMM

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Abrasive water jet (AWJ) is one of the most advanced and valuable non-traditional machining processes because of its massive advantages of removing metals ranging from hard to soft. This paper focused on studying the influence of jet pressure, feed rate and standoff distance on surface roughness during cutting carbon steel using abrasive water jet cutting. A surface roughness device assessed the surface roughness by performing sixteen experiments to identify the distinct texture of the surface. Based on the experiences, the best surface roughness value was 3.14 μm at jet pressure 300 MPa, standoff distance 4mm and feed rate 30 mm/min. The Taguchi method was introduced to implement the experiments and indicate the most influential process parameters on average surface roughness. The experimental results reveal that feed rate has a significant effect on average surface roughness.

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“…Kale et al [ 28 ] investigated the material removal rate and surface roughness with the Taguchi and grey-relational analysis and combined it with analysis of variance (ANOVA) to determine the effect changing parameters on the analysed factors. The same method, Taguchi (ANOVA), was used by Maneiah et al, who examined metal matrix composites (MMCs) of Al-6061 [ 29 ]. Fuzzy logic and regression equations have also been employed to predict surface roughness in a number of studies, including the analysis of the abrasive waterjet machining of AZ91 magnesium alloy involving response surface methodology (RSM) [ 30 ].…”

Section: Introduction—state Of the Artmentioning

confidence: 99%

The Effect of Abrasive Waterjet Machining Parameters on the Condition of Al-Si Alloy

2020

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This paper analyses the effect of the abrasive waterjet cutting parameters’ modification on the condition of the workpiece surface layer. The post-machined surface of casting aluminium alloys, AlSi10Mg and AlSi21CuNi, was characterised in terms of surface roughness and irregularities, chamfering, and microhardness in order to reveal the effect that variable jet feed rate, abrasive flow rate, and sample height (thickness of the cut material) have on the quality of surface finish. From the analysis of the results, it emerges that the surface roughness remains largely unaffected by changes in the sample height h or the abrasive flow rate ma, whereas it is highly susceptible to the increase in the jet feed rate vf. It has been shown that, in principle, the machining does not produce the strengthening effect, that is, an increase in microhardness. Owing to the irregularities that are typically found on the workpieces cut with higher jet feed rates vf, additional surface finish operations may prove necessary. In addition, chamfering was found to occur throughout the entire range of speeds vf. The statistical significance of individual variables on the 2D surface roughness parameters, Ra/Rz/RSm, was determined using factorial analysis of variance (ANOVA). The results were verified by means of artificial neural network (ANN) modelling (radial basis function and multi-layered perceptron), which was employed to predict the surface roughness parameters under consideration. The obtained correlation coefficients show that ANNs exhibit satisfying predictive capacity, and are thus a suitable tool for the prediction of surface roughness parameters in abrasive waterjet (AWJ) technology.

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Optimization of machining parameters for surface roughness during abrasive water jet machining of aluminium/magnesium hybrid metal matrix composites

Cited by 33 publications

References 10 publications

Prediction of Surface Roughness of an Abrasive Water Jet Cut Using an Artificial Neural Network

Prediction of Surface Roughness of an Abrasive Water Jet Cut Using an Artificial Neural Network

Influence of Abrasive Water Jet (AWJ) on Surface Roughness

The Effect of Abrasive Waterjet Machining Parameters on the Condition of Al-Si Alloy

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