Boundary condition for deformation wear mode material removal in abrasive waterjet milling: Theoretical and experimental analyses

Srikanth, R.; Babu, N. Ramesh

doi:10.1177/0954405417718594

Cited by 9 publications

(6 citation statements)

References 34 publications

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“…where x, y , and z are the coordinates referring to the abrasive jetting origin in accelerator tank, t the time, ρ a the air density, ρ w the water density, H the acceleration of mass point in the air, D the diameter of abrasive grit, and C is a drag coefficient expressed as the function of Reynolds number based on the concentrated stream and the kinematic viscosity of air. 24…”

Section: Multi-phase Flow Models and Stream Effectiveness Indexesmentioning

confidence: 99%

“…where x, y, and z are the coordinates referring to the abrasive jetting origin in accelerator tank, t the time, r a the air density, r w the water density, H the acceleration of mass point in the air, D the diameter of abrasive grit, and C is a drag coefficient expressed as the function of Reynolds number based on the concentrated stream and the kinematic viscosity of air. 24 Since abrasive jetting stream is very difficult to be measured directly, typical flow models should be used for flow demonstration and principle investigation in practices, to study their constructive influences on the prediction of stream effectiveness, with the k-e, k-e-R t , Spalart-Allmaras, Reynolds stress, and large-eddy simulation (LES) models be focused sequentially. 25 Besides, some calibration properties selected out as the qualified mathematical indexes calibrating stream effectiveness, such as turbulence RMS velocity (V RMS , m/s), turbulence intensity (T i ), turbulence kinetic energy (T c , kJ), turbulence entropy (T e , J/K), and Reynolds shear stress (R s , kPa), 26 are employed to quantify abrasive jetting stream thanks to their clear illustrations of physical properties and working effectiveness of multi-phase turbulence and keep independent from external signals or environmental interference.…”

Section: Multi-phase Flow Models and Stream Effectiveness Indexesmentioning

confidence: 99%

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Effectiveness prediction of abrasive jetting stream of accelerator tank using normalized sparse autoencoder-adaptive neural fuzzy inference system

Liang

Liu

Wen

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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Abrasive jetting stream generated from accelerator tank is crucial to the precision machining of industrial products during the process of strengthen jet grinding. In this article, its effectiveness prediction using normalized sparse autoencoder-adaptive neural fuzzy inference system is carried out to provide an optimal result of jetting stream. A normalized sparse autoencoder-adaptive neural fuzzy inference system capable of calculating the concentration density of abrasive impact stress by normalized sparse autoencoder and identifying the effectiveness indexes of abrasive jetting by adaptive neural fuzzy inference system is proposed to predict the stream effectiveness index in grinding practices, indicating that when turbulence root-mean-square velocity ( VRMS) is 420 m/s, turbulence intensity ( Ti) is 570, turbulence kinetic energy ( Tc) is 540 kJ, turbulence entropy ( Te) is 620 J/K, and Reynolds shear stress ( Rs) is 430 kPa (Error tolerance = ± 5%, the same as follows), the optimized effectiveness quality of abrasive jetting stream could be ensured. The effectiveness prediction involve the following steps: measuring the jet impact data on the interior boundary surface of accelerator tank, calculating the concentration density of abrasive impact stress, establishing the descriptive analytical frame work of normalized sparse autoencoder-adaptive neural fuzzy inference system, adaptive prediction of abrasive jetting stream effectiveness through normalized sparse autoencoder-adaptive neural fuzzy inference system computation, and performance verification of actual effectiveness prediction in the efficiency quantification and quality assessment when it compared to that of alternative approaches, such as genetic, simulated annealing–genetic algorithm, Taguchi, artificial neural network–simulated annealing, and genetically optimized neural network system methods. Objective of this research is to adaptive predict the abrasive jetting stream effectiveness using a new-proposed prediction system, a stable and reliable abrasive jetting stream therefore can be achieved using jetting pressure ( Pw) at 320 MPa, mass of cast steel grits ( Mc) at 270 g, mass of bearing steel grits ( Mb) at 310 g, mass of brown-fused alumina grits ( Ma) at 360 g, and mass rate of abrasives ( Fa) at 0.46 kg/min. It is concluded that normalized sparse autoencoder-adaptive neural fuzzy inference system owns an outstanding predictive capability and possesses a much better working advancement in typical calibration indexes of accuracy and efficiency, meanwhile a high agreement between the fuzzy predicted and actual measured values of effectiveness indexes is ensured. This novel method could be promoted constructively to improve the quality uniformity for abrasive jetting stream and to facilitate the productive managements of abrasive jet machining consequently.

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Section: Multi-phase Flow Models and Stream Effectiveness Indexesmentioning

confidence: 99%

Section: Multi-phase Flow Models and Stream Effectiveness Indexesmentioning

confidence: 99%

Effectiveness prediction of abrasive jetting stream of accelerator tank using normalized sparse autoencoder-adaptive neural fuzzy inference system

Liang

Liu

Wen

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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show abstract

“…2 AWJ was initially utilized for linear cutting of plates, but it has kept evolving to performing controlled depth processes such as milling and turning. 3 Hashish 4 reported that abrasive waterjet turning (AWJT) can efficiently turn carbide-metal composites, glass, and ceramics. This process exploits the eroding action of AWJ to produce axisymmetric workpieces using the same principles of centre lathe turning where the AWJ replaces the traditional tool insert.…”

Section: Introductionmentioning

confidence: 99%

A user-friendly finite element model for radial mode abrasive waterjet turning

Abdelhameed

Hassan

Kaytbay

2021

Proceedings of the Institution of Mechanical Engineers, Part B:

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This paper aims to develop a finite element (FE) model precisely simulating the multi-particle impact in the radial mode abrasive waterjet turning (AWJT). An explicit dynamic analysis was carried out to predict the crater profile resulting from the impact of the abrasive particles along a limited segment of the jet pass over the workpiece surface. The effect of both momentum transfer loss and abrasive load ratio was taken into consideration while calculating the impact velocity of the abrasive particles. To build a user-friendly model, the scripting feature of ABAQUS was involved to automatically perform all the repetitive modeling procedures. The presented FE model considers four variable turning parameters tested at five levels each, including impact velocity, abrasive mass flow rate, traverse rate, and workpiece speed. The obtained crater profile from the simulation process was utilized to calculate the depth of cut (DOC) at different parameter combinations. A comparison between the numerical and experimental results shows a good agreement with an average absolute relative error of 9.74%.

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“…Prediction of depth of cut in MMCs has been carried out using theoretical models [12], empirical models [13] and semi-empirical models [14]. Theoretical models are difficult to develop in the case of MMCs because they depend on the equations that govern the physics associated with the cutting process.…”

Section: Introductionmentioning

confidence: 99%

Semi-empirical model for depth of cut in abrasive waterjet machining of metal matrix composites

Mohankumar

Kanthababu

2020

J Braz. Soc. Mech. Sci. Eng.

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Abrasive waterjet machining (AWJM) is one of the versatile unconventional machining processes presently followed in several industries in the machining of variety of engineering materials. Achieving maximum depth of penetration is a prime requirement to achieve higher productivity. As the AWJM involves several process variables, selecting the right combinations of process variables to achieve maximum depth of cut (h) is a cumbersome and time-consuming process. Thus, scientific way of choosing process variables is being attempted by many researchers through suitable modeling techniques. This paper focuses on the development of semi-empirical model using Buckingham's π theorem to predict the depth of cut (h) during AWJM of unreinforced and reinforced aluminum metal matrix composites (MMCs) and is compared with regression model and experimental data. Experiments were designed according to Box-Behnken method, and cutting trials were performed on unreinforced aluminum and 5%, 10% and 15% volume fraction of boron carbide (B 4 C)-reinforced 6063 aluminum alloys, MMCs by keeping the process parameters of water pressure (p), mesh size (ms), mass flow rate (m a ) and traverse speed (u) each at three levels. The experimental results of depth of cut (h) at different levels of process parameters were analyzed through ANOVA. Based on the experimental results, the combinations such as #80 mesh size, 340 g/min mass flow rate, 200 MPa water pressure and 60 mm/min traverse speed, resulted in higher depth of cut (h). The experimental, semi-empirical and regression models were compared. The models' predictions are in good agreement with the experimental data under the corresponding conditions.

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Boundary condition for deformation wear mode material removal in abrasive waterjet milling: Theoretical and experimental analyses

Cited by 9 publications

References 34 publications

Effectiveness prediction of abrasive jetting stream of accelerator tank using normalized sparse autoencoder-adaptive neural fuzzy inference system

Effectiveness prediction of abrasive jetting stream of accelerator tank using normalized sparse autoencoder-adaptive neural fuzzy inference system

A user-friendly finite element model for radial mode abrasive waterjet turning

Semi-empirical model for depth of cut in abrasive waterjet machining of metal matrix composites

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