Research Progress in Abrasive Water Jet Processing Technology
Abstract
:1. Introduction
2. Basic Principles and Characteristics of Abrasive Water Jet Technology Processing
2.1. Basic Principles of Abrasive Water Jet Technology Processing
2.2. The Characteristics of Abrasive Water Jet Machining
3. Abrasive Water Jet Processing Material Mechanism
3.1. Processing Removal Mechanism of Brittle Materials
3.2. Processing Removal Mechanism of Plastic Materials
3.3. Processing Removal Mechanism of Composite Attribute Materials
4. Abrasive Water Jet Machining Process Simulation
4.1. Simulation of the Fluid Motion of Abrasive Water Jet
4.2. Process Simulation of Abrasive Water Jets Impacting Brittle Materials
4.3. Process Simulation of Abrasive Water Jets Impacting Difficult-to-Process Materials
5. Influencing Factors in the Processing Removal Process of Abrasive Water Jetting
5.1. Factors Influencing the Maximum Depth of Cut during Abrasive Water Jet Processing
5.2. Factors Influencing Surface Quality during Abrasive Water Jet Processing
5.3. Improvement and Optimization of Abrasive Water Jet Process Parameters
6. Application and Development Directions of Modern Abrasive Water Jet Technology
6.1. Application of Abrasive Water Jets
6.2. New Technology and Development Direction of Abrasive Water Jets
7. Concluding Remarks
- The abrasive water jet processing mechanism should focus on the removal mechanism of composite attribute materials for in-depth research to adapt to the composite attribute materials and new materials processing removal needs.
- In the simulation of the abrasive water jet machining process, attention should be paid to the establishment of an accurate constitutive model, so that the real machining process is correctly reflected in the computer simulation; therefore, the accuracy of model establishment should fully consider the influence of relevant parameters.
- The improvement and optimization of abrasive water jet process parameters should focus on combining production practice environmental conditions on the target-level optimization to adapt to production needs, because the weight or order of the influence of the process parameters on the optimization target level may vary accordingly depending on the processing of different materials, but the correlation with the law of the influence of the process parameters on the target level remains unchanged; therefore, the establishment of the same performance type of materials and the same target level of optimization or improvement model in the corresponding range (especially in the actual production) has strong guidance and versatility.
- The application of modern abrasive water jet technology and the direction of development should focus on precision machining or microfabrication-scale direction change to adapt to growing precision in processing and refinement in demand; at the same time, it should pay attention to the integration with other technologies to form a composite processing technology to meet the improvement of its processing range, processing capacity, machining efficiency, and machining accuracy, which has become one of the main directions for the development of precision machining or composite machining in the future.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Optimization Type | Author | Control Parameters | Optimization Objective | Materials | Method | Optimization Results |
---|---|---|---|---|---|---|
Depth of cut | Perec A [98] | Pressure (p) Transverse speed (Sr) Abrasive flow rate (ma) | D SR | Hardox Steel | Response surface method | The optimum control parameters: p = 385 MPa, Sr = 100 mm/min, ma = 416 g/min, maximum depth of cut and low surface roughness of 20.66 mm and 4.23 µm, respectively. The accuracy of the modeling was verified through experiments. |
Perec A [99] | Garnet rate (G) Pressure (p) Feed rate (F) | D SR KA | Hardox steel | Entropy-VIKOR Approach | The optimum control parameters: G = 350 g/min, p = 400 MPa, F = 100 mm/min, cutting depth of 21.06 mm, roughness of 4.21 µm, kerf angle of 2.8°. The accuracy of the modeling was verified through experiments. | |
Wan L [68] | Jet pressure (p) Mass flow rate (ṁ) Standoff distance (D) Jet angle (α) Traverse speed (u) Feed rate (S) | D MRR SR | Ti6Al4V | ADM-MO-Jaya | The optimum control parameters: p = 223.774 MPa, ṁ = 760 g/min, D = 21.629, α = 30.065°, u = 30.454 mm/min and S = 0.402 mm. The accuracy of the modeling was verified through experiment. | |
Surface quality | Yang X [100] | Cutting speed (A) Hydraulic pressure (B) Standoff distance (C) | Ta SR | CFRP | Response surface method | The optimum control parameters: A = 360 mm/min, B = 400 MPa, C = 2 mm, minimum taper error of 0.058 mm; A = 120 mm/min, B = 400 MPa, C = 2 mm, superior surface roughness of 3.58 μm. The accuracy of the modeling was verified through experiments. |
Venkateshw-ar Reddy P [101] | Standoff distance (A) Transverse speed (B) Sand flow rate (C) | MRR SR KW | Inconel-625 | WASPAS and MOORA | The optimum control parameters: A = 1 mm, B = 146 mm/min. C = 250 g/min, maximum material removal rate, low surface roughness, minimum and kerf widths of 13.56 mm3/min, 5.10 µm, and 0.72 mm, respectively. The accuracy of the modeling was verified through experiments. | |
Kant R [102] | Pressure (p) Abrasive mass flow rate (m) Traverse speed (TS) Standoff distance (SOD) | T SR H | EN31 | Taguchi approach and Analysis of Variance | The optimal optimization results: machining time of 36 s, surface roughness of 1.59 µm, and hardness of 41.7 HRC. The accuracy of the modeling was verified through experiments. | |
Karthik K [103] | Water jet pressure (A) Feed rate (B) Abrasive flow rate (C) | MRR KW | Steel 304 | Grey Relational Analysis and Response | The optimum control parameters: A = 121.76 MPa, B = 80 mm/min, C = 350 g/min, maximum material removal rate and kerf width of 931.19 mm3/min and 1.2044 mm, respectively. The accuracy of the modeling was verified through experiments. | |
Fuse K [104] | Traverse speed (TV) Abrasive mass flow rate (Af) Standoff distance (Sd) | MRR SR KA | Ti6Al4V | Heat transfer search algorithm and RSM | The optimum control parameters: TV = 193 mm/min, Af = 500 g/min, Sd = 1.98 mm., maximum material removal rate, low surface roughness, and minimum kerf taper angles of 0.2133 g/min, 3.50 µm, and 1.98°, respectively. The accuracy of the modeling was verified through experiments. | |
Rajesh M [105] | Water pressure (p) Nozzle distance (Nd) Feed rate (FR) Abrasive flow rate (AFR) | SR | Flax fiber | Analysis of variance | The optimum control parameters: p = 310 MPa, FR = 125 mm/min, Nd = 2 mm, and AFR = 225 g/min. Minimum surface roughness of 3.04 µm obtained. The accuracy of the modeling was verified through experiments. | |
Rana M [106] | Standoff distance (SOD) Abrasive mass flow rate (AMF) Transverse speed (TS) | MRR SR KA | Inconel 625 | TGRA | The optimum control parameters: pressure of 310 MPa, transverse speed of 100 mm/min, SOD = 1 mm, and AMF = 300 g/min. Maximum material removal rate, low surface roughness, and minimum kerf of 25.2 g/min, 2.31 µm, and 0.79°, respectively. The accuracy of the modeling was verified through experiments. | |
Wang Z [107] | Jet pressure (X1) Abrasive concentration (X2) Sprinkler angle (X3) | MRR | W7 diamond FAP | Response surface method | The optimum control parameters: X1 = 3.8 MPa, X2 = 3%, and X3 = 73°. The optimal removal rate obtained was 464.57 nm/min. The accuracy of the modeling was verified through experiments. | |
Srirangarajalu N [108] | Traverse speed (TSP) Abrasive mass flow rate (AAFR) Abrasive aqua jet pressure (AAJP) Gap distance (Gd) | SR KA MRR | Inconel-625 | RSM-CCD | The optimum control parameters: TSP = 75 mm/min, AAFR = 0.55 kg/min, AAJP = 300 MPa, Gd = 2.4 mm. Maximum material removal rate, low surface roughness, and minimum kerf angle of 141.78 g/min, 3.15 µm, and 1.44°, respectively. The accuracy of the modeling was verified through experiments. |
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Wang, H.; Yuan, R.; Zhang, X.; Zai, P.; Deng, J. Research Progress in Abrasive Water Jet Processing Technology. Micromachines 2023, 14, 1526. https://doi.org/10.3390/mi14081526
Wang H, Yuan R, Zhang X, Zai P, Deng J. Research Progress in Abrasive Water Jet Processing Technology. Micromachines. 2023; 14(8):1526. https://doi.org/10.3390/mi14081526
Chicago/Turabian StyleWang, Hongqi, Ruifu Yuan, Xinmin Zhang, Penghui Zai, and Junhao Deng. 2023. "Research Progress in Abrasive Water Jet Processing Technology" Micromachines 14, no. 8: 1526. https://doi.org/10.3390/mi14081526