Abrasive Water Jet Research Articles

Advanced Waterjet Technology for Machining Beveled Structures of High-Strength and Thick Material

The bevel cutting of large-thickness plates is a key process in modern industries. However, traditional processing method such as air-arc gouging bevel cutting or laser bevel cutting may cause serious deformation and rough surface quality due to the defects of the thermal cutting method. In order to improve the quality and efficiency of bevel processing, the abrasive waterjet cutting method is used in this research to overcome the challenge for bevel machining of high-strength DH40 steel plates with a large thickness. For different kinds of beveled structures, a 3D camera is used to measure the reference points defined on the workpiece and the SVD registration algorithm is adopted to transform the theoretical coordinate system to the actual coordinate system. Furthermore, the distance between the nozzle and the workpiece surface is also measured and compensated for to ensure the consistency of the bevel width. Finally, experiments are carried out for different kinds of bevels to verify the feasibility of the proposed method for high precision processing for beveled structures. The developed method has been effectively applied in the actual shipbuilding industry.

Modeling the shape profile of the machining side trimmed by abrasive water jet

Modeling the shape profile of the machining side trimmed by abrasive water jet

Monitoring Equipment Malfunctions in Composite Material Machining: Acoustic Emission-Based Approach for Abrasive Waterjet Cutting

This paper introduces an Acoustic Emission (AE)-based monitoring method designed for supervising the Abrasive Waterjet Cutting (AWJC) process, with a specific focus on the precision cutting of Carbon Fiber-Reinforced Polymer (CFRP). In industries dealing with complex CFRP components, like the aerospace, automotive, or medical sectors, preventing cutting system malfunctions is very important. This proposed monitoring method addresses issues such as reductions or interruptions in the abrasive flow rate, the clogging of the cutting head with abrasive particles, the wear of cutting system components, and drops in the water pressure. Mathematical regression models were developed to predict the root mean square of the AE signal. The signal characteristics are determined, considering key cutting parameters like the water pressure, abrasive mass flow rate, feed rate, and material thickness. Monitoring is conducted at both the cutting head and on the CFRP workpiece. The efficacy of the proposed monitoring method was validated through experimental tests, confirming its utility in maintaining precision and operational integrity in AWJC processes applied to CFRP materials. Integrating the proposed monitoring technique within the framework of digitalization and Industry 4.0/5.0 establishes the basis for advanced technologies such as Sensor Integration, Data Analytics and AI, Digital Twin Technology, Cloud and Edge Computing, MES and ERP Integration, and Human-Machine Interface. This integration enhances operational efficiency, quality control, and predictive maintenance in the AWJC process.

Study on depth weakening of reinforced concrete piles cutting by abrasive waterjets under submerged conditions

In order to improve the efficiency and safety of shield machine cutting reinforced concrete piles, abrasive waterjets (AWJs) assisted mechanical combined cutting technology is considered a feasible solution. However, the water-rich flooding environment will cause the cutting performance of AWJs to weaken, which brings great challenges to the application of this technology. In order to analyze the influence of submerged conditions on the cutting performance of AWJs, the cross-sectional morphology of reinforced concrete piles cut by AWJs is explored using indoor experiments, and the weakening law of pile foundation cutting depth is revealed. The results show that the submerged environment greatly weakens the cutting ability, and the average cutting depth is reduced by about 20.0%. Rebar materials are more difficult to remove due to their high strength and strong plastic toughness, which is the root reason of the difficulty in cutting off piles. As the cutting times increases, the cutting depth discount factor initially decreases rapidly before stabilizing. At 380 MPa, the cutting depth discount factor remains stable at 0.49, while at 280 MPa, it stabilizes at 0.62. The findings presented in this paper can provide guidance and suggestions on waterjet parameters for AWJs assisted shield machine pile breaking. HIGHLIGHTS The cross-sectional change rules of reinforced concrete pile foundation cut by abrasive waterjets is clarified. The depth weakening effect of pile foundations cut by abrasive waterjets under submerged conditions is revealed. The cutting depth discount factor for rebars is established.

Machinability study on abrasive waterjet drilling of YSZ-coated inconel 718 superalloy: optimization and modelling by machine learning

The purpose of this research is to ascertain the optimal abrasive waterjet drilling parameters for making holes on Inconel 718 superalloy coated with yttrium-stabilized zirconia, namely waterjet pressure (JP), standoff distance (SD), abrasive flow rate (FR), and angle of impact (AI). The study explores the predictive modeling of the entry diameter (DN) and exit diameter (DX) of the drilled holes using an amalgamation of experimental analysis, response optimization and machine learning approaches. Eight different ML techniques are used to predict DN and DX. Better values of DN = 1.31352 mm and DX = 1.00515 mm are obtained through Random Forest for the setting of JP = 175 MPa, FR = 250 g min−1, SD = 1.45455 mm, and AI = 0.909091 deg. Further, the tuning of hyperparameters of Random Forest algorithm is performed to study the improvement in measuring DN and DX. The least value of total absolute error = 0.468 is observed while finding the DN and DX using Random Forest algorithm than the traditional response optimization method with reference to the confirmation test results. The work provides important insights for enhancing the machinability of YSZ-coated Inconel 718 superalloy utilizing the AWJ drilling process by bridging the gap between manufacturing research, machine learning, and real-world applications.

Sandstone breaking performances and mechanisms of swirling abrasive waterjet during radial jet drilling process

Radial jet drilling (RJD) is one of the emerging hydrocarbon drilling technologies. And, the swirling abrasive waterjet (SAWJ) is expected to drill larger diameter laterals during RJD. Here, the performances and mechanisms of SAWJ breaking sandstone were studied by laboratory experiments. Results showed that the SAWJ could drill a smooth (surface roughness was 0.043 mm) & large (diameter was in 52.0–73.0 mm) circular hole on sandstone. The hole depth/volume increased as the jetting pressure, abrasive mass concentration and exposure time increased. Conversely, they decreased as the standoff distance increased. The optimal parameter combination under our experimental conditions was 30 MPa, 0 mm, 12% and 1 min. The SAWJ sandstone breaking mechanism were the erosion of cements, the integrally peeled off and broken of crystal grain. Failure mode of sandstone was mainly the tensile fracture. The key findings will provide guidance for the application of SAWJ in RJD technology.

Developments in Abrasive Water Jet Machining of Nonmetals

Abrasive water jet machining (AWJM) is known for precise cutting of metals and nonmetals. In the past few years, this machining technology has undergone some important developments which possibly enhanced its capability and extended its applications. This article presents such important developments and related attempts on AWJM made by researchers in the recent past. The scope covers AWJM of important nonmetallic materials like rock, glass, rubber, composites and ceramics etc. The important developments have been in the form of utilizing abrasives like sugar particles, glass beads, ice particles, bone powder; water-less abrasive jet machining; process optimization; and acoustic emission monitoring etc. that enhanced the capability of the AWJM process towards cost reduction, quality enhancement, and sustainability. The article intends to amass such important information related to the recent developments in AWJM for nonmetal cutting and aims to facilitate scholars and researchers with this new knowledge.

Cutting surface roughness prediction model for cutting carbon steel using premixed abrasive water jet

Cutting surface roughness prediction model for cutting carbon steel using premixed abrasive water jet

Enhancing machining accuracy of banana fiber-reinforced composites with ensemble machine learning

Through innovative predictive modeling, this study advances Abrasive Water Jet Machining (AWJM) for banana fiber-reinforced biocomposites. Utilizing Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) models, hyperparameters are meticulously tuned for predicting Surface Roughness (Ra), Material Removal Rate (MRR), and Kerf Angle (Ka). Optimal configurations are identified, such as 3–6-6–4-1, 3–5-5–4-1, and 3–5–4–4-1 for ANN models, and 250, 100, and 50 units for LSTM models. Beyond individual models, the study explores stacking ensemble models, merging ANN and LSTM strengths with a Linear Regression final estimator, showcasing robust performance validated with high R-squared values. Regression models from Design Expert 13 contribute to understanding process parameter-outcome relationships. Optimized input parameters offer insights into minimizing surface roughness and kerf angle while maximizing MRR. This holistic approach, integrating advanced machine learning and ensemble learning, enhances predictive accuracy for banana-reinforced biocomposites, providing a versatile framework for diverse materials processing applications.

Residual Stress Evaluation in L-PBF-Produced SS 316L Specimens.

The identification of residual stresses (RS) in components made by selective laser melting (SLM) is necessary for subsequent technological optimization. The presented research is devoted to evaluating the influence of the combination of laser power (P), scanning velocity (v) and the rarely considered number of layers (nL) on surface residual stresses in SLM stainless steel SS 316L. Experimental parameters were set based on the Design of Experiment (DoE) method, with follow-up X-ray diffraction (XRD) measurements and data processing using analysis of variance (ANOVA) and regression analysis. The obtained data are a valuable stepping-stone for the subsequent design of research focused on the application of sustainable eco-friendly Abrasive Water Jet (AWJ) peening for RS modification in the evaluated material.

Evaluation of the Feasibility of the Prediction of the Surface Morphologiesof AWJ-Milled Pockets by Statistical Methods Based on Multiple Roughness Indicators

Improvement of the surface quality of machined parts is essential in order to avoid excessive and costly post-processing. Although non-conventional processes can efficiently carry out the machining of difficult-to-cut materials with high productivity, they may also, for various reasons, be related to increased surface roughness. In order to optimize the surface quality of generated surfaces in a reliable way, surface profiles obtained during these processes must be adequately modeled. However, given that most studies have focused on Ra or Rz indicators or are based on the assumption of a normal distribution for the profile heights, relevant models cannot accurately represent the surface characteristics that exist in a real machined surface with a high degree of accuracy. Thus, in the present study, a new modeling approach based on the use of a statistical probability distribution for the surface profile height is proposed. After six different distributions were evaluated on the basis of a three-stage procedure involving different roughness indicators pertaining to the abrasive waterjet (AWJ) milling of pockets, it was found that, although it is not possible to model the nominal values of every roughness parameter simultaneously, in several cases, it is possible to approximate the values of critical indicators such as Ra, Rz, Rsk, Rku and Rp/Rv ratio by Weibull distribution with a sufficient degree of accuracy.

Surface Roughness of Aluminum Alloy 7024 Predicted by Linear Regression and Neural Network Model in Abrasive Water Jet Machining

Surface Roughness of Aluminum Alloy 7024 Predicted by Linear Regression and Neural Network Model in Abrasive Water Jet Machining

Mechanical investigation for enhancing jute fibre vinyl ester composite performance through garnet waste utilization

This study explores the potential utilization of garnet waste, a byproduct of the Abrasive Water Jet Machining process, as a promising filler material in composite manufacturing. The disposal of garnet waste presents significant environmental and economic challenges, underscoring the need for sustainable waste management strategies. To tackle these challenges, this research investigates the incorporation of garnet waste into composites to create value-added materials. Consequently, the study focused on developing garnet-filled jute fibre-reinforced vinyl ester composites using the hand layup method. Various mechanical properties including flexural strength, tensile strength, hardness, and impact strength were assessed. The findings demonstrate that the inclusion of garnet filler enhances the mechanical strength of the composites. Particularly, composites containing 10 wt% garnet exhibit notable improvements in tensile, flexural, impact, and hardness strengths, with increases of 40%, 124%, 26%, and 15%, respectively. This research has the potential to advance the development of advanced materials across diverse industries, marking a significant stride towards sustainable and value-driven composite manufacturing practices.

Development of 6-way CFD-DEM-FEM momentum coupling interface using partitioned coupling approach

Fluid-particle-structure interactions (FPSI) govern a wide range of natural and engineering phenomena, from landslides to erosion in abrasive water jet cutting nozzles. Despite the importance of studying FPSI, existing numerical frameworks often simplify or neglect certain physics, limiting their applicability. This work introduces a novel 6-way CFD-DEM-FEM momentum coupling for FPSI using a partitioned coupling approach, providing a flexible and adaptable solution.Our prototype uses the preCICE coupling library to couple three numerical solvers: OpenFOAM for fluid dynamics, eXtended Discrete Element Method (XDEM) for particle motion, and CalculiX for structural mechanics. The coupling approach extends existing adapters and introduces a novel XDEM preCICE adapter, allowing data exchange over surface and volumetric meshes.Numerical experiments successfully demonstrate the 6-way coupling, showcasing fluid-structure interactions and particle dynamics. The versatility of the partitioned coupling approach is highlighted, allowing the interchangeability of different single-physics solvers and facilitating the study of complex FPSI phenomena.This article offers a thorough description of the methodology, coupling strategies, and detailed results, offering insights into the advantages and disadvantages of the proposed approach. This work lays the groundwork for a scalable and customizable FPSI simulation framework with a wide range of applications.

Experimental study on rock-breaking characteristics of advanced premixed abrasive water jet

ABSTRACT To investigate the rock-breaking characteristics of the advanced premixed abrasive water jet, experiments of abrasive water jet eroding granite were carried out. The micro-failure morphology of erosion pits was observed and analyzed by CT (Computerized Tomography) and SEM (Scanning Electron Microscope). The results show that the difference in erosion effect caused by different rock breaking pressures is due to the difference in impact kinetic energy. The higher the jet pressure, the higher the impact kinetic energy of abrasive particles, and the erosion effect is enhanced. The difference in erosion effect caused by abrasive types is that the harder abrasives produce greater cutting force on the rock and consume less fracture surface energy. The CT scanning results show that the conical crack at the bottom is caused by the stamping action of abrasive particles and expands rapidly under the action of a water wedge, the action of reflective particles makes the erosion holes appear spindle-shaped. The results of SEM show that the sidewalls of the erosion pit are dominated by the cutting and fatigue-crushing effects of reflected abrasives. The bottom of the erosion pit is mainly driven by the punching action of the incident abrasive.

Abrasive waterjet cutting of r‐GO infused magnesium fiber metal laminates: Experimental investigations and optimization through gorilla troops algorithm

AbstractLaminated metal‐composite structures, known as fiber metal laminates (FMLs), are modern lightweight materials extensively utilized in diverse industries such as aerospace and automotive manufacturing. These materials offer enhanced impact and fatigue resistance, making them invaluable for various applications. However, machining FMLs poses a challenge due to the occurrence of delamination and heterogenous in nature during conventional methods. Therefore, this study aims to investigate the quality characteristics such as kerf width, surface roughness and kerf taper of Magnesium based FMLs processed with an unconventional machining process namely abrasive water jet cutting (AWJC). These FMLs comprise alternately stacked kevlar/carbon fibers adhesively bonded with epoxy resin matrix filled with varying wt% of reduced graphene oxide (r‐GO), combined with AZ31B alloy sheet as the skin material. AWJC experiments were performed by varying the process parameters including waterjet pressure (275–325 MPa), stand‐off distance (2.5–3.5 mm), and cutting speed (600–800 mm/min) based on the box–behnken experimental design. The findings from the statistical analysis underscore the significant influences of stand‐off distance and waterjet pressure on kerf characteristics, while the inclusion of r‐GO is observed to notably affect the surface quality. In pursuit of enhancing cut quality, a multi‐response optimization was conducted employing the Gorilla Troops Optimization (GTO) algorithm. Comparative analysis with established metaheuristics like the gray wolf algorithm, dragonfly algorithm, and harmony search algorithm reveals GTO's superior performance across various metrics, including rapid convergence, diversity, and spacing values. Further, the cutting‐induced damages such as matrix plowing, fiber‐metal debonding and composite delamination were observed through microstructure analysis.Highlights Magnesium (AZ31B) fiber metal laminate comprising with various wt% of reduced graphene oxide was fabricated. Abrasive waterjet cutting of FMLs were performed based on box–behnken design experimental approach. Statistical analysis and influence of process parameters on the kerf width, surface roughness and kerf taper were investigated. Multi‐response optimization was carried out using metaheuristic‐based gorilla troops algorithm.

Validation of pneumatic crack detection in hole expansion test

According to the standard ISO 16630, the hole expansion test (HET) has to be stopped at the instant when the first through-thickness crack appears at the edge of the hole. The detection of the crack is challenging, since the maximum opening width should not exceed 0.1 mm at the inner diameter according to the newest version of the standard from 2017. A pneumatic crack detection has been introduced in which a pressure drop is used to identify the through-thickness crack. In the present paper, this method for stopping the hole expansion test is validated. It was tested for steels with different edge crack sensitivities and compared to stopping with force drop method. Therefore, the curves of pressure and force have been analysed. The influence of hole insertion method (shear cutting or abrasive water jet cutting) as well as specific hole expansion parameters (punch speed, air pressure) have been investigated. Besides the expanded diameters to evaluate the edge crack sensitivity, the opening width of the edge cracks were analysed to determine the suitability of the crack detection methods for different hole expansion ratios (HER).

Effective optimization strategies for abrasive water jet machining of glass-carbon fiber reinforced composites: A comparative study of evolutionary optimization techniques

This study applied evolutionary optimization techniques and neural networks to predict optimum machining parameters of Abrasive Water Jet Machining (AWJM) for machining Glass-Carbon Fiber Reinforced Composite (GCFRC) materials. Several researchers have employed different optimization techniques; however, evolving computational capabilities further open avenues to optimize such parameters. Five evolutionary techniques, namely Artificial Neural Network (ANN), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Nonlinear Least Square Error (LSE), were applied to minimize surface roughness (Ra) and maximize kerf width (Kw) and material removal rate (MRR). The output performance parameters (Ra, Kw, and MRR) were formulated as a linear mathematical function of machining parameters: tool feed rate (TFR), cutting speed rate (CSR), and stand-off distance (SOD). Though this study solves linear optimization problems, the proposed technique will be a strong tool for solving complex associations of machining and performance parameters in the future. A dataset of machining parameters and subsequent performance parameters was adopted from the available literature. The results indicated that the LSE method outperformed other techniques, yielding the lowest Root Mean Square Error (RMSE) in predicting Ra, Kw, and MRR, thus ensuring high machining accuracy. LSE technique reported relatively least RMSE values of 0.37 µm, 0.149Mm, and 237.23 mm3/min for Ra, Kw, and MRR, respectively. SA and PSO displayed identical and competitive RMSE values, slightly higher than LSE (up to 20 % higher). ANN and GA techniques were not effective relative to other considered techniques. LSE, SA, and PSO provide superior performance in optimizing AWJM parameters. The significant contribution of this research is the proposed optimization technique, offering a clear direction for solving complex associations between the performances and machining parameters of AWJM. This work also provides a foundation for future research to optimize such associations for other machining setups.

Experimental Analysis of Kerf Characteristics of Carbon Fiber-Reinforced Polymer with Abrasive Water Jet Machining

<div>This research looks into how abrasive water jet machining (AWJM) can be used on carbon fiber-reinforced polymer (CFRP) materials, specifically how the kerf characteristics change with respect to change in process parameters. We carefully looked into four important process parameters: stand-off distance (SOD), water pressure (WP), traverse rate (TR), and abrasive mass flow rate (AMFR). The results showed that as SOD goes up, the kerf taper angle goes up because of jet dispersion, but as WP goes up, the angle goes down because jet kinetic energy goes up. The TR was directly related to the kerf taper angle, but it made the process less stable. The kerf drop angle was not greatly changed by AMFR. When it came to kerf top width, SOD made it wider, WP made it narrower, TR made it narrower, and AMFR made it a little wider. When the settings (SOD: 1 mm, WP: 210 MPa, TR: 150 mm/min, AMFR: 200 g/min) were optimized, the kerf taper angle and kerf top width were lowered. This improved the accuracy of the measurements and cut down on material waste in CFRP composite machining. These results make it clear how important parameter selection is in precision cutting.</div>

Effect of parameter optimization on the flow characteristics of venturi-self-excited oscillation mixer based on response surface model and multi-island genetic algorithm

The present study focuses on the development of a novel venturi-based self-excited oscillation mixer that effectively utilizes the venturi effect to facilitate efficient abrasive intake while simultaneously ensuring effective prevention of backflow through the utilization of the systolic section within the venturi tube. It not only ensures uniform mixing of water and abrasive but also transforms the continuous jet into a pulsed one, thereby significantly enhancing exit velocity. The orthogonal experimental design method and single factor experiment method were employed to investigate the effects of inlet water pressure, water nozzle diameter, abrasive inlet angle, aspect ratio of the self-excited oscillation mixer, and abrasive pipe inlet diameter on the inlet pressure of the abrasive pipe and the velocity of the jet exit in the new mixing device. Approximate response surface models for these parameters were constructed using lsight optimization software, combining the results of orthogonal experimental simulation. By employing a multi-island genetic algorithm, we have globally optimized this innovative mixing device to determine its optimal performance parameters. Subsequently, comparative experiments were conducted to validate the performance of different mixing devices in descaling applications. Through experimental verification, it was found that the venturi-self-excited oscillation mixer exhibits excellent rust removal capabilities in steel plate tests compared to traditional self-excited oscillation mixers. These findings provide valuable guidance for the subsequent design and enhancement of abrasive water jet mixers.