Abrasive Water Jet Machining Process Research Articles

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.

Abrasive waterjet machining of titanium alloy using an integrated approach of taguchi-based passing vehicle search algorithm

AbstractThe titanium alloy, Ti6Al4V, is a popularly used material in aerospace and medical applications due to its specific attributes, such as excellent strength-to-weight ratio and resistance to corrosion. Ti6Al4V is challenging to machine using conventional methods due to its poor thermal conductivity, which necessitates the use of unconventional machining methods like abrasive water jet machining (AWJM). In this work, AWJM was preferred for machining Ti6Al4V, considering three key process variables: nozzle traverse speed (Tv), abrasive flow rate (Af), and stand-off distance (Sd). The performance of the AWJM process was assessed using three main metrics: Material removal rate (MRR), Surface roughness (SR), and Kerf taper angle (θ), which were crucial for evaluating the effectiveness of the AWJM. Taguchi’s L9 array, a design of experiments method, was used to plan the experiments. The adequacy of the developed models was assessed by analysis of variance (ANOVA). ANOVA results have shown that Tv, Af, and Sd were found to have a significant effect on MRR, SR, θ with contributions of 73.15%, 49.72%, and 78.35% respectively. The Passing Vehicle Search algorithm was adopted to find the global optimal solution. Simultaneous optimization results using the PVS algorithm have shown the optimal MRR, SR, and θ values of 0.17 g/min, 3.28 μm, and 2.45, respectively, at Tv of 237 mm/min, Af of 450 g/min, and Sd of 2.0 mm. The optimization results with Pareto points will help to achieve desired outcomes by selecting appropriate input conditions.

Integrating response surface methodology and machine learning for analyzing the unconventional machining properties of hybrid fiber‐reinforced composites

AbstractThe aim of this investigation was to delve into the impact of abrasive water jet machining (AWJM) process variables on the surface roughness (Ra) and kerf angle (Ka) of hybrid fiber‐reinforced polyester composites. Utilizing both response surface methodology (RSM) and artificial neural network (ANN) prediction models, the study sought to optimize the input parameters for abrasive water jet machining, specifically in the context of paddy straw and PALF‐reinforced polyester hybrid composites. The process parameters targeted for optimization included the abrasive flow rate, traverse rate, and standoff distance during AWJM. The investigation identified an optimal combination of AWJM parameters that effectively meets the practical requirements for machining hybrid fiber‐reinforced polyester composites. According to the RSM, the suggested optimal values for the process parameters are an abrasive flow rate set at 300 g/min, traverse speed at 110 mm/min, and standoff distance at 1 mm. The ANN exhibited robust predictive capabilities, achieving high R2 scores of 0.932 and 0.962 for surface roughness and kerf angle, respectively. To enhance the performance of abrasive water jet machining and minimize surface roughness and kerf angle, the researchers conducted an optimization of the process parameters. Subsequently, confirmation experiments were executed to validate the predictive model and fine‐tune the set of process parameters for practical application.Highlights Investigated AWJM impact on Ra value and kerf angle of hybrid composites. Used RSM and ANN models for parameter optimization in biocomposite. Optimal AWJM parameters: AFR (300 g/min), TS (110 mm/min), and SOD (1 mm). ANN showed strong predictions: R2 scores 0.932 (Ra) and 0.962 (Ka). Confirmation experiments validated the predictive model for applications.

Effect of silicon carbide on kerf convergence and irregularity of the surface during abrasive water jet machining of fiber-metal hybrid composites

The traditional way to machine hybrid composites is hard because they tend to break, have a high retraction, have a high service temperature, and have an uneven surface irregularity. For high-strength fiber/metal composite constructions, alternative machining methods have drawn interest as a solution to these problems. Current research focuses on enhancing the Abrasive Water Jet Machining process by optimizing its variables using a composite material of epoxy reinforced with silicon carbide, stainless steel wire mesh, and Kevlar. The variables assessed are the Nozzle-to-substrate gap (S), the Abrasive discharge molding and different percentages of silicon carbide (SiC) filler (0%, 3%, and 6% by weight), three different types of hybrid laminates (H1, H2, and H3) were produced. The response surface method (RSM) was utilized in this learning, specifically on a central composite design, to calculate and optimize machining variables based on the Kerf convergence ratio (Kt) and Surface irregularity (Ra) as responses. According to the results, the traverse feed velocity, Abrasive discharge proportion, and Nozzle-to-substrate gap are the critical factors in determining Surface irregularity and Kerf convergence width (H1 laminate) for a fiber/metal laminate with 0%, 3% and 6% weight fraction. In the case of a 3% weight fraction H2 laminate, the traverse feed velocity was identified as the primary factor affecting the Kerf convergence ratio. In contrast, traverse feed velocity and Nozzle-to-substrate gap had the most significant influence on Surface irregularity. The findings also indicated that S, followed by Abrasive discharge proportion and traverse feed velocity, are the variables that have the most significant influence when cutting 6 wt% SiC filler particle fiber/metal laminate (H3 laminate). For Surface irregularity, the combination of traverse feed velocity and Nozzle-to-substrate gap had the most significant impact. To validate the optimization results, confirmatory tests was conducted, and the findings were very similar to the experimental values, indicating the accuracy and effectiveness of the optimization process. To better understand the manufacturing processes, a scanning electron microscope was used to examine the morphological features of the machined surfaces, such as delamination, fibre breakage, and fibre pull-out.

Influence of abrasive water jet machining process parameters on accuracy of hole dimensions in glass fiber reinforced polymer composites

Experimental investigation was done in this work to determine the effects of abrasive water jet (AWJ) machining process variables such abrasive grain size, standoff distance (SOD), abrasive flow rate, hydraulic pressure, material thickness, and fiber orientation on dimensional quality features. The Taguchi L27 orthogonal array is used in the experiment design. The Abrasive Water Jet Machining (AWJM) process parameters and response parameters are correlated using Taguchi's ANOVA and SEM pictures to statistically analyze the experimental outcomes. According to the experimental findings, the two most important variables affecting dimensional accuracy are material thickness and standoff distance.

Experimental Investigation on Surface Characteristics of Long Thickness Parts of CFRP during Abrasive Water Jet Machining Process

The use of advanced materials in new fields of applications is usually related with specific properties and great advantages. However, it may become an important technological challenge for the manufacturing and joining of these materials. Carbon Fiber Reinforced Plastic (CFRP) is widely used in aerospace or automotive industry, but currently is starting to be used as structural component in the building industry. In contrast to the thin parts developed for aircraft. the elements for construction are characterized by high thicknesses. This fact increases the abrasive effect of carbon fibre on cutting tools when it is machined by conventional processes. For this reason, non-conventional technologies. as Abrasive Water Jet Machining (AWJM), could be a suitable technology for this purpose. An operation of great interest for these materials is drilling. The industry demands the drilling of a wide range of increasingly large diameters. This, in combination with large thicknesses in CFRP, makes the machining process more difficult. Technologies such as orbital drilling are limited by the range of diameters that can be obtained and the abrasive wear between the material and the cutting-tool. Therefore, the use of abrasive waterjet cutting is proposed as an alternative technology capable of machining diameters in a wide range. One of the main limitations for the use of AWJM on large thickness parts of CFRP comes from the surface finish of the machined surfaces. where high values of roughness can be found. In this research, the effects of water jet cutting parameters have been evaluated for the machining of 17 mm thickness CFRP specimens. By variations on the traverse speed. abrasive mass flow rate and hydraulic pressure. differences in the performance and micro-geometrical characteristics of the machined surfaces were obtained, allowing to identify most significant affecting parameters of the process.

Optimization of machining parameters & studies on characteristics of Monel k400 alloy using abrasive water jet Machining using ANFIS

The Monel k400 alloy is a nickel-copper based alloy with an excellent corrosion resistance that exhibits better weldability and high strength. Also, it exhibits excellent resistance to either rapid flowing brackish water or seawater. Monel k400 alloy (UNS N04400) is widely used in oil, chemical and marine industries. Therefore, machinability of material plays a vital role in adopting material. As a result, in the current work, Monel k400 is used and manufactured utilizing an advanced machining method called an AWJM to examine the impact of process parameters on the machinability of alloy. The purpose of this study was to investigate the effects of abrasive mass flow rate, nozzle traverse speed, and stand-off distance on surface roughness. Adaptive Neuro-Fuzzy Inference System (ANFIS) model is used in order to predict surface roughness upon varying Abrasive Water Jet Machining process parameters for a lesser prediction error. In AWJM process Mean Absolute Percentage Error (MAPE) in between experimental, predicted surface roughness is found to be 3.46% which reflects efficacy of ANFIS model in predicting of surface roughness was evaluated to an extent of credibility. Also, the coefficient of determination R2 is evaluated and found to be 0.943, which indicates a good fit for prediction using the model against measured surface roughness values.

Modeling and multi-objective optimization of abrasive water jet machining process of composite laminates using a hybrid approach based on neural networks and metaheuristic algorithm

Although the abrasive water jet (AWJ) has proven to be a suitable process for machining composite materials, it has some limitations related to dimensional inaccuracy and surface defects. As the performance of the AWJ process mainly depends on the machining parameters, an optimal selection of them is crucial to achieving an improved quality of cut. In this context, the present study reports an experimental investigation to assess the influence of AWJ machining parameters on kerf taper angle (θ) and surface roughness ( Ra) of E glass/Vinylester 411 resin laminates. The experiments are carried out using a full factorial design by varying the water pressure, traverse speed, abrasive flow rate, and standoff distance. A first-ever attempt is made in this paper to optimize the AWJ process using a hybrid approach combining artificial neural networks (ANNs) with a recently proposed metaheuristic algorithm known as multi-objective bonobo optimizer (MOBO). The results show that standoff distance and abrasive flow rate were the most significant control factors in influencing θ and Ra, respectively. The developed ANN models are capable to predict the output responses with high accuracy and the solutions from the Pareto front provide a sufficient performance with a trade-off between θ and Ra. The corresponding levels of the optimal process parameters are 430 g/min for the abrasive flow rate, the range of 140–180 mm/min for the traverse speed, 280 MPa for the pressure, and 1.5 mm for the standoff distance.

Optimization of Abrasive Water Jet Machining Process Parameters on Onyx Composite Followed by Additive Manufacturing

Fiber-reinforced additive manufacturing components have been used in various industrial applications in recent years, including in the production of aerospace, automobile, and biomedical components. Compared to conventional methods, additive manufacturing (AM) methods can be used to obtainin lighter parts with superior mechanical properties with lower setup costs and the ability to design more complex parts. Additionally, the fabrication of onyx composites using the conventional method can result in delamination, which is a significant issue during composite machining. To address these shortcomings, the fabrication of onyx composites via additive manufacturing with the Mark forged 3D-composite printer was considered. Machinability tests were conducted using abrasive water jet machining (AWJM) with various drilling diameters, traverse speeds, and abrasive mass flow rates. These parameters were optimized using Taguchi analysis and then validated using the Genetic algorithm (GA) and the Moth Flame Optimization algorithm (MFO). The surface morphology (Dmax) and the roughness of the drilled holes were determined using a vision measuring machine with 2D software (MITUTOYO v5.0) and a contact-type surface roughness tester. Confirmation testing demonstrated that the predicted values werenearly identical to the experimental standards. During the drilling of an onyx polymer composite, regression models, genetic algorithms and the Moth-Flame Optimization algorithm were used to estimate the response surface of delamination damage and surface roughness.

Two adaptive nonmonotone trust-region algorithms for solving multiobjective optimization problems

ABSTRACT This paper attempts to propose two adaptive nonmonotone trust-region algorithms in the multiobjective optimization (MO) case. The first proposed trust-region algorithm uses a modified maximum nonmonotone technique, which takes a convex combination of the maximum value of some preceding successful iterates and the function value in the current iterate. The second one employs the average nonmonotone technique, which takes a weighted average of the successive function values. Under some suitable assumptions, the convergence of the sequences generated by the trust-region algorithms that use the aforementioned nonmonotone techniques to a critical point is shown. Using some well-known test problems, we compare our proposed adaptive nonmonotone MO algorithms with some other MO trust-region algorithms. To conduct a thorough comparison in this regard, some performance criteria are used. These numerical results confirm a significant advantage of applying the proposed adaptive nonmonotone trust-region algorithms in solving MO problems. Finally, the proposed algorithms are implemented to optimize one of the optimization problems of the abrasive water-jet machining process.

Multi-objective parametric optimization of AWJM process using Taguchi-based GRA and DEAR methodology

Abrasive waterjet machining (AWJM) is a well-known non-traditional cutting process that is widely used to shape materials such as metals, ceramics, alloys, and composites. AWJM was utilized to manufacture titanium alloy in this study, with influencing parameters such as standoff distance (SOD), abrasive flow rate (AFR), and traverse speed (TS) being varied. Material removal rate (MRR) and surface roughness (SR) have been used to quantify the impact of these variables on machining quality. The effects of process factors on output responses were investigated using a Taguchi study approach with an L9 factorial design. SOD of 1 mm, AFR of 300 g/min, and TS of 519 m/s yield the best combination for greater MRR. A combination of SOD of 3 mm, AFR of 400 g/min, and TS of 519 m/s yields the best SR results. Simultaneous optimization of both outputs was also carried out using Taguchi-based Grey relational analysis (GRA) and data envelopment analysis-based ranking (DEAR) methodologies. PCA is utilized for considering the weights of responses and the correlation among them. Both methods obtained similar results, but there is a difference in the middle rankings in the nine experimental trials. Validation trials were run on the acquired combination of parameters, and the error was found to be within acceptable bounds.

Influence of AWJM parameters on surface quality of BSHC

ABSTRACT Natural fiber hybrid composites are replacing synthetic fiber composites, which improves environmental cleanliness. In this investigation, a compression molding process was used to produce a Banana/Sisal Hybrid Composite (BSHC) and the influence of Abrasive Water Jet Machining (AWJM) process parameters on surface quality was studied. The AWJM experiments were conducted using the design of experiment techniques, with parameters such as water pressure (P), nozzle transfer speed (d), and standoff distance (s). During the AWJM process, P was identified as the most significant parameter influencing surface quality. The optimum machining parameters (P3, d1, s1) provide better surface quality which produces a surface roughness of 10.1 μm. The experimental surface roughness values have a better correlation with the predicted surface roughness which shows the efficiency of the prediction model.

An Experimental Investigation on Machining of Hardened AISI 440C Stainless Steel Using Abrasive Water Jet Machining Process

An Experimental Investigation on Machining of Hardened AISI 440C Stainless Steel Using Abrasive Water Jet Machining Process

Abrasive water jet machining of pineapple fibre epoxy composite at high transverse speed

Recently, research has been undergoing in the area of natural fibre-reinforced composite as an alternative for metals and plastics in automobile applications. It is mainly because of its strength and lighter weight. Literature shows that tribological studies are made in large numbers to evaluate these composite's strength, corrosion and wear behaviour. The machinability of these natural fibres is to be studied to understand the further scope of these materials in their respective application. In this research machinability of pineapple fibre is studied under the abrasive water jet machining process at a higher transverse speed. The material removal rate is chosen as a tool for evaluating the machinability of these composites. The Taguchi-based approach is used for planning and optimising the experiments. The optimal parameter for maximising the material removal rate is done using the signal-to-noise ratio analysis. Also, an Artificial neural network algorithm is developed to predict the responses. The results of the ANN model show that the model predicts the response with an accuracy of 94.9%. This ANN prediction model of machining study could be helpful when these composites are imposed in automobile and aerospace applications.

A Short Review on AWJM of Natural Fibre Reinforced Composite Materials

A composite is a solid material created by combining two or more different substances, each with its own set of qualities, to create a new substance with properties that outperform the original components for a specific use. Currently composite materials are widely applied across various fields due to its superior properties. The fibre reinforced composites being light in weight, and high strength are replacing metals in many applications. Nowadays, more focus is paid in developing sustainable and environment friendly materials without compromise in the quality. This reason led to the development of Natural Fibre Reinforced Composite (NFRC) materials, which is gaining more attention towards them. Though many NFRCs were developed, machining is a very big challenge. Compared to the traditional machining processes, the non-traditional machining process is proved to be recommendable for machining NFRC materials. In this article, the numerous research endeavors carried out in machining of NFRCs using Abrasive Water Jet Machining (AWJM) process is reviewed. This article describes the different parameters considered in AWJM process and their influence over the machining characteristics of NFRCs. Based on the different research carried out, the use of nanofillers in the right composition is expected to increase the performance of the AWJM process over NFRCs.

Research Trends in Abrasive Water Jet Machining Using Numerical Simulation Tools: A Bibliometric Review

Background: Abrasive water jet (AWJ) machining process is one of the most sought-after machining technologies for the processing of advanced hard-to-cut materials. It involves parameters, such as abrasive size, shape, density, pressure, standoff distance (SOD), abrasive concentration, feed rate, etc., which govern the quality of machining. It is very crucial to understand the influence of these parameters on the quality attributes. Objective: Due to the complex nature of the process that involves complex process parameters, the accurate prediction of response by experimental methods is difficult. In this scenario, numerical methods are helpful in understanding the mechanisms of material removal. Thus, a comprehensive summary of these research trends is needed. Methods: In this article, a bibliometric analysis is carried out on scientific publications pertaining to AWJ machining (AWJM) using numerical simulation tools. Citation and bibliographic coupling analyses have been carried out to identify the current research trends, the important journals, authors, institutions, and countries engaged in research on AWJM using numerical simulation. Results: The analysis revealed Shandong University to have the maximum number of affiliated researchers working in this area. The International Journal of Machine Tools and Manufacture was the leading journal based on CiteScore, SJR rank and SNIP ranks, and the largest volume of articles was published by this journal. The critical topics of research and international collaborative opportunities were identified through the analysis of keywords, Sankey and network diagrams. Conclusion: The present article would be valuable for academia and industry, aiding them in updating their knowledge on the latest developments in AWJM.

Artificial immune system approach for optimizing abrasive water jet machining process on super alloys

Artificial immune system approach for optimizing abrasive water jet machining process on super alloys

The effect of stacking sequence and fiber orientation on tensile and flexural strength of fiber reinforced composite fabricated by VARTM process

In this study, Carbon, Glass, and Aramid fiber reinforced composite and their hybridized forms were fabricated using five different stacking sequences of the fabrics. Using the Vacuum Assisted Resin Transfer Molding (VARTM) procedure, epoxy resin was injected into these fabrics and allowed to cure at room temperature. From these five stacking sequences, a standard specimen with four different orientations viz. 0/90°, 15/75°, 30/60°, 45/-45° orientations were obtained using the Abrasive Water Jet Machining(AWJM) Process. The influence of stacking order and fiber orientation on tensile and flexural properties of composite was investigated. From the result of tensile testing, the highest and lowest tensile strength values were observed for neat carbon fiber reinforced composite at 0/90° orientation and at 45/-45° orientation respectively. The highest flexural strength was achieved in a hybrid combination of two layers of carbon, glass and aramid fabric for 0/90° whereas the lowest flexural strength was found in glass reinforced composite for the 45/-45° orientation.

Investigation and optimization of drilled surfaces machined by abrasive water jet machining in Ti-6al-4V alloy

ABSTRACT Titanium alloys are the most appealing material for aerospace, biomedical, and chemical applications due to their high specific strength and fracture resistance. Drilling of titanium alloy is required in the bolt/rivet assembly of aerospace components and bone plate, fracture bone for biomedical applications. The main objective of this study is to explore the drill rate, surface roughness and drill time during abrasive water jet machining (AWJM) of Ti6Al4 V alloy. Water jet pressure (WJP), standoff distance (SOD), abrasive flow rate (AFR) and feed rate (FR) are input variables, and the output responses are surface roughness (Ra), drill rate (DR) and drill time (DT). Experiments were carried out using the Taguchi L27 orthogonal array. Analysis of variance (ANOVA) was used to investigate the most influential factor for DR, DT, and Ra. The most influential factor in reducing DT and enhancing DR is FR. WJP, SOD, and AFR are the most important factors influencing Ra. With increasing WJP, SOD, and FR selected cutting conditions, Ra was observed to increase. Increased FR results in higher DR for AWJM of Ti6Al4 V alloy. The surface morphology of an optimized AWJM sample was investigated. An EDS (energy-dispersive spectroscopy) result reveals the presence of carbon and oxygen on the machined surface. Grey relational grade is used to find the best AWJM process parameters for machining the Ti6Al4 V alloy. The AWJ machined surface was investigated with SEM to assess the quality of the optimized setting through GRA.

Optimal Cutting Conditions of Abrasive Waterjet Cutting for Ti-6Al-2Sn-2Mo Alpha-Beta Alloy Using EDAS and DFA Methods

Abrasive waterjet machining (AWJM), a known metal cutting process in manufacturing, is likely to be improved with the selection and use of the most influential parameters in machining decision-making. This work illustrates the development of two multicriteria indicators to optimize parameters for the abrasive waterjet machining process, providing optimization information for the surface morphology problem. The evaluation based on the distance from average solution (EDAS) method was used as the first indicator while the desirability function analysis (DFA) method reflects the second indicator. The results demonstrate a huge promise of both indicators, EDAS and DFA, to develop procedures for optimizing the parameters of Ti-6Al-2Sn-4Zr-2Mo alpha-beta alloy through the abrasive waterjet machining process. For the EDAS method, experimental trial 7 provided the best results with the water jet pressure of 220 bar, traverse speed of 40mm/min, and standoff distance of 1mm. The corresponding material removal rate is 151.667mm3/min while the roughness average is 2.76mm. The DFA method also provided the same results as those of the EDAS method. The present study is evidence of optimization of the parameters of Ti-6Al-2Sn-4Zr-2Mo alpha-beta alloy using the AWJM process. This warrants an intervention to enhance productivity and the economic gains of the company.