Non-traditional Machining Process Research Articles

Experimental investigation to assess the surface integrity in WEDM of Al-based hybrid composite material

Aluminum-based hybrid composites are used in various engineering applications due to their unique properties, such as high strength, good wear resistance, excellent corrosion resistance, and low density. Aluminum metal matrix (AMMC) composites reinforced with ceramic particles can be more difficult to machine than traditional aluminum alloys because of their increased hardness and abrasiveness. Wire electric discharge machining is a non-traditional machining process that uses a thin metal wire, typically made of molybdenum, brass or tungsten to cut through a workpiece using electrical discharge sparks. This process is useful for precision machining of complex shapes and profiles in hard or difficult to machine materials. WEDM is a commonly used process for cutting Al-SiC-ZrO2 HAMMCs due to their hardness and difficulty in machining using conventional techniques. The present study examines the relationship between machining parameters like pulse on time (TON), pulse off time (TOFF), peak current (IP) and wire feed (WF) and material removal rate (MRR) and surface roughness ( Ra) for Al-SiC-ZrO2 in WEDM. The Box-Beckham Design was used to plan the experiments, and response surface method was employed to develop the models. The pulse duration, TOFF, IP, and quadratic terms of [Formula: see text] and [Formula: see text] are the most influential factors of the MRR. The TON, IP and interaction term (TON × IP), and [Formula: see text] were the most significant factors for Ra. Multi-response optimization of process parameters was obtained using the desirability function approach. Furthermore, the machined samples were analyzed for surface integrity using SEM techniques. The prediction from this model validated the confirmation results.

A study on the effects of objective weighting methods on TOPSIS-based parametric optimization of non-traditional machining processes

Developing advanced engineering materials with enhanced mechanical and metallurgical properties has caused non-traditional machining (NTM) processes to emerge as cost-effective and energy-efficient options. Improper settings of the input parameters of those processes may often lead to inferior quality and accuracy of the finished products. Multi-criteria decision-making (MCDM) methods appear as potent tools for multi-objective optimization of those processes. Determining criteria weights is a crucial step in any MCDM problem; subjective approaches for evaluating criteria weights may result in inconsistent judgments. In this paper, the effects of ten different objective weighting methods, i.e., equal weight, entropy weight, Criteria Importance Through Inter-criteria Correlation (CRITIC), Method based on Removal Effects of Criteria (MEREC), Logarithmic Percentage Change-driven Objective Weighting (LOPCOW), Symmetry Point of Criterion (SPC), Criteria Impact Loss (CILOS), Integrated Determination of Objective Criteria Weights (IDOCRIW), Principal Component Analysis (PCA) and Weighted Principal Component Analysis (WPCA) are studied on Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)-based optimization of two NTM processes. In the first example, the optimal parametric combination computed using equal weight, entropy, MEREC, LOPCOW, PCA, and WPCA is noticed to be the same. In contrast, a different set of optimal parameters is derived by applying CRITIC, SPC, CILOS, and IDOCRIW. In the second example, seven out of ten approaches obtain a unique set of parametric combinations, while CILOS, IDOCRIW, and PCA lead to a different set of intermixes. It can be concluded that the ranks of the experiment trials for both the examples are almost similar irrespective of the objective criteria weighting technique employed.

MICRO-EDD OF INCONEL 800 USING ELECTROMAGNETIC FIELD

Micro-electro-discharge drilling ([Formula: see text]EDD) is a type of non-traditional machining process used for drilling micro-holes of desired dimensions with a high aspect ratio. But, there are no such research works that could have explained the desired accurate circular shape of micro-holes. The need for a more advanced hybrid machining process to improve the overall efficiency in terms of mainly desired circular shape and radial overcut is evolved. In this research work, an electromagnetic field force-assisted micro-EDM process has been carried out on Inconel 800 with a copper tool of 450[Formula: see text][Formula: see text]m. Experimental results showed that measured metal removal rate and tool wear rate decreased for ascending values of magnetic flux density, peak current and gap voltage, whereas circularity increases linearly with an increase in magnetic flux density and also the effects of magnetic field on circularity of micro-holes on Inconel 800 are more predominant than other parameters.

An In-Depth Exploration of Unconventional Machining Techniques for INCONEL® Alloys.

Build-up-edge (BUE), high-temperature machining and tool wear (TW) are some of the problems associated with difficult-to-machine materials for high-temperature applications, contributing significantly to high-cost manufacturing and poor tool life (TL) management. A detailed review of non-traditional machining processes that ease the machinability of INCONEL®, decrease manufacturing costs and suppress assembly complications is thus of paramount significance. Progress taken within the field of INCONEL® non-conventional processes from 2016 to 2023, the most recent solutions found in the industry, and the prospects from researchers have been analysed and presented. In ensuing research, it was quickly noticeable that some techniques are yet to be intensely exploited. Non-conventional INCONEL® machining processes have characteristics that can effectively increase the mechanical properties of the produced components without tool-workpiece contact, posing significant advantages over traditional manufacturing.

Optimizing Abrasive Water Jet Machining for Enhanced Machining of 316 Stainless Steel

Abrasive Water Jet Machining (AWJM) is a non-traditional machining process renowned for its versatility and ability to cut a wide range of materials precisely. This research article presents an in-depth analysis of the optimization of AWJM parameters for machining 316 stainless steel, aiming to enhance surface quality and machining efficiency. Through a comprehensive experimental setup, the study explores the effects of varying the speed, standoff distance (SOD), and flow rate on the surface roughness (Ra) of the machined workpiece. The Taguchi method's L9 orthogonal array is employed to design the experiments, and a signal-to-noise (S/N) ratio analysis, alongside an analysis of variance (ANOVA), is utilized to discern the most significant machining parameters. Response tables for S/N ratios and means are created to summarize the effects, and main effects plots are generated to visualize trends in the data. Furthermore, a regression model is developed to correlate the machining parameters with the surface roughness, which is validated by a high coefficient of determination. Residual plots and diagnostics for unusual observations are utilized to ensure the robustness of the model. The study concludes that SOD is the most influential parameter, followed by speed and flow rate. The optimization results provide a quantitative understanding that can significantly contribute to the industrial application of AWJM for 316 stainless steel, ensuring optimal surface integrity and operational cost-effectiveness. The findings of this research offer pivotal insights for manufacturing industries that seek to integrate AWJM into their production processes.

EXPERIMENTAL INVESTIGATIONS ON THE ELECTROCHEMICAL MACHINING OF D3 DIE STEEL MATERIAL AND MULTI-OBJECTIVE PARAMETER OPTIMIZATION

The removal of metal in the form of sludge can be termed an ECM process, having primary salient features consisting of the tool, workpiece, power supply and electrolyte solution. ECM is a potential nontraditional machining process that includes too many factors contributing to process performance. Therefore, obtaining optimal factor combinations for higher efficiency is complex. In this research work, machining parameters are voltage, powder concentration and electrolyte concentration, based on which material removal rate (MRR) and surface roughness (SR) are derived. Regression analysis, signal-to-noise ratios and analysis of variance (ANOVA) are performed to determine the optimal levels and to examine the effects of machining parameters on MRR and SR. The results derived from the experiments noticed that the MRR decreases when the voltage level is increased and surface roughness decreases for the applied voltage. In case of an increase in powder concentration, MRR decreases from range 4 to 6 and further reduces to 6–8. In case of SR, it additionally decreases on the maximum level of powder concentration. In electrolyte concentration, the MRR decreases on the increment of electrolyte and increases after a short period of time; simultaneously SR is increased from range value 10–20 and decreased with a maximum level 20–30 of NaCl solution. A comparison of MOORA-PCA and TOPSIS-PCA demonstrates the superiority of TOPSIS over MOORA technique. Statistical results (95% confidence level) indicate that the voltage, powder concentration, and electrolyte concentration affect the surface roughness by 69.50%, 8.67% and 14.59% in the electrochemical machining of d3 die steel. Therefore, the optimum combination of process parameters corresponding to voltage: 45[Formula: see text]V, powder concentration: 4[Formula: see text]g/L, and electrolyte concentration: 10 NaCl/L, respectively, were found to yield the desired result. The prediction accuracy of the TOPSIS-PCA hybrid approach model is found better than MOORA-PCA technique.

Enhancing efficiency in photo chemical machining: a multivariate decision-making approach

Non-Traditional Machining (NTM) outperforms traditional processes by offering superior geometric and dimensional accuracy, along with a better surface finish. Photo Chemical Machining (PCM) represents one such NTM process, using chemical etching for material removal. PCM finds substantial application in the creation of microchannels in pharmaceutical, chemical and energy industries. Several input parameters—such as etchant concentration, etching time and etchant temperature—profoundly influence the machining’s quality and efficiency. Therefore, the optimization of these parameters is crucial. This study presents a comparative analysis of five Multiple Criteria Decision Making (MCDM) techniques—Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Multi-Objective Optimization on the basis of Ratio Analysis (MOORA), Additive Ratio Assessment (ARAS), Weighted aggregated sum product assessment method (WASPAS) and Multi-Attributive Border Approximation Area Comparison Method (MABAC)—for the optimization of the PCM process. Key performance metrics considered are Material Removal Rate (MRR), Surface Roughness (SR), Undercut (Uc) and etch factor (EF). The weights of these criteria were calculated using the Criterion-Induced Aggregation Technique (CRITIC) and was compared with other popular methods like MEREC, Entropy and equal weights. MRR and EF are seen as beneficial criteria, while SR and Uc are perceived as cost criteria. Optimum process parameters were identified as 850 g/L etchant concentration, 40 min etching time and 70°C etchant temperature. Two of the three employed MCDM techniques agreed on these optimal parameters, reinforcing the findings. Furthermore, a strong correlation was observed amongst the employed MCDM techniques, further validating the results.

Material Selection for PMEDM Process

Powder Mixed Electro-Discharge Machining (PMEDM) is a non-traditional machining process. This method is used more and more to machine complex surfaces, parts made from materials with high hardness. Three indispensable components in PMEDM are powder, dielectric fluid and electrode. These components have a great influence on the efficiency of the machining process. This study was conducted to choose the best types for all three components mentioned above. The numbers of powder types, dielectric fluid types and electrode material types considered were ten, three and seven, respectively. The weight of each criterion in each product category was calculated using the two methods Rank Order Centroid (ROC) and Rank Sum (RS). In each case, the two methods were used to rank the alternatives are Faire Un Choix Adéquat (FUCA) (in French) and Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS). The optimal option will be found from the results of ranking the alternatives. The results of ranking the alternatives were also subjected to sensitivity analysis by using Sperman coefficient. This study discovered that the best option found when using the FUCA method is also the best option found when using the MARCOS method, and the best option that was found does not depend on the used weighting method.

A State-of-the-Art Review on the Recent Advancements in the Electrochemical Abrasive Flow Machining Process

Abrasive flow machining (AFM) is a non-conventional machining method used for the fine finishing of complex external/internal surfaces of metallic parts. Generally, this process has a low material removal rate (MRR) and is a more time-consuming process. So, to overcome this problem, various researchers employed hybridization of the abrasive flow machining process (AFM) with a distinct nontraditional machining process. Hence, this paper aims to provide a detailed review of the electro-chemical assisted AFM (ECA2FM) process, which is a combination of electrochemical machining and abrasive flow process. Then, the effect of processing parameters such as grit size and type, the total number of cycles, current, voltage, abrasive type and size, extrusion pressure, design of workpiece holding device and proper tooling on the material abrasion and surface finishing is summarized. Thereafter the Progressive outlook on the integration of electrochemical AFM with magnetic energy is given. Finally, the emerging applications of the electrochemical AFM process are discussed.

A review of experimental investigations to attain productive and sustainable micro-electrical discharge machining process on metals and superalloys

Purpose This study aims to understand the current production scenario emphasizing the significance of green manufacturing in achieving economic and environmental sustainability goals to fulfil future needs; to determine the viability of particular strategies and actions performed to increase the process efficiency of electrical discharge machining; and to uphold the values of sustainability in the nonconventional manufacturing sector and to identify future works in this regard. Design/methodology/approach A thorough analysis of numerous experimental studies and findings is conducted. This prominent nontraditional machining process’s potential machinability and sustainability challenges are discussed, along with the current research to alleviate them. The focus is placed on modifications to the dielectric fluid, choosing affordable substitutes and treating consumable tool electrodes. Findings Trans-esterified vegetable oils, which are biodegradable and can be used as a substitute for conventional dielectric fluids, provide pollution-free machining with enhanced surface finish and material removal rates. Modifying the dielectric fluid with specific nanomaterials could increase the machining rate and demonstrate a decrease in machining flaws such as micropores, globules and microcracks. Tool electrodes subjected to cryogenic treatment have shown reduced tool metal consumption and downtime for the setup. Practical implications The findings suggested eco-friendly machining techniques and optimized control settings that reduce energy consumption, lowering operating expenses and carbon footprints. Using eco-friendly dielectrics, including vegetable oils or biodegradable dielectric fluids, might lessen the adverse effects of the electrical discharge machine operations on the environment. Adopting sustainable practices might enhance a business’s reputation with the public, shareholders and clients because sustainability is becoming increasingly significant across various industries. Originality/value A detailed general review of green nontraditional electrical discharge machining process is provided, from high-quality indexed journals. The findings and results contemplated in this review paper can lead the research community to collectively apply it in sustainable techniques to enhance machinability and reduce environmental effects.

Multi-performance optimization for AWJ drilling process in cutting of ceramic tile: BBD with EOBL-GOA algorithm

PurposeThe goal of this study is to determine the values of the process parameters that should be used during the machining of ceramic tile using the abrasive water jet (AWJ) process in order to achieve the lowest possible values for surface roughness and kerf taper angle.Design/methodology/approachIn the present work, ceramic tile is processed by the AWJ process and experimental data were recorded using the RSM approach based Box–Behnken design matrix. The input process factors were water jet pressure, jet traverse speed, abrasive flow rate and standoff distance, to determine the surface roughness and kerf taper angle. ANOVA was used to check the adequacy of model and significance of process parameters. Further, the elite opposition-based learning grasshopper optimization (EOBL-GOA) algorithm was implemented to identify the simultaneous optimization of multiple responses of surface roughness and kerf taper angle in AWJ.FindingsThe suggested EOBL-GOA algorithm is suitable for AWJ of ceramic tile, as evidenced by the error rate of ±2 percent between experimental and predicted solutions. The surfaces were evaluated with an SEM to assess the quality of the surface generated with the optimal settings. As compared with initial setting of the SEM image, it was noticed that the bottom cut surface was nearly smooth, with less cracks, striations and pits in the improved optimal results of the SEM image. The results of the analysis can be used to control machining parameters and increase the accuracy of AWJed components.Originality/valueThe findings of this study present an innovative method for assessing the characteristics of the nontraditional machining processes that are most suited for use in industrial and commercial applications.

EFFECT OF PROCESS PARAMETERS ON THE PERFORMANCE OF POWDER MIXED EDM OF Ni-BASED SUPERALLOYS: A REVIEW

Ni-based superalloys fall under the category of difficulty in machining type material owing to their poor thermal conductivity and high strength at extreme temperatures. Machining such materials using the traditional approach is a tremendously difficult task. On the other hand, EDM, one of the most sophisticated electro-thermal manufacturing processes, is used to machine such materials. It is a well-known non-traditional machining process for generating parts that require accuracy, have complex shapes, and are small in size. However, the use of EDM in Ni-based superalloys has some disadvantages like poor surface finish and low material removal rate. So, to alleviate these disadvantages, researchers introduced powder mixed dielectric fluid in the EDM process. Further, the performance of this technique has been enhanced by studying the effect of various nano/micro-size particles and their concentrations in the dielectric medium. In this paper, the authors have reviewed the impact of non-electrical and electrical process parameters on the output responses when machining Ni-based superalloys using powder mixed EDM. The challenges faced during the conventional machining of Ni-based superalloys and the mechanism proposed for powder mixed EDM, especially under the influence of suspended powders into the dielectric medium have also been presented in this paper. Finally, future research areas of powder mixed EDM of Ni-based Superalloys, such as (i) its modelling and simulation and (ii) the effect of tool motion and powder properties on its performance, are discussed in brief.

Parametric optimization of electric discharge machining of Ni 55.65Ti based shape memory alloy using NSGA II with TOPSIS

Nickel–Titanium based shape memory alloys (SMAs) are a unique category of smart materials that possess remarkable properties like super-elasticity, shape memory effect, high wear and corrosion resistance, thereby rendering them appropriate for uses such as composite structures, aerospace, and biomedical uses etc. These distinctive features make these alloys ‘difficult in machining’ using the traditional approach of machining process. In the present investigation, machinability aspects of Ni55.65Ti-SMAs using non-traditional electric discharge machining process has been studied. The effect of input process parameters i.e., pulse time ON, duty factor, peak current on the surface roughness and material removal rate has been investigated. The output responses viz. Minimum surface roughness along with maximum material removal rate having values of 6.828 μm and 4.552 mm3/sec respectively were achieved. Furthermore, empirical modelling and ANOVA study have been executed based on the full factorial design analysis. The non-dominated sorting genetic algorithm IIalgorithm IIalg2 commonly known as NSGA II, used crowding distance approach for multi-objective process optimization. The results obtained with NSGA-II were again ranked using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach to give a more thorough understanding of the solution space and opt for the best possible solution.

To examine the effect of input parameters of rotary EDM on surface roughness of cryogenically treated Monel 400

Rotary electric discharge machining (REDM) has an advantage over traditional EDM in that the tool speed can be varied to achieve better surface characteristics of machined components. Cryogenic treatment also improves the machining efficiency of non-traditional machining processes such as EDM. REDM is used in this study to establish a relationship between response parameters and control parameters while machining cryogenic treated alloy Monel-400 with a cryogenically treatedcopper electrode. The RSM approach was used to design experiments to investigate the effect of parameters such as tool speed, peak current, pulse on time, and duty cycle on surface roughness. An experimentally validated regression modal has been proposed. As per SEM examination, the typical surface produced for the setting of low pulse energy (at pulse on time:100 µs; Current:12 A; duty factor: 0.40; and tool rotation = 800 rpm) is found to be finer and showing shallow carters.

Comparative study of micro-die sink and micro-EDM drilled holes for electrode wear and surface roughness

Electrical Discharge Machining (EDM) is one of the most widely used non-traditional machining processes due to its numerous advantages. Since there is a continuous demand for miniaturized components, micro-EDM became a very popular process. The high temperature of melting and vaporization in the machining zone causes uncontrollable electrode wear to occur in the micro-Electrical discharge machining (micro-EDM) process. In the micro-EDM process, it will make a great impact on dimensional accuracy. In this study, a comparison of micro die sinking and micro Electric Discharge drilling processes has been conducted for the three varying levels of parameters for Voltage, Capacitance, and their effect on the Electrode Wear Rate (EWR) and Surface Roughness (Sa) were studied. Then the parameter that is more strongly influencing the unwanted EWR and Surface Roughness was identified and discussed. The maximum percentage change in surface roughness while micro-EDM die sinking and micro-EDM drilling was found to be 66% and 50%. for the given capacitance also 68% and 49% for the given voltage. Similarly, the highest percentage of EWR occurred while micro-EDM die sinking and micro-EDM drilling has been found at 67% and 65% for the given capacitance also 78% and 52% for the given voltage. The erosion of electrode at the bottom surface has been observed before and after the micro-EDM die-sinking and drilling processes.

Experimental investigation of electrode wear assessment in the EDM process using image processing technique

Abstract One of the widespread non-traditional and non-contact machining processes is the electrical discharge machining (EDM) process, which depends on removing material from the surface by different repeat electrical discharges between a machined workpiece and a cutting tool called the electrode. The electrode wear in the EDM process is one of the parameters of significant technological interest. This article proposed a reliable image-based system that automatically detects and computes electrode wear in EDM of rectangular steel workpiece plate of AISI 314 using a copper electrode with a rectangular shape. MATLAB environment and image processing toolbox are used to identify and manipulate the captured rectangular electrode image for rectangular electrode data extraction needed for the electrode wear identification process. The results show that the proposed image-based approach, using a non-contact measuring system and relatively inexpensive equipment, is suitable with good accuracy and efficiency for measuring and testing electrode wear.

Prediction of material removal rate of SS304 alloy in electrochemical micromachining using machine learning

Electrochemical micromachining (ECMM) is a non-traditional machining process used in machining microfeatures that are being used in the fabrication of MEMS, finishing surgical tools, implants, and cooling channels. Machine learning (ML) algorithms can be used to predict the output of a machining process based on the given input parameters. In this Work, ML is interpreted into ECMM where, different ML models were built based on Decision tree regression, Random Forest regression, and Support vector regression algorithms to find the relationship between the interactive effects of the type of electrolyte, voltage, feed rate, and duty cycle on Material Removal Rate (MRR). The work also aims to find the best-fit algorithm and approach toward the prediction of MRR of AISI SS304 alloy when machined in ECMM using Regression Analysis. The results showed that higher efficiency was attained in the Decision tree regression model with a training accuracy of 99.87% and testing accuracy of 99.75% and decision tree was found to be the best fit algorithm for the ECMM process for the single response parameter.

Processing of Al/SiC/Gr Hybrid Composite on EDM by Different Electrode Materials Using RSM-COPRAS Approach

The present research used the stir-casting method to develop an Al-based composite. The developed composite exhibited challenges while being processed on conventional machining. Thus, a non-traditional machining process was opted to process the composite. The machining variables selected for the current research were the pulse off time (Toff), pulse on time (Ton), servo voltage (SV), current (I), and tool electrode. Three tool electrodes (SS-304, copper, and brass) were used to process the developed composite (Al/SiC/Gr). The experimental plan was designed using response surface methodology (RSM). The output responses recorded for the analysis were the material removal rate (MRR) and tool wear rate (TWR). The obtained data was optimized using complex proportional assessment (COPRAS) and machine learning methods. The optimized settings predicted by the RSM–COPRAS method were Ton: 60 µs; Toff: 60 µs; SV: 7 V; I: 12 A; and tool: brass. The maximum MRR and TWR at the suggested settings were 1.11 g/s and 0.0114 g/s, respectively. A morphological investigation of the machined surface and tool surface was conducted with scanning electron microscopy. The morphological examination of the surface (machined) presented the presence of cracks, lumps, etc.

A comprehensive study of electrochemical machining of monel-400 alloy with different electrolytes: Research challenges

Electrochemical machining plays an important role in production and manufacturing industries like aircraft, atomic and healthcare. Even with difficult –to- machine materials, ECM is generally preferred to machine- nickel- based alloys like Monel 400 or Monel super alloy which consists of about 67 % Nickel and 23 % Copper. It is highly unaffected to corrosion and various acids such as sulphuric acid (H2SO4), hydrochloric acid (HCl) etc. due to which it is highly used in aerospace, oil production and refining, marine applications, industrial heat exchangers. Nowadays nickel hydroxides are major concerns which are toxic to environment present in sludge while machining nickel based alloys like Monel alloys and other super alloys need to be filtered before discharged into environment. This paper reviews the Electrochemical machining of Monel 400 alloy by various influencing parameters like applied voltage, current density, concentration of electrolyte, electrolyte pressure and type of electrolyte on material removal rate (MRR) tool wear rate (TWR) and surface roughness (SR). The study also suggests that the varying process parameters of ECM machining influence the various machining performances such as material removal rate (MRR) tool wear rate (TWR) and Surface finish (Ra) for Monel 400 alloy. In a recent study which utilized meta-heuristic algorithms (MFO and GWO) helped in increasing MRR by 0.242 g/min and Nickel presence by 57.72 PPM while machining Monel 400 and hence a better machining performance is obtained. The paper also aims at addressing the latest developments in machining Monel alloy as compared with conventional methods of machining and its various research challenges for further developments in non-traditional machining processes.

Optimization of process parameters in wire electrical discharge machining using teaching learning based optimization

The Un-Conventional or Non-Traditional machining process is aimed to produce products at right quality. WEDM (Wire electrode discharge machining) is unconventional advanced machining technique for better accuracy. In WEDM a thin wire is used to remove the material from the work piece through electrical spark. Parameters optimization plays a major role in machining process to achieve better quality. In this paper, a technique TLBO (teacher and learner based optimization) was used to parameters optimization in WEDM process of high speed steel (HSS) plates. Experimental values were taken from Sivanaga Malleswara Rao et al. (2017) article..