Arc Machining Research Articles

Magnetic field assisted blasting erosion arc machining (M-BEAM): A novel efficient and quality improved machining method for Inconel 718

Electrical Arc Machining (EAM) presents an efficient technique for processing difficult-to-cut materials such as Inconel 718. However, despite its extremely high machining efficiency, the high energy density in machining makes it difficult to achieve a desirable surface quality that can be compatible with the subsequent cutting process. To solve this issue, this study explores the application of a magnetic field to enhance the performance of Blasting Erosion Arc Machining (BEAM), namely Magnetic Field assisted BEAM (M-BEAM). It leads to an increased Material Removal Rate (MRR) and reductions in both the Tool Wear Ratio (TWR) and the Surface Roughness (represented by Sa) with the increasing magnetic field strength, attributed to the improvements of the arc deflection and molten metal expelling. Moreover, the electromagnetic force is positively correlated with the discharge energy, which can further improve the machining performance of BEAM, especially when the hydrodynamic force is hard to act at large discharge energy. The MRR and Sa are enhanced and reduced by 14.52 % and 41.86 % respectively due to the stronger control effect at large energy. After multi-objective optimization, the optimized MRR in M-BEAM achieves a 9.22 % enhancement and Sa also decreases by 25.96 % and 60.61 % in the roughing and finishing process. This advancement also elevates machining precision as well as mitigates defects such as debris adhesion, overburning, and cracks. Moreover, the recast layer is significantly reduced by 88.59 % and 89.96 %. Consequently, M-BEAM promises extensive applications in aerospace industries for machining Inconel 718 and even for other difficult-to-cut materials with high efficiency and quality.

The effect of the slope angle and the magnetic field on the surface quality of nickel-based superalloys in blasting erosion arc machining

The effect of the slope angle and the magnetic field on the surface quality of nickel-based superalloys in blasting erosion arc machining

Effects of discharge current and discharge duration on the crater morphology in single-pulse arc machining of Ti6Al4V

Effects of discharge current and discharge duration on the crater morphology in single-pulse arc machining of Ti6Al4V

Surface structure evolution and measurement analysis of Ti6Al4V titanium alloy by electric arc electrochemical machining

In this study, a theoretical model about the conversion mechanism of material removal by electric arc electrochemical machining (EAECM) was derived. The limiting conversion rate of ECM-EAECM was measured on this basis. The current waveform, surface integrity, shape accuracy and process indexes during machining were measured and evaluated. The results showed that when the limiting conversion rate of ECM was exceeded, the material removal consisted of alternating effects of electric arc machining (EAM) and ECM. The proportion of EAM increased and the surface structure evolved into discharge defects, verifying the correctness of the theory. In addition, parametric crossover experiments were performed to clarify the degree of parametric response to EAECM modulation. Finally, the optimal combination of parameters resulted in a material removal rate of up to 5452 mm3/min, a relative electrode wear rate of 1.39 %, and a surface roughness of only 4.62 μm.

Design of Experiment for Determining Setting Parameter on Plasma Arc Machining for Stainless Steel Plate Cutting with Full Factorial Design

Plasma Arc Machining is a metal cutting where conductor metal such as sheets metal are cut with plasma arc. Problem in plasma arc machining is the result of cutting has burr which is quite large due to the heat, resulting the surface roughness on the workpiece. This research aim to minimize the surface roughness of the stainless steel plate uses a design of experiment method with full factorial design. In this research, there are three factors, that are torch height, cutting speed, and electric current. Each factor has three levels. By using full factorial design, the number of treatments are 33=27 trials. The results of the research on data processing analysis of variance show that the most influential factor on surface roughness is cutting speed with contribution value of 90.76% followed by two other factors, that is height torch with contribution value of 2.42% and electric current with contribution value of 0.23% and contribution value of noise by 6.59%. Then based on data processing robust design the optimum combination of parameters is obtained by using setting 1 mm torch height, 2400 mm/min cutting speed, and 30 A electric current. Based on the confirmation experiments, experiments with optimum parameter combinations can reach a gap noise of 2.283 dB. Therefore, the design of experiment for determining parameter setting plasma arc machining can determine the optimum combination of parameters to minimize the surface roughness.

Electrode-assisted hydrodynamic arc breaking for efficient side-cutting of Ti2AlNb

This study introduces novel electrode-assisted hydrodynamic arc-breaking methods for the efficient side-cutting of Ti2AlNb, a material widely used in the aerospace industry but notorious for its machining difficulties. The research aims to overcome the limitations of traditional arc machining techniques, with a particular emphasis on deep-sidewall machining. The paper begins with an overview of electrode manufacturing processes used in electrode-assisted hydrodynamic arc-breaking methods. Following that, Fluent simulations are used to analyze particle behavior during the side-cutting process. The feasibility of these novel electrodes is then demonstrated using extensive single-factor experiments. The optimization of process parameters using orthogonal experiments, which significantly improves machining efficiency, is a crucial aspect of this study. The results showed that electrode-assisted arc breaking and hydrodynamic arc breaking for Ti2AlNb side-cutting processing increased the material removal rate by 65 % compared to conventional electrode materials, reduced relative electrode wear rate by 54 %, and reduced surface roughness by 85 %. Therefore, electrode-assisted hydrodynamic arc-breaking methods for side-cutting processing have a promising future in difficult-to-machine materials in the aerospace industry.

Study of arc behavior and machining effects of the novel magnetic field assisted blasting erosion arc machining method

Electrical Arc Machining (EAM) is a high-efficiency, cost-effective method for machining electrical conductive materials especially difficult-to-cut ones. Because this thermal process relies on high-density energy to melt and remove material, effective arc control is crucial to maintain stable machining without overheating the workpiece. However, due to the limited discharge gap, it is difficult for the existing arc breaking methods to manage the arc plasma in a timely and uniform manner. This paper proposes a novel method named Magnetic Field Assisted Blasting Erosion Arc Machining (M-BEAM). This method introduces a magnetic field to enhance the control effect on arc plasma by directly influencing charged particles. Initially, the arc control and material removal mechanisms were revealed through an analysis and experiments of single arc discharge. Both simulated particle trajectory and high-speed photographs demonstrated that the arc plasma deflects much earlier in the magnetic field than that of traditional BEAM. Moreover, the expulsion effect of molten metal was significantly improved, resulting in a larger volume crater. Building on this, the magnetic-assisted arc control mechanism was utilized to enhance the performance of BEAM. Experimental results indicate that compared with traditional BEAM, Magnetic Field assisted BEAM increases its Material Removal Rate (MRR) by 10.09% while significantly reducing the Tool Wear Ratio (TWR) by 29.02%. Additionally, surface roughness (Sa) is decreased by 47.73%, and the thickness of the recast layer is decreased by 78.54%. Therefore, M-BEAM can further improve the machining performance and build a good foundation for subsequent machining.

Effect of the Cryogenically Treated Copper Nozzle Used in Plasma Arc Machining of S235 Steel

The investigation was carried out by making use of the design of experiments method in order to achieve its objective, which was to study wear analysis in relation to a cryogenically treated nozzle that was utilized in plasma arc machining. Kerf width and surface roughness are two output characteristics that are key variables in deciding the quality of the cut and the efficiency of the operation. Both of these metrics are outputted by the process. While machining S235 steel, an investigation into the impact that nozzle treatment has on various quality metrics is currently under way. The examination is carried out with the arc voltage, the cutting speed, and the gas pressure, all serving as important components. A cryogenic treatment of the nozzle material using liquid nitrogen at a temperature of −194°C has been attempted in an effort to increase the life of the nozzle. Machining is performed using two different nozzle conditions, such as cryogenically treated and cryogenically untreated, with regard to the input parameter combinations that have been selected. To have a better understanding of the wear behavior of nozzles, an image from a scanning electron microscope is studied. Because of the treatment, the production of wear tracks in the direction that gas flow takes has been drastically decreased. This, in turn, has increased the cutting efficiency by decreasing the amount of arc current that was necessary. In addition, a grey relational analysis is carried out in order to find the best possible machining settings in both conditions. The parameters that were optimized for a nozzle that had been cryogenically treated were 6 bar of gas pressure, 120 amperes of arc current, and 1800 of cutting speed per minute. The use of cryogenic treatment resulted in a reduction of surface roughness by 0.4670 µm and a narrowing of the kerf width by 0.96 mm. It is clear from the SEM pictures of untreated and cryogenically treated nozzles that thermal distortion and wear in the nozzle tip area are minimized to a greater extent in the treated nozzle. This is evidenced by the fact that the treated nozzle has a more uniform appearance.

Parameter Optimization Design of Plasma Arc Machining SS 304 Alloy by Means of Probabilistic Multi – objective Optimization

Plasma arc machining is an unconventional machining process, which is widely used to machine intricate part profiles of alloys with difficulty in general machine. In general, the surface roughness, kerf ratio, and material removal rate (MRR) are used as evaluation targets of the production process and quality of the machining samples; the plasma arc cutting parameters, such as arc voltage, standoff distance, cutting speed, and plasma offset, are employed as the input parameters for the cutting of SS 304 alloy machined at two different types of nozzles (130 A and 200 A). The parameter optimization design of plasma arc machining is a typical of optimal problem with multiple objectives. The employment of a rational multi – objective approach is quite important to the designers for the parameter optimization design of plasma arc machining. In this article, the probabilistic multi – objective optimization is utilized to conduct the parameter optimization design of plasma arc machining SS 304 alloy of thickness 6 mm, which is designed according to a mixed Taguchi design of L18 orthogonal array. The optimal parameters of plasma arc machining SS 304 alloy from the designed experiment for Nozzle 1 (130 A) are arc voltage at 136V, cutting speed of 2000mm/min, standoff distance of 2mm, and plasma offset of 2.25mm; the optimized parameters of plasma arc in machining SS 304 alloy from the designed experiment for Nozzle 2 (200 A) are arc voltage at 133V, cutting speed of 2000 mm/min, standoff distance of 2 mm, and plasma offset of 1.25mm. The results indicate the reasonability of the approach.

Milling performance of Ti2AlNb by DC short electric arc machining based on different electrode materials

Milling performance of Ti2AlNb by DC short electric arc machining based on different electrode materials

AI based modeling & optimization of arc machining of Ti–6Al–4V alloy using ANN-evolutionary algorithm

AI based modeling & optimization of arc machining of Ti–6Al–4V alloy using ANN-evolutionary algorithm

Highly energy-efficient and safe-environment-friendly ultra short electrical arc machining for titanium alloy: Mechanism, characteristics, and parameter estimation

A higher energy-efficient and safe-environment-friendly method named ultra short electric arc machining (US-EAM) was proposed to meet the imperative needs of cleaner production. Low loop circuit resistance, long discharge time, and continuous energy output ensured highly stable and low energy output at low voltages in US-EAM. Voltage-current (V-A) analysis and single-point experiments investigated the machining stability. Typical US-EAM characteristics, such as the material removal rate (MRR), specific energy consumption (SEC), relative electrode wear rate (REWR), and surface characteristics, were investigated as a function of the electrode rotation speed, gas pressure, and milling depth. Moreover, the optimal parameters were obtained through the grey correlation situation decision method, showing that the MRR reached 17100 mm3/min, SEC was limited to 27.296 kJ/cm3, and the REWR was limited to 1.487%.

ELECTROCHEMICAL ARC DRILLING OF NICKEL–TITANIUM SHAPE MEMORY ALLOY USING MOLYBDENUM ELECTRODE: INVESTIGATION, MODELING AND OPTIMIZATION

In the present scenario, electrochemical arc machining (ECAM) (hybrid of electric discharge erosion and electrochemical dissolution) is an evolving procedure for difficulty in machining the materials due to constraints of existing processes. This research aims to investigate the machinability of Ni[Formula: see text]Ti alloy through electrochemical arc drilling using molybdenum electrode. Electrolyte concentration (ethanol with ethylene glycol and sodium chloride), supply voltage, and tool rotation are considered as the variable factors to evaluate the ECAM performance characteristics in drilling blind hole operation concerning overcut (OC), tool wear rate (TWR) and materials removal rate (MRR). Consequently, response surface methodology is implemented for predictive modeling of various performance characteristics. Finally, multi-objective optimization through desirability function approach (DFA) has produced a set of optimal parameters to improve the productivity along with the accuracy, which is the prime requirement for the industrial applicability of the ECAM process. Results demonstrated that supply voltage is the influential key factor for improvement of machining rate. Scanning electron microscope (SEM) photographs revealed the development of heat affected zone (HAZ), white layer, melted droplet, craters, re-solidified material, ridge-rich surface and voids as well as cavities around the end-boundary surfaces of a blind hole. Composition analysis through energy dispersive spectroscopy (EDS) indicated the oxygen content on the machined surface because electrolyte breakdown causes oxidation to take place at elevated temperatures across the machining zone. Moreover, carbide precipitation like TiC was found in the melting zone of the drilled hole, as revealed by X-ray diffraction (XRD) analyses, which has the affinity to reduce the SMA properties in HAZ.

Process Parameter Optimization of Abrasive Jet, Ultrasonic, Laser Beam, Electrochemical, and Plasma Arc Machining Processes Using Optimization Techniques: A Review

<div>A comprehensive literature review of the optimization techniques used for the process parameter optimization of Abrasive Jet Machining (AJM), Ultrasonic Machining (USM), Laser Beam Machining (LBM), Electrochemical Machining (ECM), and Plasma Arc Machining (PAM) are presented in this review article. This review article is an extension of the review work carried out by previous researchers for the process parameter optimization of non-traditional machining processes using various advanced optimization algorithms. The review period considered for the same is from 2012 to 2022. The prime motive of this review article is to find out the sanguine effects of various optimization techniques used for the optimization of various considered objectives of selected non-traditional machining processes in addition to deemed materials and foremost process parameters. It is found that most of the researchers have more inclination towards the minimization of Surface Roughness (SR) compared to the maximization of the Material Removal Rate (MRR) as their objective function for AJM and PAM. Similarly, for USM and ECM, researchers are more inclined towards the maximization of MRR compared to the minimization of SR. Minimization of the Heat-Affected Zone (HAZ) and SR are the two most considered response parameters for the LBM and its allied processes. This study provides ready-to-use details on the use of various advanced optimization techniques for AJM, USM, LBM, ECM, and PAM, with the considered workpiece material, process parameters, and imposed limitations. This review work is carried out on such a large scale that it will help future researchers and industrialists to decide their research direction.</div>

Unravelling the effect of CO2 laser machining parameters on the surface and shape memory characteristics of CuAlFeMn quaternary shape memory alloy

Shape Memory Alloys (often called SMAs) belong to a branch of alloys that “remember” their original shape. When the metals are twisted or disturbed from their initial shape, they are capable of coming back to their original shape on application of external stimuli such as magnetic field, heat or stress. Despite its widespread commercialization, Ni-Ti is considered a costly alternative. As a result, cost-effective and easy-to-process alloys such as Cu- and Fe-based alloys are currently being investigated. Processing of Fe-based alloys is difficult as their properties are texture dependant. Cu-based shape memory alloys are thus considered viable alternatives. So the unconventional machining operations like electrical discharge machining (EDM) and laser machining can also be used to manufacture Cu-based SMA components. But thermal machining operations like IBM (ion beam machining), EBM (electron beam machining), LBM (laser beam machining), PAM (plasma arc machining) and EDM (electrical discharge machining) removes material from the surface of the work piece through melting/vaporization. However, thermal damage such as theheat affectedzone is brought on by the intense heat flow in laser machining. Hence only a few works have been reported to differentiate and characterize the varied properties of the SMA machined through laser machining. This research work aims at optimizing the laser beam machining parameters and study its influence on surface characteristics and shape memory properties of Cu-based SMA. Based on the experiments carried out using L9 orthogonal array design matrix, optimal parameters were found out with response tables and graphs which is further analysed using grey relational analysis. Surface topography was analysed by scanning electron microscope (SEM). Differential scanning calorimetry (DSC) was used to investigate phase transformation temperatures and some thermodynamic parameters. X-Ray Diffraction (XRD) was also performed to find out the different phases that are present. The results showed the grey relational analysis coupled with Taguchi approach is an effective and convenient technique to optimize the laser machining parameters and achieve better results.

Current Research Trends of Electrical Arc Machining (EAM) with Reference to Electrical Discharge Machining (EDM)

Current Research Trends of Electrical Arc Machining (EAM) with Reference to Electrical Discharge Machining (EDM)

A hybrid short electric arc and electrochemical machining of the Ti6Al4V alloy

ABSTRACT In this study, the persistent challenges of high surface roughness and large thickness of the re-solidified layer (RSL) and the heat-affected zones (HAZ) in short electric arc machining (SEAM) are confronted by a hybrid simultaneous SEAM and ECM (SEACM) technique. The SEACM is investigated in low conductivity NaCl electrolytes as the working medium with an additionally applied air pressure, which can meet the requirements of both SEAM and ECM. Firstly, the effect of feed rate and electrolyte conductivities on material removal rate (MRR), roughness, line profiles, and surface morphologies were studied. Later, the current-voltage waveforms, topographies, PSD curves, and dimensional accuracy at different feed rates and electrolyte conductivities were analyzed. The SEACM significantly reduced the roughness and the thickness of RSL and HAZ at an appropriately optimized electrolyte conductivity and feed rate. The developed technique provides the basis for transferring from rough machining to finishing in SEAM.

Influence of the Cutting Feed Rate on the Hardness and Microstructure of Copper Using Plasma Arc Machining (PAM)

This study investigated the influence of the cutting feed rate on the hardness and microstructure of copper machined using a plasma arc (PA) to examine the resulting changes and their impact on the quality of the cut surface. Various constant cutting feed rates and amperage values were used as parameters to measure the cutting performance. Pre- and post-cut hardness measurements and scanning electron microscope (SEM) images were taken. The hardness of the copper surface was the same before and after plasma arc cutting (PAC). PAC did not affect the copper’s hardness or the microstructure of the thermally affected cutting zone. The copper from the cut surface was melted by the PA operation near the edge of the cutting surface with no change in the microstructure. SEM imaging of the cut confirmed this. Thus, the quality of the cutting surface was not affected. In addition, the microstructure of the copper’s thermally affected cutting zone did not alter the cutting surface’s quality. Hardness measurements post-cutting yielded 69.28, 71.65, 70.15, and 60.09 HB for four tests at 500 mm/min and 30 A. The lowest cutting width was 1.504 mm at 12,000 mm/min, and the surface roughness was 2.5 µm at 500 mm/min.

Study on the effect of air and argon assisted short electric arc machining on Ti6Al4V titanium alloy

The use of an argon–water medium rather than air–water medium is proposed to increase the material removal rate (MRR) and decrease surface oxide production for short electric arc machining. To provide a more stable discharge state during high-temperature processing, this approach uses argon to reduce the production of highly resistant refractory materials (e.g. titanium oxide). For short-arc machining of Ti6Al4V titanium alloy, the effects of the voltage, milling depth, electrode speed and gas flow rate on the MRR, relative electrode loss rate and surface roughness were compared for air–water and argon–water mixtures. It was discovered that the milling depth and voltage had the greatest impact on the MRR, voltage and electrode rotational speed had the greatest impact on the relative tool electrode wear rate (REWR), and voltage had the greatest impact on the surface roughness. In contrast to the air–water mixture, the MRR with the argon–water working medium was as high as 9391 mm3/min, with a 13% improvement in MRR, as well as a smooth and flat surface of the machined workpiece. Nevertheless, the REWR increased to 2.46% and the surface roughness was at least 432 μm with the argon–water medium, increasing by 59 μm compared with the air–water medium. Moreover, the addition of argon reduced the types of surface compound on the workpiece and decreased the residual stress defects, including the recast layer, microcracks and holes.

A Grey Fuzzy Approach to the Selection of Cutting Process from the Aspect of Technological Parameters

This study deals with the selection of the cutting process using the grey fuzzy relation approach. The analysis was performed using plasma arc machining, laser beam machining, and abrasive waterjet machining on three different workpiece thicknesses with different cutting speeds. The objective was to select the best cutting process considering several performance characteristics such as machining time, dimensional accuracy, kerf width, and surface roughness. Data normalization, grey relation coefficients, fuzzy inference system, and grey fuzzy relation grade are used to evaluate the machining performances of the machining processes. The developed fuzzy model can be used to study the effects of different cutting processes on technological features. The results show that the grey fuzzy technique can be effectively used for the analysis and selection of cutting processes.