Input Machining Parameters Research Articles

Machine learning assisted prediction with data driven robust optimization: Machining process modeling of hard part turning of DC53 for tooling applications supporting semiconductor manufacturing

This research investigates the hard part turning of DC53 tool steel, which is engineered for better mechanical properties compared to AISI D2 tool steel, using Xcel cubic boron nitride. The machining input parameters such as workpiece hardness (different heat treatments), cutting speed, feed rate, and depth of cut are used to thoroughly evaluate process science across conflicting machinability attributes such as cutting tool life, machined workpiece surface roughness, volume of material removed, machine tool power consumption, and tool-workpiece zone temperature. A full factorial design of experiments with two levels, resulting in 16 experiments, is performed with statistical parametric significance analysis to better control process variability. Multiple artificial neural network (ANN) architectures are generated to accurately model the non-linearity of the process for better prediction of key characteristics. The optimized architectures are used as prediction models to a non-sorting genetic algorithm (NSGA-II) to determine the optimal compromise among all conflicting responses. The significance analysis highlighted that heat treatment is the most influential variable on machinability, with a significance of 74.63% on tool life, 59.03% on roughness, 66.45% on material removed, 38.03% on power consumption, and 29.60% on interaction-zone temperature. The confidence of all ANN architectures is achieved above 0.97 R2 to accurately incorporate parametric relations with physical mechanisms. The compromise against conflicting machinability attributes identified by NSGA-II optimization results in a 92.05% increase in tool life, a 91.83% increase in volume removed, a 33.33% decrease in roughness, a 26.73% decline in power consumption, and a 9.61% reduction in machining temperature. The process variability is thoroughly analyzed using statistical and physical analyses and computational intelligence, which will guide machinists in better decision-making.

Machining performance of Inconel 718 in dry, air-cooling and bio-oil based minimum quantity lubrication environments

Inconel 718 is a widely used superalloy in aerospace, automotive and marine industries due to its high strength and ability to resist high mechanical stresses at high temperatures. However, its low thermal conductivity results in high temperature at the cutting zone during machining that adversely affects the surface quality and tool life. This work explores the machining performance of Inconel 718 alloy by cemented carbide cutting tool in dry, air-cooled and bio-oil based minimum quantity lubrication environments. Putranjiva roxburghii bio-oil was used as a cutting fluid. Emulsions were prepared by varying the water-oil ratio by volume as 100:5, 100:10, 100:15, 100:20, 100:25 and 100:30. The machining input parameters considered were spindle speed, feed rate and depth of cut. Box's evolutionary optimization technique was used to obtain the best combination of machining input parameters. Machining performance was assessed by surface roughness, tool temperature and tool wear. The most favourable machining environment was found to be the minimum quantity lubrication environment. The most desirable surface value of surface roughness obtained was 0.29 µm and the lowest temperatures of the tool and workpiece were 41.1 °C and 31.46 °C, respectively. These were obtained in a minimum quantity lubrication environment at a spindle speed of 180 r/min, feed rate of 16 mm/min and an emulsion ratio of 100:20. Microscopic analysis of the cutting tool revealed less wear indicating minimum quantity lubrication as the preferable environment for machining.

Optimization of Material Removal Rate and Wear Rate in EDM Machining of Inconel 625 Using a Novel Nano Al-Ni Composite Electrode: A Response Surface Methodology Approach

The electric discharge machine is a novel machining technique. When the tool and the work item do not communicate. Components composed of difficult-to-machine hard materials. In the current experimental inquiry, the process factors that influence the machining concert process and its effectiveness are explored. The response table for each set of machining limitations is used in conjunction with the response surface methodology in the research to determine the appropriate levels of machining factors. The work's machining input parameters are optimized for maximum material removal rate (MRR) and reduce the electrode wear rate (EWR) when electro-discharge machining Inconel 625 using the tool Nano Al-Ni composite electrode. Another analysis of variance is to identify the factors causing the different answers mentioned above. EDAX was also used to investigate the material composition of the workpiece. Machined workpiece surfaces were evaluated using scanning electron microscopy to assess surface accuracy and microstructural characteristics (SEM). The following conclusions may be drawn from this work. The MRR parameter that is most affected is pulse off time. As pulse-off time lengthens, MRR increases. According to the response graph, the greatest MRR value that can be attained under the ideal parameters of Ip = 8A, T-ON = 50μs, and T-OFF= 100μs is 0.0115 g/min. The magnitudes of MRR range from 0.0091 to 0.044 g/min. Impact of T ON and T OFF: Higher EWR is 0.0022 g/min is seen in runs with T ON = 70 μs and T OFF = 100 μs.

Experimental Investigation on Wire Electric Erosion Behaviour of Silicon Dioxide Particulate Reinforced Composite

The intend of current study was focused on the prediction of material removal rate (MRR) and surface roughness (SR) for the AA7050-SiO2 composite during wire electric erosion or discharge machining (WEDM) process using a brass (Br) wire electrode. Here, stir casting process was employed to develop the AA7050 matrix composite with inclusion of 10wt.% SiO2 particle reinforcement. The multi-objective optimization method of Technique for order preference by similarity to ideal solution (TOPSIS) approach has been applied to find out the optimal setting of input machining parameters such as peak current (Ip), pulse-on time (Ton) and pulse-off time (Toff). Furthermore, the significant effects of parameters were identified by analysis of variance (ANOVA). Taguchi L9 (33) orthogonal design has been formulated to perform the experimental work. TOP SIS results stated that the optimal setting of Ip at 30 amps, Ton of 130 μs and Toff of 55 μs provide the better MRR with lesser SR. The ANOVA results noticed that Ip has the prime noteworthy parameter over the adopted responses having a contribution of 45.67%, followed by Ton (32.34%) and Toff (12.26%), respectively. The confirmation test was carried out by the optimal parameters setting to verify the predicted results. Finally, the scanning electron microscopy (SEM) test was carried out for the machined surface of the composite specimen and it was reveals that the formation of craters and recast layer thickness in the machined surfaces.

An Experimental Study on Hard Turning Performance of DIN 17350 Tool Steel

The main objective is to investigate the effects of cutting speed, feed rate and depth of cut on surface roughness Rz in hard turning process of DIN 17350-tool steel (60÷62HRC) under dry condition. The full factorial experimental planning design with the help of Minitab 19 software was applied to evaluate the main effects and interaction effects of input machining parameters. The experimental results indicate that feed rate and depth of cut cause the stronger impact on the objective function Rz than cutting speed. In addition, the interaction effects of cutting speed and feed rate as well as the cutting speed and depth of cut have significant influences on the output, but the interaction effect between the feed rate and depth of cut has little impact. Furthermore, cutting speed of 1400 rpm, feed rate of 0.05 mm/rev, and cutting depth of 0.1 mm should be recommended for the smaller surface roughness Rz values.

Influence of Novel Al-Ni Electrodes on Roughness and Machining Time in Inconel 625 EDM Machining: A Comprehensive RSM Performance Analysis

In the context of Inconel 625 machining, this study explores the effects of novel Al-Ni nano electrodes on surface roughness and machining time. Thorough performance study was undertaken to clarify the complex connections between the machining settings, electrode composition, and final surface quality. Important insights were obtained via methodical experimentation and data analysis, which helps optimize the machining procedures for improved productivity and surface quality when milling Inconel 625. This research presents an experimental investigation of the effects of the Electric Discharge Machining (EDM) input parameters, namely current, pulse-on time, and pulse-off time, on the surface roughness and machining time output parameters. Inconel 625 was the material of choice for the work piece. Electric discharge machining oil was used as the dielectric fluid and aluminium nanocomposite as the tool electrode. Response surface methodology (RSM) approach was employed in the experimentation. ANOVA was used to optimize the parameters and obtain the lowest possible surface roughness (SR) and machining time. The findings show that pulse-off time is the most significant motivating factor for SR. The current is the main determining factor for machining time. The Central Composite Design approach was used for optimization.

Multi-Objective Optimization and Prediction of Responses Using GRA and ANN in Micro-EDM of Ti6Al4V Using Different Tool Materials

An experimental investigation was conducted to evaluate the machinability of a titanium alloy (Ti6Al4V) using copper (Cu), tungsten carbide (WC), and graphite (C) tools. Voltage (V), capacitance (pF), pulse-on time (Ton), and pulse-off time (Toff) were considered as the input machining parameters, whereas the material removal rate (MRR) and tool wear rate (TWR) were considered as the output machining parameters. A Taguchi L[Formula: see text] orthogonal array and gray relational analysis (GRA) were utilized to design and optimize the machining parameters for both responses. Artificial neural network (ANN) analysis was performed to predict the experimental outcomes. Scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS) were used to assess the surface morphology and determine the elemental composition of the machined surface. The results indicated that the optimum machining conditions for the copper tool were 150 V, 1000 pF, 15[Formula: see text]s (Ton), and 15[Formula: see text]s (Toff). However, the optimal machining conditions for the WC were 200 V, 100 pF, 25 [Formula: see text]s (Ton), and 10[Formula: see text]s (Toff), and the optimal conditions for the C were 200 V, 1000 pF, 20[Formula: see text]s (Ton), and 25[Formula: see text]s (Toff), respectively. The highest MRR achieved using the WC tool was 9.4510 mg/s, whereas the TWR of the Cu, WC, and C tools were 1.1039 mg/s, 1.0307 mg/s, and 1.2796 mg/s, respectively. The results showed that machining with the graphite tool had a higher TWR than machining with the Cu and WC tools.

Electrode Wear Rate of RBD Palm Oil as Dielectric Fluids on Electrical Discharge Machining (EDM) at Different Peak Current and Pulse Duration of Titanium Alloys

Electrical Discharge Machining (EDM) is a widely utilized commercial method for generating a variety of forms of materials that are used as the fundamental material in a wide range of industries. Vegetable oil as the dielectric fluid is one technique for assuring EDM's long-term viability because it is ecologically benign and biodegradable. This article presents experimental investigations on the influence of electrical discharge machining (EDM) input parameters on electrode wear rate (EWR) while using EDM oil and vegetable oil as dielectric fluid. Kerosene which is known as conventional oil for EDM has been used as a benchmark to compare the feasibility of the EDM process by using a different type of dielectric fluid which is RBD palm oil. Peak currents (Ip) in the 6 to 12A range and pulse durations (ton) in the 50 to 150 µs range were chosen as the major factors for this study to improve the efficiency of EDM operation. Scanning electron microscopy (SEM) was used to observe the morphology surface characteristics of the copper electrode and the migration of workpiece material elements to the tool electrode. The EWR increases with increasing peak current, although it is inversely related to pulse duration. The lowest EWR for kerosene and RBD palm oil is 0.0416mm3/min and 0.0480mm3/min, respectively, at the same IP=6A and ton=150µs. Simultaneously, the highest EWR for both kerosene and RBD palm oil was at the same IP=12A and ton=50µs, which is 0.1725mm3/min and 0.2425mm3/min, respectively.

MOGA and TOPSIS-based multi-objective optimization of wire EDM process parameters for Ni50.3-Ti29.7-Hf20 alloy

Conventional machining techniques face challenges in processing Ni-Ti-Hf alloys, which exhibit superior properties and are increasingly considered promising materials for high-temperature shape memory actuator applications. Thus, this article focuses on investigating the effect of Wire Electric Discharge Machining (WEDM) input parameters, namely discharge time (TON), pause time (TOFF), gap voltage (SV), and wire travel speed (WF), on the surface quality and shape memory properties of these alloys. These parameters were optimized to obtain a better removal rate (MRR) and surface finish quality (Ra) by employing a hybrid approach of Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Multi-Objective Genetic Algorithm (MOGA). TON emerged as the most influencing parameter for both MRR and Ra, and the sample machined using optimal parameter setting, which had a MRR of 5.287 mm3/min and Ra of 2.335 µm, showed better surface quality with fewer surface defects and irregularities, lower recast layer thickness of 10.057 µm, and better shape memory properties with less than 15 % deviation in their latent heat of transformation values and a less than 5ºC change in their austenite and martensite transformation temperature values, which indicates MOGA was successful in finding a trade-off between the two responses.

Optimization of Machining Parameters for Mild Steel in Dry Turning Using Taguchi Method

In present days every manufacturing industry thrives hard to produce quality products at an economical price while fulfilling the requirements of both manufacturer and the customer who happens to the end user of the product. Machining of sophisticated parts has posed a greater challenge to manufacturer. Optimization techniques like Taguchi method has been introduced and widely applied in industries in order to achieve the desiredgoals. In this analysis, an attempt is made to study the effects of machining parameters like speed, feed and depth of cut in a turning operation of mild steel by employing one of the widely used Optimization techniques which is Taguchi Method. The main input machining parameters namely Speed, Feed and Depth of cut are taken Page 7 into consideration, while the output parameter Material Removal Rate (MRR) is considered as the response variable. Taguchi method is applied and signal-to-noise ratio is calculated using L9 Orthogonal Array (OA) with the help of Minitab software. Based on the graphs from the Minitab software optimum levels of machining parameters were obtained and maximization of material removal rate (MRR) is found out and the same is checked experimentally in a machining workshop Keywords: Optimization technique, Taguchi Method, Turning Operation, Material Removal Rate, Minitab software.

Measurement and analysis of cutting forces using dynamometer in turning of EN-8 steel

This experimental study determines the cutting forces and optimum machining parameters identified in the turning operation on the lathe machine. The advantage of the present experiments is to improve the machining performance by high material removal rate and minimum tool bear, surface roughness and power. These experiments use EN8 steel workpieces and carbide as a cutting tool. Machining input parameters are speed of spindle (rpm), Feed (mm/rev) and depth of cut (mm). A triaxial dynamometer has been used to measure the thrust, feed, and cutting forces by the adaptive measurement of orthogonal components of cutting force acting on the workpiece. The digital data is transferred to the computer with LAB-VIEW software to compute the forces acting on the EN8 steel workpiece. The LAB-VIEW software displays the real-time cutting forces acting on the workpiece by the data acquisition program. Taguchi method is evaluated on output parameters of machining and the ANOVA method illustrates the error percentage from experimental values to predicted values. Finally, the range of inputs, the effect of input parameters and various cutting forces such as feed force, thrust force and resultant force have been analyzed.

Investigation of tool wear rate during micro-electrochemical spark machining process

Micro-electrochemical spark machining (micro-ECSM) process exhibits good potential to process any materials with high dimensional accuracy and surface quality. However, the tool erosion (due to sparking) in different directions affects the accuracy of the finished features. Tungsten carbide (WC) material is widely used as a tool material to minimize dimensional inaccuracy of the machined part. Tungsten is a potential alternative tool material for this process, having melting point higher than WC. Due to the high melting point, the radial and axial erosion may be reduced significantly. The in-depth study on tungsten tool erosion (wear) behaviour has not been studied widely. In this work, the influence of different input machining parameters on tool erosion behaviour has been explored. The influence of voltage, duty factor, pulse frequency, electrolyte type, their concentrations, and tool immersion length in the electrolyte was considered for the analysis purpose. The results reveal that voltage is the most influencing parameter for tool erosion which mainly occurs at the bottom edge, whereas electrolyte level is the least influencing parameter. The tool weight loss is increased to 153% by changing voltage from 50 V to 60 V whereas axial length loss is 146 µm at 60 V is observed. Further, the micro hole is fabricated at machining parameter voltage: 50 V, duty factor: 40%, frequency: 6 kHz, NaOH electrolyte of concentration 2 M, and 2 mm electrolyte level. The wall surface of the hole shows an average surface roughness of 4.31 µm having the average recast layer thickness and HAZ of 31 µm and 56 µm, respectively, at the edge of the hole.

Parametric Analysis of Surface Roughness and Metal Removal Rate during Electrical Discharge Machining of O1 Tool Steel

This work studies the impact of input machining parameters of Electrical Discharge Machining (EDM) on the machining process performance. Tool steel O1 was selected as the workpiece material, copper as the electrode material, and kerosene as the dielectric medium. Experimental runs have been carried out with a Design of Experiment (DOE) technique. Twenty tests are accomplished with the current range of (18 to 24 Ampere), a pulse duration range of (150 to 200 µs), and a pulse-off time range of (25 to 75 µs). Based upon the experimental study's output results, the EDM parameter's effect (voltage of power supply, discharge current, pulse duration, and pulse pause interval) on the responses of the process represented by surface roughness value Ra and Metal Removal MR rate. The results obtained by the DOE approach are analyzed by STATISTICA software. It has been concluded that an increase in the current and pulse duration maximizes both metal removal rate and surface roughness. At the same time, they are minimized by maximizing the pulse pause interval.

Process performance characteristics evaluation on the EDM of ASTM A36 steel

Providing product and quality features in Electric Discharge Machining (EDM) is a demanding process and can be done efficiently using appropriate machining parameters. It is therefore important to check the influence of machining parameters in the EDM. ASTM A36 steel was used as a working material to check the machining parameters in EDM using a copper electrode as a tool material. Peak current, pulse on duration (Ton) and pulse off duration (Toff) are the input machining parameters considered for this experimental work. The main objective here was to study the influence of the input machining parameters on process performance characteristics namely, the material removal rate (MRR) and surface roughness (Ra). The individual effect of peak current and Ton and the combined effect of both these machining parameters on MRR and Ra were studied through different graphs. Consequently, it was found that MRR and Ra were mainly influenced by peak current and Ton. Metallography analysis to examine the surface morphology of machined surface was disclosed with the aid of a scanning electron microscope (SEM).

Experimental Analysis of (triaxial /3D) Machining Cutting Forces and Surface Roughness in Turning Operation OF EN8 Steel

Abstract: The measurement of all machining forces and surface finish during the turning process. All types of machining process the cutting tool, workpiece of surfaces roughness, tool wear, quality, and accuracy of the part of machining the present study.to investigate the effect of machining input parameters (spindle speed, feed rate, depth of cut) on measuring the forces after machining (cutting force, feed force, thrust force) and surface roughness for turning operation. Using of workpieces is EN8 steel and a cutting tool is a carbide tool. experiment stabilized on the lathe machine. The force is measured by a triaxial piezoelectric sensors base dynamometer and data is transmitted by a data aquation system. this is controlled by LAB-VIEW software and stores the forces in the computer. there are 27 experiments, and one parameter is changed and two parameters are constant at an experiment. measured the forces and (Ra) is analyzed is by minitab18 software design a regression equation and (ANOVA). The minimum force and (Ra) the experimental (force &surface roughness is nearest to the predicted value

Evaluating CNC Milling Performance for Machining AISI 316 Stainless Steel with Carbide Cutting Tool Insert.

The present study investigates the CNC milling performance of the machining of AISI 316 stainless steel using a carbide cutting tool insert. Three critical machining parameters, namely cutting speed (v), feed rate (f) and depth of cut (d), each at three levels, are chosen as input machining parameters. The face-centred central composite design (FCCCD) of the experiment is based on response surface methodology (RSM), and machining performances are measured in terms of material removal rate (MRR) and surface roughness (SR). Analysis of variance, response graphs, and three-dimensional surface plots are used to analyse experimental results. Multi-response optimization using the data envelopment analysis based ranking (DEAR) approach is used to find the ideal configuration of the machining parameters for milling AISI 316 SS. The variables v = 220 m/min, f = 0.20 mm/rev and d = 1.2 mm were obtained as the optimal machine parameter setting. Study reveals that MRR is affected dominantly by d followed by v. For SR, f is the dominating factor followed by d. SR is found to be almost unaffected by v. Finally, it is important to state that this work made an attempt to successfully machine AISI 316 SS with a carbide cutting tool insert, to investigate the effect of important machining parameters on MRR and SR and also to optimize the multiple output response using DEAR method.

Prediction of cutting forces using MRA, GMDH and ANN techniques in micro end milling of titanium alloy

Micro End Milling is an specific procedure for manufacturing biomedical, automotive, naval, aerospace and mining parts especially for super alloys. Surface finish is the necessity output parameter after machining these alloys in the said applications. The study of cutting forces is mandatory to know the required surface finish to be generated on the work material. The influence of machining parameters can be investigated by experimental and predicted results that are very useful for the practitioner engineers inorder to evaluate. In this manuscript, experimental investigation of cutting forces was done and a comparison of the modelling methods - multiple regression analysis (MRA), Group method data handling (GMDH) and Artificial Neural Network (ANN) were presented. In the manuscript, the impact of input factors in machining of Ti-6Al-4 V (Grade-5) titanium alloy using uncoated tungsten carbide tools was studied on cutting forces. From the experimental interpretations, it is found that while increasing machining input parameters cutting forces on workpiece subsequently increases. After the adherences of experiments, experimental values are in good agreement with the accuracy of neural network prediction.

Experimental Investigations and Optimization of Machining Parameters in CNC Turning of SS304 Using Coolant at 0 °C

During the machining process, coolant is utilized to remove chips and tiny abrasive particles created during the machining process as well as to lessen heat concentration and friction between tools and chips. The machining performances, such as tool life, surface roughness, cutting forces, retention of mechanical properties of the work material, etc., are also desired to be retained or improved at the same time. This presented research work’s main goal is to investigate and analyze the impact of coolant at 0 °C on input machining parameters when turning SS304 (an austenitic stainless steel of the 300 series with high corrosion resistance) on a CNC lathe and to optimize the input variable factors, such as feed rate, cutting speed, and depth of cut for the best machining conditions, and each input cutting parameter is given a weight using the analytic hierarchy process (AHP) technique. A novel experimental setup is created to decrease the temperature of emulsion coolant and to use it in control conditions during machining operation. To research and assess the impact on the workpiece surface roughness, forces produced during actual cutting operations, the rate of tool wear, and the rate of material removal, twenty-seven sets of experiments using the partial factorial design approach are devised and carried out. Prioritizing the many optimal solutions accessible for this work is done using the technique for the order of preference by similarity to ideal solution (TOPSIS) and grey relation grade (GRG) approaches. Further, the surface finish of the workpiece after machining, rate of tool wear, cutting force generated during machining, and material removal rate from the workpiece were compared with traditionally/conventionally used input parameters with newly obtained optimized parameters through this work. Approximately a 30% improvement is observed in output parameters compared with using traditional parameters, and was close to the 50% of the result obtained through cryogenic machining. The work piece’s chip morphology along with tool wear was observed in form of SEM images, and it supports the claim of the surface finish and tool wear. The material removal rate was physically observed during machining. SEM pictures were used to physically validate the changes in tool wear. It has also been shown that keeping the coolant temperature at 0 °C significantly improves a number of work quality and machining characteristics. This method offers a substitute for cryogenic machining, making it useful for the manufacturing sectors.

MACHINE LEARNING-BASED MODELING AND OPTIMIZATION IN HARD TURNING OF AISI D6 STEEL WITH ADVANCED AlTiSiN-COATED CARBIDE INSERTS TO PREDICT SURFACE ROUGHNESS AND OTHER MACHINING CHARACTERISTICS

In recent times, mechanical and production industries are facing increasing challenges related to the shift towards sustainable manufacturing. In this work, the machining was performed in dry cutting condition with the newly developed AlTiSiN-coated carbide inserts coated through scalable pulsed power plasma technique, and a dataset was generated for different machining parameters and output responses. The machining parameters are speed, feed and depth of cut, while the output responses are surface roughness, cutting force, crater wear length, crater wear width and flank wear. Abrasion and adhesion were found to be the two dominant wear mechanisms. With speed, the tool wear was found to increase. Cutting force was found to increase rapidly for higher speed ranges (70, 80 and 90[Formula: see text]m/min). But reduction of cutting force was observed in both low and medium ranges of cutting speed (40, 50, 55 and 60[Formula: see text]m/min). With higher values of feed and depth of cut, higher cutting force was also noticed. With the variation of depth of cut, the machined surface morphology and machined surface roughness were found to deteriorate. With higher feed and depth of cut values, more surface damages were observed compared to low values of feed and depth of cut. Both the feed rate and tool–chip contact length contributed significantly to the formation of crater on the tool rake surface. At high speed, continuous chips were observed. Both chip sliding and sticking were observed on the tool rake face. The data collected from the machining operation was used for the development of machine learning (ML)-based surrogate models to test, evaluate and optimize various input machining parameters. Different ML approaches such as polynomial regression (PR), random forests (RF) regression, gradient boosted (GB) trees and adaptive boosting (AB)-based regression were used to model different output responses in the hard machining of AISI D6 steel. Out of the four ML methodologies, RF and PR performed better in comparison to the other two algorithms in terms of the [Formula: see text] values of predictions. The surrogate models for different output responses were used to prepare a complex objective function for the germinal center algorithm-based optimization of the machining parameters of the hard turning operation.

EXPERIMENTAL STUDY OF GLASS MACHINING PROCESS PARAMETERS IN ELECTROCHEMICAL SPARK MACHINING PROCESS

Advanced machining processes show a very crucial role in the machining of high-strength brittle materials. However, process efficiency and cost are still issues in currently used advanced machining processes. Quartz glass has broad application in the area of microfluidics systems, but the high brittleness of this material poses difficulty in its machining. In the present work, the machining of quartz glass is performed through Electrochemical spark machining processes (ECSM) which is one of the hybrid advanced machining processes. Input machining parameters are voltage, duty cycle under the constant condition of electrolyte concentration and tool electrode rotation. Input machining parameter effects are observed on the surface roughness of the machined quartz glass workpiece. Taguchi method is used to identify best optimal parameters using full factorial.