Machining Parameters For Surface Roughness Research Articles

Experimental and statistical investigation of electro-erosion machining performance of cryogenic treated hardened AISI H13 hot work tool steel

This study investigated the machinability of AISI H13 hot work tool steel with a hardness of 54 HRC, which has been deep cryogenically treated using the electro-erosion machining technique, one of the unconventional manufacturing methods. The machinability parameters used in the study were determined as current (4, 8 and 12 A), pulse time (200, 300 and 400 µs) and three different electrodes (Copper, Graphite and Tuncop). The experimental design was designed according to the Taguchi L27 model, and machinability tests were carried out to examine the results obtained from the experimental study both experimentally and statistically. When the literature was examined, it was seen that cryogenic treatment was generally applied to electrode materials, and only surface roughness and material wear amounts were examined as output parameters. The original value of the study is the interpretation of the results by applying cryogenic treatment to the test material and exploring the surface roughness and material wear amount, as well as the hole diameters and criterion diameters formed on the treated surfaces after the EDM process. As a result of the study, it was observed that the surface roughness value, the hole diameter, the amount of material wear, and the crater diameter increased with the increase in ampere and impact time. The lowest surface roughness value was 2.266 µm in the graphite electrode material at 4 amps and 200 µs impact time and the highest wear amount was realized as 0.555 g in the copper electrode material 12 amps and 400 µs impact time. The smallest hole diameter was 12.254 mm with a tuncop electrode with a pulse duration of 4 amps and 200 µs, and the lowest crater diameter was 127.301 µm with a graphite electrode at a current of 4 amps and a pulse duration of 200 µs. When the signal-to-noise ratios were examined, the optimum machining parameters for surface roughness, hole diameter, material wear amount, and crater diameter were determined as A2B1C1, A1B3C3, A3B1C1, and A3B1C1, respectively. When the ANOVA results are examined, it is determined that the most effective parameter for the surface roughness is of the current value with 86.54%, the most effective parameter for the hole diameter is the electrode material with 85.20%, the most effective parameter for material wear amount is the electrode material with 45.67% and the most effective parameter for the crater diameter is current value with 75.35%.

Optimization of machining parameters for surface roughness during abrasive water jet machining of aluminium/magnesium hybrid metal matrix composites

Metal Matrix Composites (MMCs) play a very important role in the production of any new components in industrial applications. It is regularly used in many applications, such as automotive, agriculture, medical, electrical and electronic fields, due to its exceptional characteristics. Due to its various elements are using to develop composites high hardness will increase, it is difficult to machine by the conventional process. Aluminum metal matrix composite is a modern type of material which focuses not only on enhancing mechanical properties but also on the machinability of materials that are challenging to process using abrasive water jet machining. Stir casting technique is chosen for fabricating the aluminum metal matrix composites. Experiments are carried out by choosing the usual parameters of the system such as a stand of distance, feed rate, and abrasive flow rate. The Taguchi-based Analysis of variance (ANOVA) method was introduced to optimize the AWJM (Abrasive Water Jet Machining) process parameters to reduce surface roughness (SR). The significance of process parameters was obtained through variance analysis based on L9 orthogonal array, which showed the most significant parameters being feed rate and abrasive flow rate.

The Influence of Circumferential Waviness of the Diamond Wheel Working Surface on the Machined Surface Roughness

The authors investigate the potential of damping down the undulating change of form of a diamond wheel working surface by dressing it with a single-point diamond tool. This is shown to alter the machined surface roughness parameters, namely: the height parameters decrease, the spacing of the profile irregularities and the parameters of the reference length of the profile grow.

Taguchi S/N based optimization of machining parameters for surface roughness, tool wear and material removal rate in hard turning under MQL cutting condition

Minimum quantity lubrication (MQL) is adorned with improved machining performance and environmental sustainability. In this context, this paper presents the study of roughness parameters (Ra, Rq, Rz), tool wear parameters (VB, VS) and material removal rate (MRR) in MQL-assisted hard turning by using coated cemented carbide tool. As methodology, the Taguchi orthogonal array-based design of experiment and signal-to-noise ratio-based optimization have been utilized. Prior to this, the collected machining data have been tested to check the normal probability distribution. Furthermore, the analysis of variance determined the influences of cutting speed, feed rate and depth of cut on the aforementioned responses. In addition, different types of tool wear, prevailed in principal and auxiliary flank faces, have been identified. The quantitative analysis revealed that the cutting speed impacted the surface roughness; the depth of cut influenced the tool wear; the feed rate afflicted the material removal rate predominantly. Based on the imagery evidence the adhesion, abrasion, and built-up-edge were found as the governing wear mechanism.

Surface Roughness of Optoelectronic Components in Mechanical Polishing

The investigation of the mechanism of mechanical polishing of optoelectronic components made of crystalline materials has demonstrated that the machined surface roughness parameters grow linearly with increasing most probable values of debris particle size and surface area, unit cell volume, and surface area of the crystal plane machined. The surface roughness parameters are shown to be inversely proportional to the energy spent for the debris particle formation. The relative surface roughness of the polished silicon carbide, gallium nitride, aluminum nitride, and sapphire workpieces is represented by the following ratio: 0.68: 0.67: 0.63: 1.00.

Optimization of MRR and Surface Roughness for 7024 AL-alloy in EDM Process

Electro discharge machining is major non-traditional operations for cutting the materials due to its suitability and benefits. The experimental work of this paper deals with electrico discharge machining (EDM). A system for machining in this process has been developed. Many parameters are studied such as current, time on and time off. Different current rates are used ranging from (30, 36 and 42) Amp, found that low current gives less material removal rates and good surface roughness. The results showing that maximum MRR is achieved (0.525) mm3/min when machining current (42), time on (150), and time off (50) while good surface roughness (2.11 μm) when machining current (30), time on (50), and time off (25).The level of importance of the machining parameters for surface roughness and material removal rate is determined by using Taguchi design experiments and analysis of variance (ANOVA).

Process modeling study of the ultrasonic elliptical vibration cutting of Inconel 718

As an advanced cutting method, ultrasonic elliptical vibration cutting (UEVC) has been successfully applied to machine difficult-to-cut materials for the last decade. However, few researches have been studied on cutting of Inconel 718 using a single-point diamond tool applying the UEVC method. The objective of this paper was to experimentally derive empirical equations for the prediction of multiple machined surface roughness parameters for UEVC machined Inconel 718 workpiece by means of a systematic statistical experimental approach. A Taguchi parametric screening design of experiments (DOE) was carried out to systematically determine the UEVC process parameters with the largest effect on mean surface roughness. Next, empirically determined equations for the four common surface roughness metrics (Ra, Rq, Rc, and Rz) were developed via Box-Behnken surface response tests. Validation tests were conducted resulting in predicted and experimental surface roughness in varying levels of agreement. The ultrasonic effect on surface quality was also discussed to confirm feasibility of UEVC of Inconel 718 with diamond cutting tools.

On-line Monitoring and Parameter Control of a Surface Microprofile

The article presents an analytical review of methods for cutting process on-line monitoring that are applicable for enhancing the performance of technological production systems and experimental verification of some findings of the analytical research. The interrelation between the thermophysical properties of the “tool-workpiece” contact pair and the technological processing conditions along with the cutting operation outcome has been considered. The degree of influence certain factors and the cutting conditions have on the machined surface roughness parameters and the cutting forces components have been analyzed. The analysis results showed the possibility of on-line estimating of the machined surface microrelief of with the application of dynamometric methods for assessing the cutting force vibration in order to increase the assurance reliability of the required quality parameters. There are mathematical models that enable us to reliably predict the values of the surface average roughness and the cutting forces radial and axial components.

Comparative assessment of two ceramic cutting tools on surface roughness in hard turning of AISI H11 steel: including 2D and 3D surface topography

This paper presents a comparison of surface roughness between both ceramic cutting tools namely, TiN coated mixed ceramic CC6050 and uncoated mixed ceramic CC650 when machining hardened hot work steel X38CrMoV5-1 [AISI H11] treated at 50 HRC. A mathematical model, relating surface roughness criteria and main factors such as cutting radius, cutting speed, feed rate, and depth of cut, was developed using response surface methodology (RSM) and its adequacy was checked by regression analysis. The effect of cutting parameters on surface roughness is evaluated and the optimum cutting conditions to minimize the surface roughness are determined. A multiple linear models have been established between the cutting parameters and the surface roughness using response surface methodology. The experimental results reveal that the most significant machining parameter for surface roughness is the feed followed by cutting radius. Also the determined optimal conditions really reduce the surface roughness on the machining of AISI H11 steels within the ranges of parameters studied. In addition, excellent surface roughness was obtained in hard turning using CC650 tools. The coated ceramic tools had no advantage over CC650 from the point of view of surface roughness.

Experimental Investigation and Prediction of Flatness and Surface Roughness During Face Milling Operation of WCB Material

In this work, effect of machining parameters spindle speed, feed and depth of cut were investigated during Face Milling of Wrought Cast Steed grade B (WCB). WCB is widely used in manufacturing valves due to its less cost. 23 full factorial design with four centre points is selected to perform the reliable experiments. Here the response parameters selected are surface roughness and flatness, a form control of Geometric Dimensioning & Tolerancing (GD&T). The values of flatness and surface roughness affect a lot during leakage testing of dual plate check valve. To achieve the desire value of flatness and surface roughness machining parameters need to be controlled. The right selection of process parameters can be achieved through a predictive model. ANOVA has been carried out to know the significance of input parameters. The values predicted from the model and experimental values are very close to each other.

Surface roughness prediction of particulate composites using artificial neural networks in turning operation

Article history: Received December 10, 2014 Received in revised format: March 2, 2015 Accepted March 2, 2015 Available online March 2 2015 A number of factors, e.g. cutting speed and feed rate, affect the surface roughness in machining process. In this paper, an Artificial Neural Network model was used to forecast surface roughness with related inputs, including cutting speed and feed rate. The output of the ANN model input parameters related to the machined surface roughness parameters. In this research, twelve samples of experimental data were used to train the network. Moreover, four other experimental tests were implemented to test the network. The study concludes that ANN was a reliable and accurate method for predicting machining parameters in CNC turning operation of Particulate Reinforced Aluminum Matrix Composites (PAMCs) specimens with 0%, 5%, 10% and 15% filler. The aim of this work is to decrease the production cost and consequently increase the production rate of these materials for industry without any trial and error method procedure. Growing Science Ltd. All rights reserved. 5 © 201

Shaping the cutting ability of grinding wheels with zone-diversified structure

This article presents the results of experimental tests aimed at determining the influence of shaping the microgeometry of particular functional zones (rough and finish grinding) of the grinding wheel active surface in the dressing cut and what effect this would have on the results of the internal cylindrical grinding process of steel 100Cr6. Single-pass grinding processes and the grinding wheel used in them are described. Grinding wheels with zone-diversified structure, made from abrasive grains along the basis of Al2O3 (white fused alumina 99A and microcrystalline sintered corundum), are characterized, and the construction of a special device for precise shaping of the conic chamfer on the grinding wheel active surface is presented. The experimental test results form the basis for determining the influence of the chief dressing parameters (grinding wheel peripheral speed vsd, dressing axial feed rate vfd) on the selected machined surface roughness parameters ( Ra, Rz, RSm, Rdq), surface bearing ratio and grinding power P in the internal cylindrical traverse grinding process. It was proven that the most advantageous grinding results are obtained when using different dressing parameters for the conic rough grinding zone and the cylindrical finish grinding zone of the grinding wheel. When shaping the grinding wheel conic chamfer, increased values of the dressing feed rate vfd( cone) need to be used in order to reduce the grinding power. The most advantageous parameter values for describing the machined surface roughness were obtained with low values of axial table dressing feed rate vfd( cylinder), providing precise alignment and sharpening of the grinding wheel active surface cylindrical zone.

Wear phenomena of grinding wheels with sol–gel alumina abrasive grains and glass–ceramic vitrified bond during internal cylindrical traverse grinding of 100Cr6 steel

This article presents the results from a traverse internal cylindrical grinding process conducted using grinding wheels with sol–gel alumina abrasive grains and varying volumes of glass–ceramic vitrified bond. The aim was to determine how changing the bond volume within the range of V b = 11.5–14.5 % influenced the wear phenomenon of the microcrystalline sintered corundum abrasive grains and the ceramic bond bridges with glass-crystalline structure. The paper presents grinding wheels with conic chamfer and the most important wear phenomena of the grinding wheel components occurring in the area of contact between the tool and the workpiece material. The work includes the methodology of the experimental tests on the traverse internal cylindrical grinding of bearing rings made from steel 100Cr6 (62 ± 2 HRC). A wide range of test results were analyzed, including machined surface roughness parameters (Ra, Rz, Sm, Δa), grinding power, grinding wheel volumetric wear V s , material removal V w , G-ratio, root-mean–square roundness deviation from mean circle (Δ, rms), grinding wheel edge wear, grinding wheel microtopography parameters (Sa, St, Sdr, Sds), and SEM images of the wear marks on the grinding wheel active surface. The dominant wear phenomena of the grinding wheels with conic chamfer and the influence of the bond volume share on its intensity were experimentally determined. The results show that the decrease in bond volume from V b = 14.5 to 11.5 % increases the grinding wheel life by twofold. This was due to a decrease in abrasive wear and plastic flow phenomena and to a greater fracture wear (mostly fatigue and thermal-fatigue). These events led to a periodic shedding of the oxide layer and the plastically deformed grain layer and also revealed the crystals’ sharp edges, located below the plastically deformed surface layer.

Multi-Objective Optimization of CNC Turning Machining Parameters

An experimental investigation into CNC turning operation on Brass C36000 alloy as work piece material which is widely used for various industrial applications is performed. Multi objective optimization is carried out to find out the influencing machining parameters among spindle speed (rpm), feed (mm per revolution) and depth of cut (mm) for CNC turning of Brass C36000 alloy with surface finish and Material Removal Rate as performance parameters using Taguchi method. Taguchi orthogonal array [L27 (33)] is used for the experimental design. All experiments are conducted using EMCO Concept Turn 250 machine tool with carbide insert cutting tool. The optimization result shows that feed is the most significant turning machining parameter for surface roughness while depth of cut has high influence on material removal rate followed by spindle speed during CNC turning of Brass C36000 alloy. Above results is further validated using ANOVA approach.

Fuzzy rule-based modeling of machining parameters for surface roughness in turning carbon particle-reinforced polyamide

Carbon particle-reinforced polyamides (PAs) are finding increasing applications in many engineering fields. Machining of these materials is needed for obtaining near net shape and precision fit. In this work, application of fuzzy logic technique along with the Taguchi’s orthogonal array is used in turning of PA6 to develop a model for predicting surface roughness. There are many factors affecting the surface roughness. Among these factors the machining parameters that play a dominant role are cutting speed, feed, and depth of cut. The adequacy of the fuzzy rule-based model is verified through coefficient of determination. The correlation that exists between the experimental value and the fuzzy model is on the higher side; hence, the fuzzy logic technique can be effective when estimating the surface roughness in turning of PA6.

Experimental Investigation and Optimization of Cutting Parameters in Machining of Ti6Al4V Alloy by an MT-CVD Insert

Titanium alloys find wide applications in aerospace, defence, and surgical industries due to their unique high strength- to- weight ratio which is maintained even at high temperatures and also their exceptional corrosion resistance. The machining of these materials is a thermally dominant process. At high temperature, they become chemically active and tend to react with tool materials. In the present study an attempt has been made to investigate the effect of cutting parameters (cutting speed, feed rate and depth of cut) on surface roughness in turning a Ti6Al4V Titanium alloy with a medium temperature chemical vapor deposition tool insert. Experiments have been conducted using Taguchi’s experimental design technique. Optimum cutting parameters for minimizing surface roughness have been determined by using the desirability function based approach. A second order model has been established between the cutting parameters and the surface roughness, using the response surface methodology. Predicted values and measured values fairly tally and it indicates that the developed model can be effectively used to predict the surface roughness in the machining of Ti6Al4V Titanium alloy. Experimental results reveal that among the cutting parameters, the feed rate is the most significant machining parameter for surface roughness, followed by depth of cut and cutting speed.

Modeling and analysis of machining parameters for surface roughness in powder mixed EDM using RSM approach

Electrical discharge machining (EDM) is one of the most successful and widely accepted non-conventional machining processes for production of complex shapes with high accuracy irrespective to its hardness by removing material through electrically discharges in the presence of dielectric fluid. The paper presents the state of the art technology of high-performance machining of advanced materials i.e. Powder mixed EDM. In this article, parametric optimization of Surface Roughness (SR) study on the powder mixed electrical discharge machining (PMEDM) of H11 steel has been carried out. Response surface methodology (RSM) has been used for developing, improving and optimizing the experiments. Central composite experimental design was used to estimate the model coefficients of the process parameters. Pulse time on, discharge current and concentration of Aluminum powder added into dielectric fluid of EDM were chosen as process parameters to study the PMEDM performance. Experiments have been performed on newly designed experimental setup and the analysis of variance revealed that the factors peak current and concentration are most influential parameters affecting SR. Keywords: Powder EDM, H11 steel, Modeling, Surface roughness, Response Surface Methodology (RSM).

Application of Taguchi and response surface methodologies for surface roughness in machining glass fiber reinforced plastics by PCD tooling

This paper discusses the use of Taguchi and response surface methodologies for minimizing the surface roughness in machining glass fiber reinforced (GFRP) plastics with a polycrystalline diamond (PCD) tool. The experiments have been conducted using Taguchi’s experimental design technique. The cutting parameters used are cutting speed, feed and depth of cut. The effect of cutting parameters on surface roughness is evaluated and the optimum cutting condition for minimizing the surface roughness is determined. A second-order model has been established between the cutting parameters and surface roughness using response surface methodology. The experimental results reveal that the most significant machining parameter for surface roughness is feed followed by cutting speed. The predicted values and measured values are fairly close, which indicates that the developed model can be effectively used to predict the surface roughness in the machining of GFRP composites. The predicted values are confirmed by using validation experiments.