Optimum Value Of Surface Roughness Research Articles

Orthogonal turning of AISI 310S austenitic stainless steel under hybrid nanofluid-assisted MQL and a sustainability optimization using NSGA-II and TOPSIS

AISI 310S austenitic stainless steels are widely used in many industries, such as oil, gas, and petrochemical industries containing hot concentrated acids, due to their superior properties like corrosion resistance, high-temperature service, and creep resistance. However, a study on the machining of AISI 310 stainless steel has not been published in the scientific literature. It is known that the machinability of the austenitic stainless steel alloys, in which the AISI 310S alloy is included, is poor due to its low thermal conductivity, high work hardening, and built-up edge (BUE) formation. This situation causes sustainability problems by increasing the energy consumptions, the total machining cost and the amount of carbon emissions. The presented study investigated for the first time machining response and sustainability assessment in the orthogonal turning of AISI 310S stainless steel using three different cutting speeds and feed values, and five different cutting conditions such as dry, vegetable-based fluid minimum quantity lubrication (MQL), nano molybdenum disulfide (nMoS2) reinforced nanofluid MQL, nanographene (nGP) reinforced nanofluid MQL, and nMoS2/nGP reinforced hybrid nanofluid MQL, and a sustainability optimization was performed. Sustainability optimization was conducted to minimize the resultant force, surface roughness, carbon emission, and total machining cost using multi-objective optimization with NSGA-II (Non-dominated Sorting Genetic Algorithm II) and multi-criteria decision-making with TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution). The resultant force, surface roughness, total carbon emission, and total machining cost were reduced by a significant, 56.8%, 86.8%, 30.4%, and 2.9%, respectively, under the nGP reinforced nanofluid MQL cutting condition compared to the dry cutting condition. As a result of sustainability optimization, the use of high cutting speed (220 m/min), low feed values (between 0.11 mm/rev and 0.14 mm/rev), and nGP reinforced nanofluid MQL method (N2-MQL) in the turning of AISI 310S stainless steel material provides the optimum surface roughness values, cutting forces, carbon emissions, and total machining costs.

Application of Taguchi’s Orthogonal Array and S/N Ratio on Surface Roughness Optimization

Surface finish plays an important role in making quality products in the manufacturing sector. A turning experiment is conducted by cutting tool of nose radii (1 mm and 0.65 mm) to find its influence on surface roughness. Eight experiments were performed with Taguchi’s Orthogonal Array (OA) technique. The prime objective was to investigate the tool nose radius effects in the prediction of the optimum value of surface roughness. Cutting speed (V), feed (F), depth of cut (D), and tool nose radius (NR) were selected for the experimental work having two levels each. To find out the influential parameters analysis of variance (ANOVA) was carried out. Signal to noise ratio and average performance graph was analyzed to obtain optimum response values. The results concluded that the large nose radius develops a superior finish in comparison to the smaller one.

Effect of Cutting Parameters on Surface Roughness in Dry Machining of S45C Steel Using Carbide Tools

Wet machining is still carried out in the metal cutting industry today; machining experts continue to work to reduce the use of coolant so that benefits for the environment, economy and operator safety can be obtained. The research aims to obtain cutting conditions which have a good chance of realizing the concept of dry machining on S45C steel regarding surface roughness. The research was carried out using 9 specimens by statics method with a normal distribution curve where S45C steel turning using uncoated carbide chisels was carried out on dry machining and wet machining. Machine spindle rotation of 700 rpm, 1000 rpm and 1300 rpm can be converted in the amount of cutting speed, then for infeed 0.15 mm/r, 0.2 mm/r, 0.25 mm/r and the depth of cut remains 1 mm. Machining S45C steel is selected for optimum cutting conditions on wet and dry machining. Machined surface roughness was tested using the Surface Test measuring instrument. The surface roughness obtained for dry machining was 1.602 µm, 1.667 µm and 2.041 µm for dry machining while for wet machining the results were 1.521 µm, 1.593 µm respectively and 1.915 µm. So, the most optimum cutting is obtained in cutting conditions with a cutting speed of 87.92 m/min at 700 rpm machine spindle rotation with Ra = 1.602 µm for dry machining while wet machining obtains a surface roughness value of Ra = 1.521 µm on wet machining. Comparison of the results of dry machining (Ra= 1.602 µm) and wet machining (Ra= 1.521 µm) to obtain the optimum surface roughness value Ra is not significant, so it is a good opportunity for the possibility of a dry machining technology to be applied to the metal cutting industry even though today many still use the wet machining method of turning using a cutting fluid to cut metal. Keywords: S45C steel, cutting parameters, surface roughness, dry turning

Experimental study of surface roughness on improving the tribological performance of sealing pairs in magnetorheological damper

Comprehension of the tribological behavior of magnetorheological fluid (MRF) is crucial in many fields, particularly those related to seal wear. The sealing conditions are more complex than traditional media due to composition and rheological characteristics of MRF. The sealing pairs are prone to wear failure when in contact with magnetic particles. To this end, the paper focuses on the influence of surface roughness on MRF tribological properties with and without magnetic field. The whole experiment is carried out by a self-developed device to reproduce the wear evolution between the sealing pairs for magnetorheological damper. Experimental results show that higher surface roughness exhibits lower friction coefficient and better friction performance under MRF, especially in the absence of magnetic field. This is in stark contrast to the best wear resistance exhibited by polished samples under methyl silicone oil, a carrier fluid of MRF. This means that the effective coupling between surface roughness and iron particles helps to improve the friction and wear performance of sealing pairs. On this basis, the optimum surface roughness values of metal pin under MRF are obtained. Subsequently, the effect of pin surface morphology on the dynamics of iron particles are confirmed through the analysis of scanning electron microscopy images. It is further demonstrated that wear mechanism varies with surface roughness, that is, as the roughness increases, from sliding to mixing, and finally to rolling. Consequently, this research is of great significance for improving the wear resistance of sealing pairs under MRF.

Optimization Of Process Parameters In Turning 817M40T Steel At Minimum Quantity Lubrication (MQL) Condition

This research conducts optimization of cutting parameters on surface roughness during turning of 817M40T steel under dry and minimum quantity lubrication (MQL) conditions. The cutting parameters such as spindle speed, feed rate and depth of cut were the parameters taken as input variables for the investigation of the surface roughness quality. The experimental design adopted in this work was a three-factors-three-levels of L9 Orthogonal Array of Taguchi Robust Design Technique. Fifty four samples were experimented in a CNC Turning in dry and MQL environment and surface roughness measured using profilometer instrument. The optimal surface roughness value for MQL condition was found to be 1.9853μm. The result of ANOVA has the spindle speed contributing 71.5 %, followed by feed rate 14.10% and depth of cut has least effect of 7.80%. In dry condition, the result of ANOVA has spindle speed with 55.78% contribution followed by feed rate of 35.7% while depth of cut has least effect of 5.52%. In dry condition, the optimum value of surface roughness is 2.7096μm. MQL cutting condition was found to be better and more reliable because it is environmentally friendly and gives better surface finish. With the obtained optimum input parameters for surface roughness, production machining operations will be enhanced

Study of optimum welding performance in friction stir welding of dissimilar Mg alloys using integrated RSM-TLBO algorithm

Lightweight with excellent strength of magnesium alloys has attracted its use in transportation industries but difficulty in fusion welding of magnesium alloys restricts its application. The present research investigates solid state friction stir welding of dissimilar AZ31-AZ91 magnesium alloys with aim to achieve optimum quality welds. Surface roughness, microstructure and mechanical properties of these joints have been investigated at different tool rotational speed, welding speed and tool shoulder diameter. Maximum joint strength obtained is 89.71% (as compare to AZ31) which is more than the previously reported joint strengths of dissimilar magnesium alloys. Further, mathematical relations for responses have been developed and utilised for multi-objective optimization using teaching-learning-based optimization algorithm. Eventually, teaching-learning-based optimization algorithm results suggest that the optimum value of surface roughness (3.3925 µm), grain size (12.6869 µm), tensile strength (237.9621 MPa), microhardness (69.3652 Hv) and flexural strength (333.2285 MPa) can be achieved at 921 rpm rotational speed, 30 mm/min welding speed and 15 mm shoulder diameter with overall improvement in multiple responses.

Studying the Influence of Dressing Parameters on the Surface Roughness when Conducting the External Grinding of SKD11 Steel

This study presents the results of a study on multi-objective optimization of internal cylindrical grinding of hardened SKD11 steel in order to find one objective function which satisfy minimum surface roughness, Ra. By this procedure, an optimum set of dressing parameters such as coarse dressing depth, number of coarse dressing, fine dressing depth, number of fine dressing, non-feeding dressing, and dressing feed speed will be found. Taguchi design methodology is accepted to find the optimum set of dressing parameters which can lead a condition of objective function as above mentioned. ANOVA is conducted based on experimental results to find the significance of each input parameter on the responses. The results reveal that the optimum value of surface roughness is 0.111 μm when using the optimum dressing parameters such as fine dressing depth at level of 2, number of fine dressing at level of 3, number of non-feeding dressing at level of 4, number of coarse dressing at level of 3, coarse dressing depth at level of 2, and dressing feed rate at level of 1.

SURFACE ROUGHNESS ANALYSIS OF CRYO-TREATED AND TEMPERED STEELS IN CYLINDRICAL GRINDING OPERATION

Cylindrical grinding operation is an important metal cutting process used as a finish process to achieve the surface quality and dimensional stability of the products. In this context, experimental work and statistical analysis in researches contribute to improve product quality of manufactured parts. Tempered steels are widely used for automotive components and manufacturing applications. The objective of this study is to analyze the surface roughness (Ra) values of cryogenically (cryo) treated and tempered steels in cylindrical grinding operation. According to the grinding experiments created by the Taguchi method, grinding wheels (Al2O3 and SiC), heat treated steel samples (HT, CT24, and CT36) and depth of cut (50, 100, and 150[Formula: see text][Formula: see text]m) were selected to determine the optimum surface roughness values of these steels. The results showed that significant improvements in Ra values of cryo-treated and tempered steels were observed. The lowest Ra values were obtained in cryo-treated sample (CT36) with SiC grinding wheel and depth of cut (50[Formula: see text][Formula: see text]m).

Modeling of external cylindrical grinding process using T1 tool steel

The selection of the optimal external cylindrical grinding conditions importantly contributes to increase of productivity and quality of the products. The external cylindrical grinding is a method of finishing machine elements surface with an indeterminate blade shape. External cylindrical grinding can process surfaces that require high gloss and precision, although it can also be used to remove large surplus stock. Therefore, multi objective optimization for the external cylindrical grinding process is a problem with high complexity. In this study, an experimental study was performed to improve the productivity and quality of grinding process. By using the experimental date, the surface roughness, cutting force, and vibrations were modeled. To achieve the minimum value of surface roughness and maximum value of material removal rate, the optimal values of external cylindrical grinding conditions were determined by using the combination of Genetic Algorithms (GAs) and weighting method. The optimum values of surface roughness and material removal rate are 0.510 μm and 5.906 mm2/s, respectively. The obtained optimal values of cutting parameters were a feed rate of 0.3 mm/rev, a workpiece speed of 188.1 rpm, a cutting depth of 0.015 mm, and a workpiece Rockwell hardness of 54.78 HRC. The optimal values of cutting parameters, and workpiece hardness were successfully verified by comparing of experimental and predicted results. The approach method of this study can be applied in industrial machining to improve the productivity and quality of the products in external cylindrical grinding process of the T1 tool steel

Multi-objective optimization using the genetic algorithms for external cylindrical grinding process of 9CrSi alloy

In this study, by performing the experimental research, the surface roughness, cutting force and vibration were modeled. The Genetic Algorithms (GAs) were applied to determine the optimal values of external cylindrical grinding conditions to achieve the minimum value of surface roughness and the maximum value of the material removal rate. The optimum values of surface roughness and material removal rate are 0.490 [Formula: see text]m and 3.974 mm2/s, respectively, that were obtained at a feed rate of 0.3 m/min, at a workpiece speed of 164.82 rpm, at a cutting depth of 0.015 mm, and a workpiece Rockwell hardness of 56.32 HRC. The optimal values were successfully verified by experimental results with very promising results.

A Study on Influence of Milling Types and Cutting Conditions on Surface Roughness in Milling of Aluminum Alloy Al6061-T6

In this study, by using Taguchi method, with four controllable factors-three levels (milling type, axial depth of cut, feed rate, and spindle speed), the orthogonal array L27 was used to investigate the effects of milling type and cutting conditions on the surface roughness. By analysis of variance (ANOVA), the influences degree of milling type, axial cutting depth, feed rate, and spindle speed on the surface roughness were 9.26 %, 12.85 %, 12.69 %, and 63.08 %, respectively. The interaction factors of these factors that have a quite small influence on the surface roughness. The surface roughness was modeled as a quadratic regression with the confidence level is more than 99.82%. This model was successfully verified by comparison of experimental and predicted results. The optimization process of surface roughness was performed by both Taguchi method and the ANOVA analysis with the same results. The optimum value of surface roughness was 0.374 µm that was obtained in the half up milling, at a cutting depth of 0.4 mm, a feed rate of 480 mm/min, and a spindle speed of 5000 rpm.

Effect of magnetic abrasive machining process parameters on internal surface finish

The surface finish is an important characteristic of the machined surface which is influenced by wear, friction and rotation of the mechanical component. In this work, the effect of different range of process parameters like working gap, rotational speed and machining time on magnetic-abrasive machining process are analyzed. The finishing action is done on mild steel cylindrical work piece. Moreover, the optimization of the best parameter at a particular range of electromagnetic flux for surface roughness of mild steel tube is done by Taguchi method. For surface roughness, the working gap is significant, followed by machining time and rotational speed, and obtained the optimum value of surface roughness (Ra) is 1.489 µm at 35 mm working gap, 430 rpm rotational speed and 2 min. machining time.

Experimental Investigation of Surface Roughness of Cutting Parameters in T6 Aluminum Alloy Milling Process

In this study, optimum machining conditions were determined by investigating the surface roughness of the 7075-T6 aluminum alloy milling, depending on the spindle speed (rpm), feed per tooth (Fz-mm/tooth) and the cooling type parameters. Taguchi experiment design method was used to save time and cost. Experiments were based on the Taguchi L16 orthogonal array and signal/noise (S/N) ratios were used in the evaluation of the test results. Optimum surface roughness values were determined with Taguchi optimization. In addition, variance analysis and regression analysis were performed. Confirmation tests were conducted to verify the work. As a result of the confirmation tests, it was found that the surface roughness optimization in the milling of the 7075-T6 aluminium alloy was successfully applied.

Multiparametric Optimization of Low Plasticity Burnishing Process for AISI 4340 by using Utility Concept

Low plasticity burnishing (LPB®) is an innovative method which provides deep and stable compressive residual stresses. It improves surface characteristic such as low and high cycle fatigue strength, surface finish, microhardness, wear resistance, corrosion resistance, etc. The objective of the multi parametric process optimization for LPB is to find out appropriate levels of process variables i.e. ball diameter, pressure, speed, initial surface roughness and number of passes for achieving optimum values of surface roughness, hardness and fatigue life simultaneously. Considering multiple output responses in the present study, multi response optimization is essential. This paper deals with multi objective LPB process optimization problem of response characteristics with Utility concept for AISI 4340 steel alloy

Prediction and optimization of surface roughness in thermal drilling using integrated ANFIS and GA approach

In the recent decade, thermal drilling is becoming popular in aerospace and automobile industries because of its unique advantages over conventional twist drilling process. Surface finish of the thermally drilled hole along with its bushing, is a major concern in all crucial applications and it is worthy of investigation. In the present study, the surface roughness of the thermally drilled hole on galvanized steel is predicted and then optimization is carried out, employing an integrated adaptive network-based fuzzy inference system (ANFIS) and genetic algorithm (GA) approach. Experimentation is based on Taguchi L27 orthogonal array and significant parameters such as spindle speed, angle of tool and workpiece thickness are varied in different levels keeping feed rate as constant. Using the experimental results, an ANFIS model is developed for prediction of surface roughness. An objective function is then formulated on minimization of surface roughness with the help of predicted results of the ANFIS model. Then this objective function was imported into GA toolbox of MATLAB software to optimum values of surface roughness of thermally drilled hole. High degree of closeness is observed between the experimental and predicted results. It is also found that the spindle speed and angle of tool play a significant role on the surface roughness of drilled holes in galvanized steel.

Optimization Of Roller Burnishing Process Parameters On Surface Roughness Using Response Surface Methodology

Abstract The present study is to optimize the roller burnishing process parameters for improved surface quality. A Response Surface Methodology based on Central Composite Design is utilized for experiments. Four process parameters considered include feed, speed, number of passes and depth of penetration. The experiment was performed with work piece of EN19 alloy steel using new roller burnishing tool on a CNC lathe machine. A regression model has been developed to find out the optimum value of surface roughness. Using Response surface methodology concept to optimized the values of the burnishing process should be determined- feed 0.06 mm/rev, depth of penetration 0.17mm and burnishing speed 1196 rpm at single pass.

Optimization of Cutting Parameters in Finishing Milling of Hardox 400 Steel

In this study, it was performed to optimization of cutting parameters in finishing milling of Hardox 400 steel with PVD TiAlN+TiN coated carbide inserts. Milling experiments were made according to Taguchi L16 orthogonal array. The evaluation of the experimental results was based on the signal/noise (S/N) ratio. Control factors that given optimum surface roughness values were determined by using the Taguchi method. Two different cutting speeds (60 and 120 m/min) and cooling method (dry and wet) as control factors was selected. In addition, depth of cut and feed rate were taken as 0.3 mm and 0.1 mm/rev, respectively. The effect levels on the surface roughness of the control factors with analysis of variance (ANOVA) performed using the experimental results were determined. The Taguchi analysis found the optimum results for surface roughness to be with the cutting speed of 120 m/min and cooling method of wet.

Experimental investigation on surface integrity in machining of Inconel X750 with WEDM using Taguchi technique

The objective of the present work is to investigate the effects of various WEDM process parameters such as pulse on time, pulse off time, servo voltage, peak current, wire tension and wire feed rate on the surface roughness of the machined samples. The analysis of variance was carried out for raw data as well as for signal to noise ratio. Four input parameters have been found to be statistically significant for their effects on the response of interest. The optimum value of surface roughness was obtained at pulse on time - 110; pulse off time - 51; spark gap voltage - 65 V; peak current - 90 A; wire feed - 11; wire tension - 5. The confirmation experiments were also performed for validating the predicted results. Scanning electron microscopy was performed on the machined samples to investigate the effect and microstructure of the samples after machining.

Modeling and experimental investigation of process parameters in WEDM for surface roughness using regression model

The present study focused on the multiple regression modeling and predicting the surface roughness of the Aluminum hybrid composite during the WEDM process. The hybrid MMC was manufactured by process named as stir casting utilizing particulates of Silicon carbide and graphite each in Al6061 combination. The analyses were outlined with Taguchi L27 design matrix. Mathematical relationships between the surface roughness and WEDM cutting parameters (Pulse on time, Pulse off time, current, gap voltage, wire speed and wire tension) have been investigated. The results show that the multiple regression analysis is a successful method for developing a mathematical model to predict the surface roughness. The optimum value of process parameters for the predicted optimum value of surface roughness (1.285) is pulse on time 106 units (Level 1), pulse off time 60 units (Level 3), peak current 90 units (Level 2), gap set voltage 50 units (Level 3), wire speed3 units (Level 1) and wire tension 12 units (Level 3).The optimum results are adopted in validation study and the results based on WEDM process responses can be effectively improved.

Experimental study on different adherend surface roughness on the adhesive bond strength

In this study, an investigation on adherend surface roughness on adhesive bond strength was performed. The adherend materials used were mild steel and aluminium AA6063, whereas the adhesive used was an epoxy resin (Araldite® 2015). Surface of adherend materials were treated by the mechanical abrasion process using an emery paper of different grade to get different level of roughness. Single strap joints were tested at room temperature under tensile loading in Universal Testing machine. Results showed that optimum surface roughness values were found in the different range with respect to the adhered material joints.