Showing 7 results for Surface Roughness
Saadat Ali Rizvi, Ali Wajahat ,
Volume 30, Issue 3 (9-2019)
Abstract
CNC turning is widely used as a manufacturing process through which unwanted material is removed to get the high degree of surface rough. In this research article, Taguchi technique was coupled with grey relation analysis (GRA) to optimize the turning parameters for simultaneous improvement of productivity, average surface roughness (Ra), and root mean square roughness (Rq).Taguchi technique L27 (34) orthogonal array was used in this experimental work. Feed, speed, and depth of cut were considered as the controllable process parameters. average roughness (Ra), root mean square roughness (Rq),and material removal rate (MRR) were considered as the performance characteristic and from TGRA result, it was revealed that the optimum combinational parameters for multi-performance, based on mean response values and confirmation experiments with Taguchi-based GRA is A1B1C1 (Vc=400 rpm, f=0.06 mm/rev, and DOC=0.5 mm). The optimum values obtained from experimental investigations for Ra was 6.86 μm, and MRR was 20690.31 mm3/s,further analysis of variance(ANOVA) were applied and it was identified that the depth of cut having most significant effect followed by speed and feed for multiresponse optimization. The percentage contribution of depth of cut was 38.28.71 %, speed was 11.89 % and feed was 8.466 %.
CNC turning is widely used as a manufacturing process through which unwanted material is removed to get a high degree of surface roughness. In this research article, Taguchi technique was coupled with grey relation analysis (GRA) to optimize the turning parameters for simultaneous improvement of productivity, the average surface roughness (Ra), and root means square roughness (Rq). Taguchi technique L27 (34) orthogonal array was used in this experimental work. Feed, speed, and depth of cut were considered as the controllable process parameters. average roughness (Ra), root mean square roughness (Rq), and material removal rate (MRR) were considered as the performance characteristic and from TGRA result, it was revealed that the optimum combinational parameters for multi-performance, based on mean response values and confirmation experiments with Taguchi-based GRA is A1B1C1 (Vc=400 rpm, f=0.06 mm/rev, and DOC=0.5 mm). The optimum values obtained from experimental investigations for Ra was 6.86 μm, and MRR was 20690.31 mm3/s, further analysis of variance(ANOVA) were applied and it was identified that the depth of cut having most significant effect followed by speed and feed for multiresponse optimization. The percentage contribution of the depth of cut was 38.28.71 %, speed was 11.89 % and feed was 8.466 %.
Reza Rostami Heshmatabad, Mohammadreza Shabgard,
Volume 31, Issue 3 (9-2020)
Abstract
In this study, the electrochemical machining (ECM) of the 304 stainless steel with the response surface methodology (RSM) approach for designing, analyzing and mathematical modeling was used. The electrolyte type, concentration and current parameters were considered as the machining parameters. The mathematical model for the responses was presented and based on the type of electrolyte including NaCl, NaNO3 and KCl. The results showed that the current has the highest effect on Surface Roughness (SR) and Material Removal Rates (MRR) and respectively it improves them to 0.465μm and 0.425gr/min. The electrolyte concentration has the highest effect on Over Cut (OC) and causes to increase its values. Under the conditions of NaCl electrolyte, 1 molarity concentration and 55 A current, the optimum condition 0.4006 gr/min MRR, 0.75 mm OC and 0.465mm SR was achieved.
Bhanudas Bachchhav,
Volume 32, Issue 1 (1-2021)
Abstract
The present work aims to investigate Abrasive Water Jet Machining parameters for machining of Al-Al2O3 Metal Matrix Composite. Plan of experiments, based on Taguchi’s analysis technique were performed using L9 orthogonal array. A correlation was established between concentration of Al2O3, Stand-off distance, pressure and Transverse feed with Metal Removal Rate, Surface Roughness, Over-cut and Taper angle by regression analysis. On the basis of experimental results and S/N ratio analysis, ranking of the parameters has been done. The analysis of variance (ANOVA) has been used to find out the impact of individual parameters on response parameters. Al2O3 concentration plays a very significant role in determination of MRR and surface roughness. Also overcut is largely influenced by stand off distance. Furthermore, multi-objective optimization can be carried out using advanced optimization techniques. This work helped to generate technical database for industrial applications of MMC.
Marwa El-Mahalawy, M. Samuel, N. Fouda, Sara El-Bahloul,
Volume 32, Issue 2 (6-2021)
Abstract
Abstract: Wire Electrical Discharge Machining (WEDM) is a non-traditional thermal machining process used to manufacture irregularly profiled parts. Machining of ductile cast iron (ASTM A536) under several machining factors, which affect the WEDM process, is presented. The considered machining factors are pulse on time (Ton), pulse off (Toff), peak current (Ip), voltage (V), and wire speed (S). To optimize the machining factors, their setting is performed via an experimental design using the Taguchi method. The optimization objective is to achieve maximum Material Removal Rate (MRR) and minimum Surface Roughness (SR). Additionally, the analysis of variance (ANOVA) is used to identify the most significant factor. Also, a regression analysis is carried out to forecast the MRR and SR dependent on defined machining factors. Depending on consequences, the best regulation factors for reaching the maximum MRR are Ton = 32 μs, Toff = 8 μs, Ip = 4 A, S = 40 mm/min. and V = 70 volt. Whereas, the optimal control factors that achieve the minimum SR is Ton = 8 μs, Toff = 8 μs, Ip = 2 A, S = 20 mm/min, and V= 30 volt. It is hypothesized that the perfect combination of control factors that achieves minimum SR and maximum MRR is Ton = 8 μs, Toff = 8 μs, Ip=5 A, S=50 mm/min. The microstructure of the machined surface in the optimal machining conditions shows a very narrow recast layer at the top of the machined surface.
Saadat Ali Rizvi, Wajahat Ali,
Volume 32, Issue 3 (9-2021)
Abstract
The present study is focused to investigate the effect of the various machining input parameters such as cutting speed (vc), feed rate (f), depth of cut, and nose radius (r) on output i.e. surface roughness (Ra and Rq) and metal removal rate (MRR) of the C40 steel by application of an artificial neural network (ANN) method. ANN is a soft computing tool, widely used to predict, optimize the process parameters. In the ANN tool, with the help of MATLAB, the training of the neural networks has been done to gain the optimum solution. A model was established between the computer numerical control (CNC) turning parameters and experimentally obtained data using ANN and it was observed from the result that the predicted data and measured data are moderately closer, which reveals that the developed model can be successfully applied to predict the surface roughness and material removal rate (MRR) in the turning operation of a C40 steel bar and it was also observed that lower the value of surface roughness (Ra and Rq) is achieved at the cutting speed of 800 rpm with a feed rate of 0.1 mm/rev, a depth of cut of 2 mm and a nose radius of 0.4 mm.
M Kaladhar, Vss Sameer Chakravarthy, Psr Chowdary,
Volume 32, Issue 3 (9-2021)
Abstract
Surface quality is a technical prerequisite in the field of manufacturing industries and can be treated as a quality index for machined parts. Attainment of appropriate surface finish plays a key role during functional performance of machined part. It is typically influenced by the machining parameters. Consequently, enumerating the good relation between surface roughness (Ra) and machining parameters is a highly focused task. In the current work, response surface methodology (RSM) based regression models and flower pollination algorithm (FPA) based sparse data model were developed to predict the minimum value of surface roughness in hard turning of AISI 4340 steel (35 HRC) using a single nanolayer of TiSiN-TiAlN PVD-coated cutting insert. The results obtained from this approach had good harmony with experimental results, as the standard deviation of the estimated values was simply 0.0804 (for whole) and 0.0289 (for below 1 µm Ra). When compared with RSM models, the proposed FPA based model showed the least percentage of mean absolute error. The model obtained the strongest correlation coefficient value of 99.75% among the other models values. The behavior of machining parameters and its interaction against surface roughness in the developed models were discussed with Pareto chart. It was observed that the feed rate was highly significant parameter in swaying machining surface roughness. In inference, the FPA sparse data model is a better choice over the RSM based regression models for prognosis of surface roughness in hard turning of AISI 4340 steel (35 HRC). The model developed using FPA based sparse data for surface roughness during hard turning operation in the current work is not reported to the best of author’s knowledge. This model disclosed a more dependable estimation over the multiple regression models.
Tuan Ngo, Bao Ngoc Tran, Minh Duc Tran, the Long Tran, Trang Dang,
Volume 35, Issue 4 (12-2024)
Abstract
Improving hard machining efficiency is a growing concern in industrial production, but environmentally friendly characteristics are guaranteed. Nanofluid minimum quantity lubrication (NF MQL) has emerged as a promising solution to improve cooling and lubrication performance in the cutting zone. This paper utilizes Box-Behnken experimental design to identify the influences of Al2O3/MoS2 hybrid nanofluid MQL hard turning using CBN inserts on surface roughness and cutting forces. Mathematical models were employed to predict thrust cutting force, tangential cutting force, and surface roughness in hard turning under MQL conditions using Al2O3/MoS2 hybrid nanofluid. The study results reveal that the minimum thrust force (Fy) occurs at a nanoparticle concentration of 0.5%, air pressure of 5 bar, and flow rate of 236 l/min. In comparison, the tangential force (Fz) reaches its minimum at a nanoparticle concentration of 0.8%, air pressure of 5 bar, and airflow rate of 227 l/min. The minimum surface roughness was achieved with a nanoparticle concentration of 1%, air pressure of 4.7 bars, and airflow rate of 186 l/min. Additionally, based on the multi-objective optimization, an optimal parameter set of NC=1%, p=5 bar, and Q = 210 l/min was identified to bring out the minimal values of surface roughness (Ra) of 0.218 µm, thrust force (Fy) of 115.9 N, and tangential force (Fz) of 93.3 N.
