Study on surface roughness measurement for turning of Al 7075/10/SiCp and Al 7075 hybrid composites by using response surface methodology (RSM) and artificial neural networking (ANN)

Measurement ◽  
2015 ◽  
Vol 65 ◽  
pp. 166-180 ◽  
Author(s):  
Ravinder Kumar ◽  
Santram Chauhan
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Hybrid Composites ◽  
Roughness Measurement ◽  
Surface Roughness Measurement ◽  
Artificial Neural Networking ◽  
Al 7075 ◽  
Artificial Neural

scholarly journals Investigation on Ti-6Al-4V Alloy Surface Characteristics Using Nano Grinding Wheel by Response Surface Methodology (RSM) with Artificial Neural Networking (ANN)

2020 ◽  
Author(s):  
Deborah Serenade Stephen ◽  
Sethuramalingam Prabhu
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Removal Rate ◽  
Research Work ◽  
Surface Characteristics ◽  
Grinding Wheel ◽  
Ti Alloy ◽  
Roughness Prediction ◽  
Artificial Neural

Abstract In this research work, surface characteristics of Ti-6Al-4V alloy have been investigated and the grinding process has been optimized using nano grinding wheel. Experiments have been conducted using L27 full factorial design. Surface roughness prediction model in nano grinding wheel for Ti alloy was developed using Response Surface Methodology (RSM) and compared with the model using Artificial Neural Network (ANN) methodology to predict the experimental behavior of the system. Grinding wheels with and without 3% nano Al2O3 powders were fabricated and their surface characteristics like surface roughness, material removal rate (MRR) and temperature were measured. Grinding was carried out on grade 5 Ti alloy with different wheels by varying input parameters. On comparing experimental and predicted results, it was found that the empirical values of surface roughness were close to the predicted values by 5%.

scholarly journals Modeling, Optimization and Performance Evaluation of TiC/Graphite Reinforced Al 7075 Hybrid Composites Using Response Surface Methodology

Materials ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4703
Author(s):  
Mohammad Azad Alam ◽  
Hamdan H. Ya ◽  
Mohammad Yusuf ◽  
Ramaneish Sivraj ◽  
Othman B. Mamat ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Hybrid Composites ◽  
Compaction Pressure ◽  
Physical And Mechanical Properties ◽  
Microstructural Characterization ◽  
X Ray ◽  
Weight Percentage ◽  
Al 7075 ◽  
Independent Parameters

The tenacious thirst for fuel-saving and desirable physical and mechanical properties of the materials have compelled researchers to focus on a new generation of aluminum hybrid composites for automotive and aircraft applications. This work investigates the microhardness behavior and microstructural characterization of aluminum alloy (Al 7075)-titanium carbide (TiC)-graphite (Gr) hybrid composites. The hybrid composites were prepared via the powder metallurgy technique with the amounts of TiC (0, 3, 5, and 7 wt.%), reinforced to Al 7075 + 1 wt.% Gr. The microstructural characteristics were investigated by optical microscopy, scanning electron microscopy (SEM), X-ray diffraction (XRD) and energy dispersive X-ray spectroscopy (EDS) elemental mapping. A Box Behnken design (BBD) response surface methodology (RSM) approach was utilized for modeling and optimization of density and microhardness independent parameters and to develop an empirical model of density and microhardness in terms of process variables. Effects of independent parameters on the responses have been evaluated by analysis of variance (ANOVA). The density and microhardness of the Al 7075-TiC-Gr hybrid composites are found to be increased by increasing the weight percentage of TiC particles. The optimal conditions for obtaining the highest density and microhardness are estimated to be 6.79 wt.% TiC at temperature 626.13 °C and compaction pressure of 300 Mpa.

Comparison of ANN and RSM modeling approaches for WEDM process optimization

2021 ◽  
Vol 63 (4) ◽  
pp. 386-392
Author(s):  
Aysun Sagbas ◽  
Filiz Gürtuna ◽  
Ulviye Polat
Keyword(s):  
Neural Networks ◽  
Surface Roughness ◽  
Response Surface Methodology ◽  
Artificial Neural Networks ◽  
Response Surface ◽  
Process Optimization ◽  
Predictive Models ◽  
Machining Process ◽  
Artificial Neural ◽  
Dielectric Flushing

Abstract In this paper, an effective process optimization approach based on artificial neural networks with a back propagation algorithm and response surface methodology including central composite design is presented for the modeling and prediction of surface roughness in the wire electrical discharge machining process. In the development of predictive models, cutting parameters of pulse duration, open circuit voltage, wire speed and dielectric flushing are considered as model variables. After experiments are carried out, the analysis of variance is implemented to identify the contribution of uncontrollable process parameters effecting surface roughness. Then, a comparative analysis of the proposed approaches is carried out to determine the most efficient one. The performance of the developed artificial neural networks and response surface methodology predictive models is tested for prediction accuracy in terms of the coefficient of determination and root mean square error metrics. The results indicate that an artificial neural networks model provides more accurate prediction than the response surface methodology model.

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