scholarly journals Modelling of cutting forces and surface roughness evolutions during straight turning of Stellite 6 material based on response surface methodology, artificial neural networks and support vector machine approaches

2021 ◽  
Vol 15 (4) ◽  
pp. 8540-8554
Author(s):  
Brahim Ben Fathallah ◽  
R. Saidi ◽  
S. Belhadi ◽  
M.A. Yallese ◽  
T. Mabrouki
Keyword(s):  
Support Vector Machine ◽  
Response Surface Methodology ◽  
Cutting Force ◽  
Response Surface ◽  
Feed Rate ◽  
Research Work ◽  
Operating Conditions ◽  
Support Vector ◽  
Stellite 6 ◽  
Artificial Neural

The present research work proposes an experimental investigation helping to comprehend fundamental impacts of operating conditions during the cutting of cobalt alloys (Stellite 6). Thus, an experimental design was adopted to allow to build predicted mathematical models for the outputs, which are the average peak-to-valley profile roughness (Rz) and the tangential cutting force (Ft). Artificial neural network (ANN), support vector machine (SVM) and response surface methodology (RSM) were exploited to model the pre-cited outputs according to operation parameters. As a result, it has been highlighted that both feed rate and cutting depth, considerably, affect tangential cutting force evolution. Moreover, results show that both the insert feed rate and nose radius, are higher. This means the average peak-to-valley profile roughness is higher. In order to put out the effect of operating parameters on cutting outputs, Analysis of variance (ANOVA) method has been employed. This has allowed the detection of significant cutting conditions affecting average peak-to-valley profile roughness and tangential cutting force. In fact, to highlight the performance of adopted mathematical approaches, a comparison between RSM, ANN, and SVM has been also established in this study.

scholarly journals Effective Chromium Adsorption From Aqueous Solutions and Tannery Wastewater Using Bimetallic Fe/Cu Nanoparticles: Response Surface Methodology and Artificial Neural Network

2021 ◽  
Vol 14 ◽  
pp. 117862212110281
Author(s):  
Ahmed S. Mahmoud ◽  
Nouran Y. Mohamed ◽  
Mohamed K. Mostafa ◽  
Mohamed S. Mahmoud
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Industrial Effluent ◽  
Oxygen Demand ◽  
Operating Conditions ◽  
Tannery Wastewater ◽  
P Value ◽  
Cu Nanoparticles ◽  
Artificial Neural ◽  
Cr Removal

Tannery industrial effluent is one of the most difficult wastewater types since it contains a huge concentration of organic, oil, and chrome (Cr). This study successfully prepared and applied bimetallic Fe/Cu nanoparticles (Fe/Cu NPs) for chrome removal. In the beginning, the Fe/Cu NPs was equilibrated by pure aqueous chrome solution at different operating conditions (lab scale), then the nanomaterial was applied in semi full scale. The operating conditions indicated that Fe/Cu NPs was able to adsorb 68% and 33% of Cr for initial concentrations of 1 and 9 mg/L, respectively. The removal occurred at pH 3 using 0.6 g/L Fe/Cu dose, stirring rate 200 r/min, contact time 20 min, and constant temperature 20 ± 2ºC. Adsorption isotherm proved that the Khan model is the most appropriate model for Cr removal using Fe/Cu NPs with the minimum error sum of 0.199. According to khan, the maximum uptakes was 20.5 mg/g Cr. Kinetic results proved that Pseudo Second Order mechanism with the least possible error of 0.098 indicated that the adsorption mechanism is chemisorption. Response surface methodology (RSM) equation was developed with a significant p-value = 0 to label the relations between Cr removal and different experimental parameters. Artificial neural networks (ANNs) were performed with a structure of 5-4-1 and the achieved results indicated that the effect of the dose is the most dominated variable for Cr removal. Application of Fe/Cu NPs in real tannery wastewater showed its ability to degrade and disinfect organic and biological contaminants in addition to chrome adsorption. The reduction in chemical oxygen demand (COD), biological oxygen demand (BOD), total suspended solids (TSS), total phosphorus (TP), total nitrogen (TN), Cr, hydrogen sulfide (H2S), and oil reached 61.5%, 49.5%, 44.8%, 100%, 38.9%, 96.3%, 88.7%, and 29.4%, respectively.

scholarly journals Pioneer dataset and recognition of Handwritten Pashto characters using Convolution Neural Networks

2020 ◽  
pp. 002029402096482
Author(s):  
Sulaiman Khan ◽  
Abdul Hafeez ◽  
Hazrat Ali ◽  
Shah Nazir ◽  
Anwar Hussain
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Research Work ◽  
Recognition Rate ◽  
Recognition Task ◽  
Convolution Neural Network ◽  
Medium Size ◽  
Support Vector ◽  
Artificial Neural

This paper presents an efficient OCR system for the recognition of offline Pashto isolated characters. The lack of an appropriate dataset makes it challenging to match against a reference and perform recognition. This research work addresses this problem by developing a medium-size database that comprises 4488 samples of handwritten Pashto character; that can be further used for experimental purposes. In the proposed OCR system the recognition task is performed using convolution neural network. The performance analysis of the proposed OCR system is validated by comparing its results with artificial neural network and support vector machine based on zoning feature extraction technique. The results of the proposed experiments shows an accuracy of 56% for the support vector machine, 78% for artificial neural network, and 80.7% for the proposed OCR system. The high recognition rate shows that the OCR system based on convolution neural network performs best among the used techniques.

scholarly journals Fixed bed utilization for the isolation of xylene vapor: Kinetics and optimization using response surface methodology and artificial neural network

2020 ◽  
Vol 26 (2) ◽  
pp. 200105-0
Author(s):  
Kaushal Naresh Gupta ◽  
Rahul Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Flow Rate ◽  
Operating Conditions ◽  
Optimum Operating ◽  
Operating Parameters ◽  
Bed Height ◽  
Artificial Neural

This paper discusses the isolation of xylene vapor through adsorption using granular activated carbon as an adsorbent. The operating parameters investigated were bed height, inlet xylene concentration and flow rate, their influence on the percentage utilization of the adsorbent bed up to the breakthrough was found out. Mathematical modeling of experimental data was then performed by employing a response surface methodology (RSM) technique to obtain a set of optimum operating conditions to achieve maximum percentage utilization of bed till breakthrough. A fairly high value of R2 (0.993) asserted the proposed polynomial equation’s validity. ANOVA results indicated the model to be highly significant with respect to operating parameters studied. A maximum of 76.1% utilization of adsorbent bed was found out at a bed height of 0.025 m, inlet xylene concentration of 6,200 ppm and a gas flow rate of 25 mL.min-1. Furthermore, the artificial neural network (ANN) was also employed to compute the percentage utilization of the adsorbent bed. A comparison between RSM and ANN divulged the performance of the latter (R2 = 0.99907) to be slightly better. Out of various kinetic models studied, the Yoon-Nelson model established its appropriateness in anticipating the breakthrough curves.

Modeling and Experimental Study on Cutting Force of Diamond Circular Saw in Cutting Granite Using Response Surface Methodology

2013 ◽  
Vol 652-654 ◽  
pp. 2191-2195 ◽  
Author(s):  
Zheng Mei Zhang ◽  
Hai Wen Xiao ◽  
Gui Zhen Wang ◽  
Shu Zhong Zhang ◽  
Shu Qin Zhang
Keyword(s):  
Response Surface Methodology ◽  
Cutting Force ◽  
Response Surface ◽  
Feed Rate ◽  
Cutting Speed ◽  
Feed Speed ◽  
Machining Parameters ◽  
Cutting Depth ◽  
First Order ◽  
Circular Saw

Based on experiment of sawing Wulian red granite with diamond circular saw, the relations between the cutting force with machining parameters are studied. Cutting speed, feed rate and cutting depth are considered as the process parameters. The cutting force in sawing granite operation are measured and the experimental results are then analyzed using response surface methodology. From the analysis, it is seen that the cutting force Fx , Fy and Fz are reduced with the increase of cutting speed and increased with the increase of feed rate and cutting depth, and the mathematical models of the cutting force are developed. By ANOVA for the cutting force models, It is concluded that the models are significant at 95% confidence level and the significant effects are the first-order of cutting speed, feed speed, cutting depth and the quadratic of cutting depth.

Multi-Objective Optimization Using Box-Behken of Response Surface Methodology for High-Speed Machining of Inconel 718

2014 ◽  
Vol 629 ◽  
pp. 487-492 ◽  
Author(s):  
Mohd Shahir Kasim ◽  
Che Hassan Che Haron ◽  
Jaharah Abd Ghani ◽  
E. Mohamad ◽  
Raja Izamshah ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Cutting Force ◽  
Response Surface ◽  
Tool Life ◽  
Feed Rate ◽  
Inconel 718 ◽  
High Speed ◽  
Cutting Speed ◽  
High Speed Milling

This study was carried out to investigate how the high-speed milling of Inconel 718 using ball nose end mill could enhance the productivity and quality of the finish parts. The experimental work was carried out through Response Surface Methodology via Box-Behnken design. The effect of prominent milling parameters, namely cutting speed, feed rate, depth of cut (DOC), and width of cut (WOC) were studied to evaluate their effects on tool life, surface roughness and cutting force. In this study, the cutting speed, feed rate, DOC, and WOC were in the range of 100 - 140 m/min, 0.1 - 0.2 mm/tooth, 0.5 - 1.0 mm and 0.2 - 1.8 mm, respectively. In order to reduce the effect of heat generated during the high speed milling operation, minimum quantity lubrication of 50 ml/hr was used. The effect of input factors on the responds was identified by mean of ANOVA. The response of tool life, surface roughness and cutting force together with calculated material removal rate were then simultaneously optimized and further described by perturbation graph. Interaction between WOC with other factors was found to be the most dominating factor of all responds. The optimum cutting parameter which obtained the longest tool life of 60 mins, minimum surface roughness of 0.262 μm and resultant force of 221 N was at cutting speed of 100 m/min, feed rate of 0.15 mm/tooth, DOC 0.5 m and WOC 0.66 mm.

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%.

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