CNC turning process parameters optimization on Aluminium 6082 alloy by using Taguchi and ANOVA

Palaniappan, S.P.; Muthukumar, Karuppan; Sabariraj, R.V.; Kumar, S. Dinesh; Sathish, T.

doi:10.1016/j.matpr.2019.10.053

Cited by 127 publications

(21 citation statements)

References 15 publications

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“…Most of chest radiology imaging of COVID-19 patients indicated coordinating component like ground-glass opacities, adjusted morphology, fringe lung appropriation and aspiratory consolidation [13] , [14] , [15] , [16] , [17] . Notwithstanding the way that chest radiology imaging may offer help with early screening of suspected cases, the highlights of various major viral pneumonia are comparative [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] . In this way, it gets hard for the radiologists to perceive COVID-19 from different pneumonias.…”

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Convolutional neural network use chest radiography images for identification of COVID-19

Murali¹,

Bhuvaneswari²,

Parvathi

et al. 2020

Materials Today: Proceedings

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The epic Covid sickness 2019 (COVID-19) has turned into the significant danger to humankind in year 2020. The pandemic COVID-19 flare-up has influenced more than 2.7 million individuals and caused around 187 thousand fatalities worldwide [1] inside scarcely any months of its first appearance in Wuhan city of China and the number is developing quickly in various pieces of world. As researcher everywhere on the world are battling to discover the fix and treatment for COVID-19, the urgent advance fighting against COVID-19 is the screening of immense number of associated cases for disconnection and isolate with the patients. One of the key methodologies in screening of COVID-19 can be chest radiological imaging. The early investigations on the patients influenced by COVID-19 shows the attributes variations from the norm in chest radiography pictures. This introduced a chance to utilize distinctive counterfeit clever (AI) frameworks dependent on profound picking up utilizing chest radiology pictures for the recognition of COVID-19 and numerous such framework were proposed indicating promising outcomes. In this paper, we proposed a profound learning based convolution neural organization to characterize COVID-19, Pneumonia and Normal cases from chest radiology pictures. The proposed convolution neural organization (CNN) grouping model had the option to accomplish exactness of 94.85% on test dataset. The trial was completed utilizing the subset of information accessible in GitHub and Kaggle.

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Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Convolutional neural network use chest radiography images for identification of COVID-19

Murali¹,

Bhuvaneswari²,

Parvathi

et al. 2020

Materials Today: Proceedings

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“…Palaniappan et al [8] effusively discussed about the turning process parameter-based optimization methods with three different responses to identify the optimum input parameters.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Optimization on Operation Parameters in Reinforced Metal Matrix of AA6066 Composite with HSS and Cu

Sesharao¹,

Sathish

Palani

et al. 2021

Advances in Materials Science and Engineering

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This optimization investigation focused on the reinforced metal matrix composite of aluminium alloy. Novel of this work is to fabricate the AA6066 composite with HSS and Cu, continually conduct machining tests, and evaluate the tool wear, surface roughness, and thrust force of the stir-casted specimens. The aluminium composite has 90 percentage of AA6066 alloy reinforcement with six percentage of high-speed steel and four percentage of copper alloy made by the casting method. The fabricated composites’ turning parameters were optimized through the Taguchi method. The turning operation can be done with the help of the normal lathe with the CBN insert tool. The operation parameters such as feed, depth of cut, and steam pressure of the cutting fluid were considered with three different equal intervals in each parameter. In this investigation, the L9 orthogonal array method is used to identify the optimum values of the turning parameters among the considered machining parameters concerning the response such as wear on the turning tool and thrust forces created on machining. The outcome based on the parameters was identified and mentioned as the rank order for individual and combination of all responses with different conditions. Then, the separate and combined optimized input parameters were provided as the conclusion.

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“…To identify the significant effects of the input parameters and their relationship on the output parameters, one of the statistical techniques [ 23 ]—the one-way Analysis of Variance (ANOVA) presented by Tank et al [ 24 ], Köklü et al [ 25 ], Kumar et al [ 26 ], as well as Palaniappan et al [ 27 ]—was used. Statistical calculations were carried out at a confidence level of α = 0.05.…”

Section: Resultsmentioning

confidence: 99%

Dimensional Analysis of Workpieces Machined Using Prototype Machine Tool Integrating 3D Scanning, Milling and Shaped Grinding

et al. 2020

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In the literature, there are a small number of publications regarding the construction and application of machine tools that integrate several machining operations. Additionally, solutions that allow for such integration for complex operations, such as the machining of shape surfaces with complex contours, are relatively rare. The authors of this article carried out dimensional analysis of workpieces machined using a prototype Computerized Numerical Control (CNC) machine tool that integrates the possibilities of 3D scanning, milling operations in three axes, and grinding operations using abrasive discs. The general description of this machine tool with developed methodology and the most interesting results obtained during the experimental studies are given. For a comparative analysis of the influence of the machining method on the geometric accuracy of the test pieces, an Analysis of Variance (ANOVA) was carried out. The obtained results show that for four considered features (deviations of flatness, vertical parallelism, opening dimensions, and opening cylindricality), no statistically significant differences were detected. For the evaluation criteria, the probability level p exceeded the assumed confidence level α = 0.05 and ranged from p = 0.737167 to p = 0.076764. However, such differences were found for two others—a dimensional deviation between flat surfaces (p = 0.010467) and horizontal parallelism deviation (p = 0.0)—as well as for the quality of the machined surface defined by four surface texture parameters: Ra (p = 0.831797), Rt (p = 0.759636), Rq (p = 0.867222), and Rz (p = 0.651896). The information obtained by the ANOVA will be useful for the elimination the weaknesses of the prototype machine tool, further analysis of technological strategies, and to find possible benefits of integrating machining operations.

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CNC turning process parameters optimization on Aluminium 6082 alloy by using Taguchi and ANOVA

Cited by 127 publications

References 15 publications

WITHDRAWN: Convolutional neural network use chest radiography images for identification of COVID-19

WITHDRAWN: Convolutional neural network use chest radiography images for identification of COVID-19

[Retracted] Optimization on Operation Parameters in Reinforced Metal Matrix of AA6066 Composite with HSS and Cu

Dimensional Analysis of Workpieces Machined Using Prototype Machine Tool Integrating 3D Scanning, Milling and Shaped Grinding

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