Multiple regression model for surface roughness using full factorial design

Dahbi, Samya; Moussami, Haj El; Ezzine, Latifa

doi:10.1109/iesm.2015.7380194

Cited by 13 publications

(9 citation statements)

References 7 publications

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“…In essence factorial design model defines the effect of each predictor variable on the response variable. The model also defines the effect of interaction of predictors on the response variable (Gottipati and Mishra, 2010;Dahbi et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

Estimating tree species diversity in the savannah using NDVI and woody canopy cover

Madonsela

Cho

Ramoelo

et al. 2018

International Journal of Applied Earth Observation and Geoinfor

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Remote sensing applications in biodiversity research often rely on the establishment of relationships between spectral information from the image and tree species diversity measured in the field. Most studies have used normalized difference vegetation index (NDVI) to estimate tree species diversity on the basis that it is sensitive to primary productivity which defines spatial variation in plant diversity. The NDVI signal is influenced by photosynthetically active vegetation which, in the savannah, includes woody canopy foliage and grasses. The question is whether the relationship between NDVI and tree species diversity in the savanna depends on the woody cover percentage. This study explored the relationship between woody canopy cover (WCC) and tree species diversity in the savannah woodland of southern Africa and also investigated whether there is a significant interaction between seasonal NDVI and WCC in the factorial model when estimating tree species diversity. To fulfil our aim, we followed stratified random sampling approach and surveyed tree species in 68 plots of 90m X 90m across the study area. Within each plot, all trees with diameter at breast height of >10cm were sampled and Shannon index-a common measure of species diversity which considers both species richness and abundance-was used to This article was published in JAG: https://www.sciencedirect.com/science/article/pii/S0303243417302568 quantify tree species diversity. We then extracted WCC in each plot from existing fractional woody cover product produced from Synthetic Aperture Radar (SAR) data. Factorial regression model was used to determine the interaction effect between NDVI and WCC when estimating tree species diversity. Results from regression analysis showed that (i) WCC has a highly significant relationship with tree species diversity (r 2 = 0.21; p < 0.01), (ii) the interaction between the NDVI and WCC is not significant, however, the factorial model significantly reduced the error of prediction (RMSE = 0.47, p <0.05) compared to NDVI (RMSE = 0.49) or WCC (RMSE = 0.49) model during the senescence period. The result justifies our assertion that combining NDVI with WCC will be optimal for biodiversity estimation during the senescence period.

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Section: Discussionmentioning

confidence: 99%

Estimating tree species diversity in the savannah using NDVI and woody canopy cover

Madonsela

Cho

Ramoelo

et al. 2018

International Journal of Applied Earth Observation and Geoinfor

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“…where s j is the weighted sum of the normalized inputs (V, a, f, r) and it is calculated by equation (9). Weights w ji and biases b j for the eight neurons of the hidden layer are given in Table 5.…”

Section: Resultsmentioning

confidence: 99%

“…Indeed, the results of our previous study reveal that interactions between turning parameters have significant effects on surface roughness. 9 The three main turning parameters considered in the previous studies were cutting speed, feed rate, and depth of cut. Unfortunately, few studies integrated the tool nose radius as a crucial cutting parameter in their modeling approaches.…”

Section: Introductionmentioning

confidence: 99%

“…In fact, nose radius and its interaction with the three main cutting parameters have significant effects on surface roughness according to our previous study. 9 Therefore, the integration of this parameter as an input of our ANN will contribute to take into account its main and interaction effects to model the eight cutting performances with high accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Modeling of cutting performances in turning process using artificial neural networks

Dahbi

Ezzine

Moussami

2017

International Journal of Engineering Business Management

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In this article, we present the modeling of cutting performances in turning of 2017A aluminum alloy under four turning parameters: cutting speed, feed rate, depth of cut, and nose radius. The modeled performances include surface roughness, cutting forces, cutting temperature, material removal rate, cutting power, and specific cutting pressure. The experimental data were collected by conducting turning experiments on a computer numerically controlled lathe and by measuring the cutting performances with forces measuring chain, an infrared camera, and a roughness tester. The collected data were used to develop an artificial neural network that models the pre-cited cutting performances by following a specific methodology. The adequate network architecture was selected using three performance criteria: correlation coefficient (R 2), mean squared error (MSE), and average percentage error (APE). It was clearly seen that the selected network estimates the cutting performances in turning process with high accuracy: R 2 > 99%, MSE < 0.3%, and APE < 6%.

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“…A well designed model should facilitate control of the manufacturing process and making it resilient to internal and external disruptions, as well as leave room and provide mechanisms for optimization of the process. Many successful applications of SPC and DoE tools prove that they should be used in the manufacturing industry [9,10,11]. However, efficient use of the tools is often hindered by a lack of clear guidelines concerning particular industrial applications.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Surface Layer Grinding Process With the Use of Design of Experiments Methodology

Kujawińska¹,

Rogalewicz²,

Diering³

et al. 2018

dtetr

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The authors introduce the problem of excessive waste of raw material in the wood industry on the basis of the floorboard manufacturing process. The study was aimed to optimize the process of lamella grinding -the final stage in the manufacturing of the oak surface layer. The paper describes implementation of the design of experiments (DoE) methodology to improve the production process of the floorboard surface layer (lamella). The authors made certain assumptions and designed an experiment to obtain high quality lamellas and minimize the waste of raw material. A three -factor full factorial experiment was conducted. The paper presents the conducted study and the authors' recommendations concerning the determination of optimal machining parameters (on the basis of the results of Experiment 1), parallel arrangement of lamellas (on the basis of Experiment 2), and minimum machining allowance (Experiment 3).

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Multiple regression model for surface roughness using full factorial design

Cited by 13 publications

References 7 publications

Estimating tree species diversity in the savannah using NDVI and woody canopy cover

Estimating tree species diversity in the savannah using NDVI and woody canopy cover

Modeling of cutting performances in turning process using artificial neural networks

Improvement of Surface Layer Grinding Process With the Use of Design of Experiments Methodology

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