A Hybrid Predictive Approach for Chromium Layer Thickness in the Hard Chromium Plating Process Based on the Differential Evolution/Gradient Boosted Regression Tree Methodology

The purpose of the industrial process of chromium plating is the creation of a hard and wear-resistant layer of chromium over a metallic surface. One of the main properties of chromium plating is its resistance to both wear and corrosion. This research presents an innovative nonparametric machine learning approach that makes use of a hybrid gradient boosted regression tree (GBRT) methodology for hard chromium layer thickness prediction. GBRT is a non-parametric statistical learning technique that produces a prediction model in the form of an ensemble of weak prediction models. The motivation for boosting is a procedure that combines the output of many weak classifiers to produce a powerful committee. In this study, the GBRT hyperparameters were optimized with the help of differential evolution (DE). DE is an optimization technique within evolutionary computing. The results found that this model was able to predict the thickness of the chromium layer formed in this industrial process with a determination coefficient equal to 0.9842 and a root-mean-square error value of 0.01590. The two most important variables of the model were the time of the hard-chromium process and the thickness of the layer removed by electropolishing. Thus, these results provide a foundation for an accurate predictive model of hard chromium layer thickness. The derived model also allowed the ranking of the importance of the independent input variables that were examined. Finally, the high performance and simplicity of the model make the DE/GBRT method attractive compared to conventional forecasting techniques.

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Effect of Abrasive Tools in Galvanomechanical Treatment on the Roughness of the Deposited Chromium Layer

Solid State Phenomena ◽

10.4028/www.scientific.net/ssp.284.1178 ◽

2018 ◽

Vol 284 ◽

pp. 1178-1183 ◽

Cited By ~ 2

Author(s):

D.A. Zherebtcov ◽

Oksana N. Gruba ◽

K.R. Smolyakova

Keyword(s):

Experimental Studies ◽

The Body ◽

Abrasive Tool ◽

Chromium Layer ◽

Pressing Force ◽

Hard Chromium ◽

Chromium Plating ◽

Cylindrical Parts ◽

Abrasive Tools

The article deals with the method of obtaining a hard chromium coating on details of the "body of rotation" type with the use of an abrasive tool. The influence of the composition and hardness of the elastic abrasive tool on the results of galvanomechanical chromium plating of rotating cylindrical parts has been studied. Binder compositions for an abrasive tool used to improve the roughness of the deposited chromium layer have been developed. A series of experimental studies has been carried out with chromium plating of steel cylindrical parts with simultaneous abrasive processing. Beforehand, an abrasive tool with previously developed binder formulations was manufactured. The obtained results of the influence of the characteristics of the abrasive tool and its pressing force on the chromium-plated part on the quality of the precipitated chromium made it possible to determine the optimum modes for obtaining a coating of the required thickness. Also, a suitable abrasive tool has been chosen to obtain a coating of proper quality.

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Evolutionary support vector regression algorithm applied to the prediction of the thickness of the chromium layer in a hard chromium plating process

Applied Mathematics and Computation ◽

10.1016/j.amc.2013.11.031 ◽

2014 ◽

Vol 227 ◽

pp. 164-170 ◽

Cited By ~ 8

Author(s):

F. Sánchez Lasheras ◽

P.J. García Nieto ◽

F.J. de Cos Juez ◽

J.A. Vilán Vilán

Keyword(s):

Support Vector Regression ◽

Support Vector ◽

Chromium Layer ◽

Hard Chromium ◽

Chromium Plating

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The used of the Boosted Regression Tree Optimization Technique to Analyse an Air Pollution data.

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b3807.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 1565-1575 ◽

Cited By ~ 1

Keyword(s):

Mean Squared Error ◽

Optimization Technique ◽

Model Development ◽

Regression Tree ◽

Peninsular Malaysia ◽

Boosted Regression Trees ◽

Atmospheric Environment ◽

Optimum Number ◽

Boosted Regression Tree ◽

Hourly Data

The stochastic boosted regression trees (BRT) technique has the capability to quantify and explain the relationships between explanatory variables. We applied this machine learning modelling technique to derive the relationships between the gases air pollutants, meteorological conditions and time system variables of particulate matter (PM10) concentrations. In order to get lowest prediction error and to avoid over-fitting, the parameters of the BRT model need to be tuned. In this experiment, 25 BRT models were generated from 14 years’ worth of hourly data (122,736 a one hour averaged data from January 2000 to December 2013 gathered from four Continuous Automated Air Quality Monitoring Stations in peninsular Malaysia (located in Klang, Selangor (CA0011), Perai, Penang (CA0003), Kota Bharu, Kelantan (CA0022) and Kemaman, Terengganu (CA0002)). Seventy percent of the data were used for training and 30 percent for validation of the models. An experiment was conducted to determine the best iteration that could model hourly PM10 concentrations by optimizing the BRT parameter which are learning rate (lr), tree complexity (tc) and number of trees (nt). Five different lr (0.001, 0.005, 0.01, 0.05 and 0.1) were tested with different tree complexities (1 to 20) in the BRT model development process. From the experiment, the combination of lr = 0.05 and tc = 5 for the training set for the BRT model achieved the lowest root mean squared error (RMSE) compared to the other tested combinations. It was also found that the number of trees increased with the increment in the number of samples. A high coefficient of determinant (R2 ) value (0.90) for the linear relationship between the number of samples and nt was found for all the four stations. The optimum number of trees for the model was estimated by using 10-fold cross-validation. It was found that the best number of iterations for Klang, Perai, Kota Bahru and Kemaman were 12,327, 32,987, 16,370 and 57,634, respectively. The prediction accuracy of the model was tested by using the fraction of prediction namely a factor of two (FAC2), mean bias, mean gross error, RMSE, correlation coefficient (R), and index of agreement (IOA). The prediction performance of the final BRT model based on the R value was 0.81, 0.78, 0.85 and 0.81 for for Perai, Kemaman, Klang and Kota Bahru, respectively, which indicates that the BRT model developed and applicability of this can be used in other atmospheric environment data.

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Chrome Layer Thickness Modelling in a Hard Chromium Plating Process Using a Hybrid PSO/ RBF–SVM–Based Model

International Journal of Interactive Multimedia and Artificial Intelligence ◽

10.9781/ijimai.2020.11.004 ◽

2020 ◽

Vol 6 (4) ◽

pp. 39

Author(s):

Paulino José García Nieto ◽

Esperanza García-Gonzalo ◽

Fernando Sánchez Lasheras ◽

Antonio Bernardo Sánchez

Keyword(s):

Layer Thickness ◽

Hard Chromium ◽

Chromium Plating ◽

Chrome Layer

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The use of design of experiments to improve a neural network model in order to predict the thickness of the chromium layer in a hard chromium plating process

Mathematical and Computer Modelling ◽

10.1016/j.mcm.2010.03.007 ◽

2010 ◽

Vol 52 (7-8) ◽

pp. 1169-1176 ◽

Cited By ~ 35

Author(s):

F. Sánchez Lasheras ◽

J.A. Vilán Vilán ◽

P.J. García Nieto ◽

J.J. del Coz Díaz

Keyword(s):

Neural Network ◽

Design Of Experiments ◽

Network Model ◽

Neural Network Model ◽

Chromium Layer ◽

Hard Chromium ◽

Chromium Plating

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Using boosted regression tree models to predict the diets of juvenile bull sharks in a subtropical estuary

Marine Ecology Progress Series ◽

10.3354/meps13568 ◽

2020 ◽

Author(s):

E Cottrant ◽

P Matich ◽

MR Fisher

Keyword(s):

Regression Tree ◽

Boosted Regression Tree ◽

Tree Models ◽

Subtropical Estuary

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Drivers of diversity gradients of a highly mobile marine assemblage in a mesoscale seascape

Marine Ecology Progress Series ◽

10.3354/meps13264 ◽

2020 ◽

Vol 638 ◽

pp. 149-164

Author(s):

GM Svendsen ◽

M Ocampo Reinaldo ◽

MA Romero ◽

G Williams ◽

A Magurran ◽

...

Keyword(s):

Environmental Gradients ◽

Regression Tree ◽

Demersal Fish ◽

Open Ocean ◽

Shared Resources ◽

Thermal Front ◽

Boosted Regression Tree ◽

Diversity Gradients ◽

Α Diversity ◽

Bottom Depth

With the unprecedented rate of biodiversity change in the world today, understanding how diversity gradients are maintained at mesoscales is a key challenge. Drawing on information provided by 3 comprehensive fishery surveys (conducted in different years but in the same season and with the same sampling design), we used boosted regression tree (BRT) models in order to relate spatial patterns of α-diversity in a demersal fish assemblage to environmental variables in the San Matias Gulf (Patagonia, Argentina). We found that, over a 4 yr period, persistent diversity gradients of species richness and probability of an interspecific encounter (PIE) were shaped by 3 main environmental gradients: bottom depth, connectivity with the open ocean, and proximity to a thermal front. The 2 main patterns we observed were: a monotonic increase in PIE with proximity to fronts, which had a stronger effect at greater depths; and an increase in PIE when closer to the open ocean (a ‘bay effect’ pattern). The originality of this work resides on the identification of high-resolution gradients in local, demersal assemblages driven by static and dynamic environmental gradients in a mesoscale seascape. The maintenance of environmental gradients, specifically those associated with shared resources and connectivity with an open system, may be key to understanding community stability.

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Regional Perspective of Safety Performance Functions and Their Application to Florida Intersections in Suburban Residential and Urban General Context Classification Categories

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211008447 ◽

2021 ◽

pp. 036119812110084

Author(s):

Ghalia Gamaleldin ◽

Haitham Al-Deek ◽

Adrian Sandt ◽

John McCombs ◽

Alan El-Urfali

Keyword(s):

Negative Binomial ◽

Regression Tree ◽

Influential Factors ◽

Safety Performance ◽

General Context ◽

Boosted Regression Tree ◽

Safety Performance Functions ◽

Traffic Volumes ◽

Intersection Types ◽

The Individual

Safety performance functions (SPFs) are essential tools to help agencies predict crashes and understand influential factors. Florida Department of Transportation (FDOT) has implemented a context classification system which classifies intersections into eight context categories rather than the three classifications used in the Highway Safety Manual (HSM). Using this system, regional SPFs could be developed for 32 intersection types (unsignalized and signalized 3-leg and 4-leg for each category) rather than the 10 HSM intersection types. In this paper, eight individual intersection group SPFs were developed for the C3R-Suburban Residential and C4-Urban General categories and compared with full SPFs for these categories. These comparisons illustrate the unique and regional insights that agencies can gain by developing these individual SPFs. Poisson, negative binomial, zero-inflated, and boosted regression tree models were developed for each studied group as appropriate, with the best model selected for each group based on model interpretability and five performance measures. Additionally, a linear regression model was built to predict minor roadway traffic volumes for intersections which were missing these volumes. The full C3R and C4 SPFs contained four and six significant variables, respectively, while the individual intersection group SPFs in these categories contained six and nine variables. Factors such as major median, intersection angle, and FDOT District 7 regional variable were absent from the full SPFs. By developing individual intersection group SPFs with regional factors, agencies can better understand the factors and regional differences which affect crashes in their jurisdictions and identify effective treatments.

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