Estimation of coating thickness in electrostatic spray deposition by machine learning and response surface methodology

Paturi, Uma Maheshwera Reddy; Reddy, N.S.; Cheruku, Suryapavan; Narala, Suresh Kumar Reddy; Cho, Kwon Koo; Reddy, Mohan M.

doi:10.1016/j.surfcoat.2021.127559

Cited by 22 publications

(7 citation statements)

References 31 publications

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“…Precise control of film thickness reduces the paint consumption. [50][51][52] The variations in film thickness for normal paint material and different MWCNT-based nanopaint are plotted in Figure 8. It is observed that there is a decrease of 3.1% in film thickness when compared with 0.5 gm of MWCNT nanopaint and a 3.4% decrease than normal painting thickness.…”

Section: Effect Of Thickness Of the Coating By Robot Paintingmentioning

confidence: 99%

Fuzzy logic expert system with multi-objective optimization of carbon nanotube infused nanopaint surface characteristics analysis using IRB 1410 robot

Jakkula

Sethuramalingam

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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In this investigative study, the Taguchi design (L9) of experiments with TOPSIS multi-objective optimization is used for the robot nanopainting. The optimization objective is to maximize the film glue individually and minimize the surface blemishes and film thickness by varying the IRB 1410 robot painting parameters, such as the robot speed, pressure, and distance. The multi-wall carbon nanotube is infused into the paint materials using the ultrasonication process, and the mechanical properties are analysed with a comparison to regular paint materials. The virtual robot cycle time analysis is carried out for three different robot pathway patterns, namely linear, zig-zag, and circular. It is then compared with real-time experimental robot nanopaint on cold rolled close annealing steel materials. The obtained results show that surface roughness and thickness are minimized by 54% and 33.4%, respectively, using the robot nano spray coating when compared with regular spray coating. The film adhesive of robot Nano spray coating is maximized by 2.8% more than normal spray coating. A fuzzy logic expert system model is used to evaluate the surface finish characteristics of the nanopaint surface with low prediction error and with 89.2% confidence level. Heat transfer analysis of the robot nano-coated substrate is compared with that of the robot normal-coated substrate.

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Section: Effect Of Thickness Of the Coating By Robot Paintingmentioning

confidence: 99%

Fuzzy logic expert system with multi-objective optimization of carbon nanotube infused nanopaint surface characteristics analysis using IRB 1410 robot

Jakkula

Sethuramalingam

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…Several papers have suggested machine learning (ML) approaches as an alternative to polynomial regression. The approaches that are considered in the literature include Support Vector Machines (SVM) [1][2][3][4][5][6], Neural Networks (NN) [1,2,4,[6][7][8][9], Random Forests (rf) [1,3,4,10], Boosting and its extension [1,3], Extra Tree regression (ET) [3,4] and Classification And Regression Trees (CART) [1]. In these papers, the authors compared some of these approaches only on real datasets, using specific settings with respect to the nature and dimensions of the data.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Alternatives to Response Surface Models

Ghattas

Manzon

2023

Mathematics

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In the Design of Experiments, we seek to relate response variables to explanatory factors. Response Surface methodology (RSM) approximates the relation between output variables and a polynomial transform of the explanatory variables using a linear model. Some researchers have tried to adjust other types of models, mainly nonlinear and nonparametric. We present a large panel of Machine Learning approaches that may be good alternatives to the classical RSM approximation. The state of the art of such approaches is given, including classification and regression trees, ensemble methods, support vector machines, neural networks and also direct multi-output approaches. We survey the subject and illustrate the use of ten such approaches using simulations and a real use case. In our simulations, the underlying model is linear in the explanatory factors for one response and nonlinear for the others. We focus on the advantages and disadvantages of the different approaches and show how their hyperparameters may be tuned. Our simulations show that even when the underlying relation between the response and the explanatory variables is linear, the RSM approach is outperformed by the direct neural network multivariate model, for any sample size (<50) and much more for very small samples (15 or 20). When the underlying relation is nonlinear, the RSM approach is outperformed by most of the machine learning approaches for small samples (n ≤ 30).

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“…For example, typical control factors have been fed to machine learning models such as a support vector machine (SVM) [13], a neural network (NN) [14], or a Gaussian process regression (GPR) [15] to predict coating quality. Paturi et al [16] employed a genetic algorithm (GA) and response surface methodology to establish the optimum conditions for electrostatic spray deposition parameters, and they estimated coating thickness using proposed artificial neural network (ANN) and SVM models. However, this hybrid method had significant cost for model training and could not ensure production fluctuation.…”

Section: Introductionmentioning

confidence: 99%

Self-Attention-Augmented Generative Adversarial Networks for Data-Driven Modeling of Nanoscale Coating Manufacturing

Zhu

Yang

et al. 2022

Micromachines

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Nanoscale coating manufacturing (NCM) process modeling is an important way to monitor and modulate coating quality. The multivariable prediction of coated film and the data augmentation of the NCM process are two common issues in smart factories. However, there has not been an artificial intelligence model to solve these two problems simultaneously. Focusing on the two problems, a novel auxiliary regression using a self-attention-augmented generative adversarial network (AR-SAGAN) is proposed in this paper. This model deals with the problem of NCM process modeling with three steps. First, the AR-SAGAN structure was established and composed of a generator, feature extractor, discriminator, and regressor. Second, the nanoscale coating quality was estimated by putting online control parameters into the feature extractor and regressor. Third, the control parameters in the recipes were generated using preset parameters and target quality. Finally, the proposed method was verified by the experiments of a solar cell antireflection coating dataset, the results of which showed that our method performs excellently for both multivariable quality prediction and data augmentation. The mean squared error of the predicted thickness was about 1.6~2.1 nm, which is lower than other traditional methods.

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Estimation of coating thickness in electrostatic spray deposition by machine learning and response surface methodology

Cited by 22 publications

References 31 publications

Fuzzy logic expert system with multi-objective optimization of carbon nanotube infused nanopaint surface characteristics analysis using IRB 1410 robot

Fuzzy logic expert system with multi-objective optimization of carbon nanotube infused nanopaint surface characteristics analysis using IRB 1410 robot

Machine Learning Alternatives to Response Surface Models

Self-Attention-Augmented Generative Adversarial Networks for Data-Driven Modeling of Nanoscale Coating Manufacturing

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