Effect of Process Parameters on the Electrodeposition of Zinc on 1010 Steel: Central Composite Design Optimization

Kul, Mehmet; Oskay, Kürşad Oğuz; Erden, Fuat; Akça, Erdem; Katırcı, Ramazan; Köksal, Erkan; Akinci, Evindar

doi:10.20964/2020.10.19

Cited by 18 publications

(11 citation statements)

References 44 publications

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“…The relationship between the various factors that affect the rate of electrodeposition is complex and non-linear [28], so it is difficult to determine with mathematical expressions. To solve this problem, based on the BP neural network, we establish the mapping relationships of the various factors and the electrochemical deposition rate.…”

Section: Microscale Meniscus Confined Electrodeposition Parameter Opt...mentioning

confidence: 99%

Study of Microscale Meniscus Confined Electrodeposition Based on COMSOL

Zhang

Rong

et al. 2021

Micromachines

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The rate and quality of microscale meniscus confined electrodeposition represent the key to micromanipulation based on electrochemistry and are extremely susceptible to the ambient relative humidity, electrolyte concentration, and applied voltage. To solve this problem, based on a neural network and genetic algorithm approach, this paper optimizes the process parameters of the microscale meniscus confined electrodeposition to achieve high-efficiency and -quality deposition. First, with the COMSOL Multiphysics, the influence factors of electrodeposition were analyzed and the range of high efficiency and quality electrodeposition parameters were discovered. Second, based on the back propagation (BP) neural network, the relationships between influence factors and the rate of microscale meniscus confined electrodeposition were established. Then, in order to achieve effective electrodeposition, the determined electrodeposition rate of 5 × 10−8 m/s was set as the target value, and the genetic algorithm was used to optimize each parameter. Finally, based on the optimization parameters obtained, we proceeded with simulations and experiments. The results indicate that the deposition rate maximum error is only 2.0% in experiments. The feasibility and accuracy of the method proposed in this paper were verified.

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Section: Microscale Meniscus Confined Electrodeposition Parameter Opt...mentioning

confidence: 99%

Study of Microscale Meniscus Confined Electrodeposition Based on COMSOL

Zhang

Rong

et al. 2021

Micromachines

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show abstract

“…On the other hand, as already reported, electroplating is not a spontaneous process, thus the current is the driving force of this method and directly impact the current density. As much higher is this operational parameter, a greater amount of zinc tended to be reduced on the substrate surface [14,19,23].…”

Section: Effect On Process Efficiencymentioning

confidence: 99%

“…Most reported studies are focused on optimizing the acid baths containing chloride [11,23] or in the morphology changes caused by additives in the electrolyte bath constitution [18,24]. Therefore, the process efficiency and the zinc coating morphology on electroplated AISI 1008 carbon steel samples were evaluated in this work.…”

Section: Introductionmentioning

confidence: 99%

Influence of current density and temperature in the zinc electroplating process at sulfate-based acid solution: study on process efficiency and coating morphology

et al. 2021

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Zinc as a metallic coating is a common strategy to protect the carbon steel against corrosion. The most common process of zinc deposition is known as electroplating. Because of the high toxicity of cyanide-based baths, the interest in acid baths has grown, but they present many challenges to be overcome. Several operational parameters and bath constitution – such as current density, pH, and zinc concentration – can impact the current efficiency, deposit quality, and coating morphology. In this work, the process efficiency and the coating morphology were evaluated on electroplated AISI 1008 carbon steel samples. The current density and temperature were individually varied on a range from 7.5 mA.cm-2 to 30.5 mA.cm-2, and from 40 °C to 60 °C, respectively. The process efficiency was evaluated by current efficiency (eC). The surface morphology was analyzed by both optical microscopy (OM) and scanning electron microscopy (SEM). Varying the bath temperature did not promote impacts in the current efficiency, which remained in all temperatures evaluated over 95%. On the other hand, increasing the current density, increased the current efficiency, starting from (85 ± 2)% at 7.5 mA.cm-2 to (92 ± 2)% at 19.0 mA.cm-2, and (95 ± 1)% at 30.5 mA.cm-2. Through OM and SEM analysis, the increase in the temperature tended to turn the coating rougher, and the sample was not completely covered at 7.5 mA.cm-2. Therefore, we recommend the use of a temperature between 40 °C and 50 °C associated with a current density of 30.5 mA.cm-2.

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“…can be performed using this method [2]- [6]. Zinc coating among the metallic coatings is the most vastly used method to protect the steels from the corrosion [7]. Two types of zinc coating baths are present.…”

Section: Introductionmentioning

confidence: 99%

“…Two types of zinc coating baths are present. One is acidic and the other is alkaline zinc electroplating bath [7], [8]. Many parameters are present to perform the coating in the electrodeposition bath [8].…”

Section: Introductionmentioning

confidence: 99%

The Optimization of The Zinc Electroplating Bath Using Machine Learning And Genetic Algorithms (NSGA-II)

Katırcı¹,

TEKİN>²

2022

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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In this study, our aim is to predict the compositions of zinc electroplating bath using machine learning method and optimize the organic additives with NSGA-II (Non-dominated Sorting Genetic Algorithm) optimization algorithm. Mask RCNN was utilized to classify the coated plates according to their appearance. The names of classes were defined as ”Full Bright”, ”Full Fail”, ”HCD Fail” and ”LCD Fail”. The intersection over union (IoU) values of the Mask RCNN model were determined in the range of 93–97%. Machine learning algorithms, MLP, SVR, XGB, RF, were trained using the classification of the coated panels whose classes were detected by the Mask RCNN. In the machine learning training, the additives in the electrodeposition bath were specified as input and the classes of the coated panels as output. From the trained models, RF gave the highest F1 scores for all the classes. The F1 scores of RF model for ”Full Bright”, ”Full Fail”, ”HCD Fail” and ”LCD Fail” are 0.95, 0.91, 1 and 0.80 respectively. Genetic algorithm (NSGA-II) was used to optimize the compositions of the bath. The trained RF models for all the classes were utilized as the objective function. The ranges of organic additives, which should be used for all the classes in the electrodeposition bath, were determined.

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Effect of Process Parameters on the Electrodeposition of Zinc on 1010 Steel: Central Composite Design Optimization

Cited by 18 publications

References 44 publications

Study of Microscale Meniscus Confined Electrodeposition Based on COMSOL

Study of Microscale Meniscus Confined Electrodeposition Based on COMSOL

Influence of current density and temperature in the zinc electroplating process at sulfate-based acid solution: study on process efficiency and coating morphology

The Optimization of The Zinc Electroplating Bath Using Machine Learning And Genetic Algorithms (NSGA-II)

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