Review of coating and curing processes: Evaluation in automotive
          industry

Pendar, Mohammad-Reza; Rodrigues, F. F.; Páscoa, José; Lima, Rui

doi:10.1063/5.0109376

Cited by 20 publications

(5 citation statements)

References 199 publications

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“…In the context of coating processes in automotive manufacturing, Pendar et al [86] focused on reviewing recent findings conducted with improved modern methods in this subject area for higher sustainability and efficiency. Under the research background of marine coating processes, Ye et al [29] investigated the effects of shoreline winds and spraying distance on the TE and film thickness distribution during airless spraying on ship painting, based on a Euler-Lagrange approach, as is shown in Figure 5.…”

Section: Research On Film Formation Characteristics and Mechanismsmentioning

confidence: 99%

A Review of the Developments of the Characteristics and Mechanisms of Airless Spraying on Complex Surfaces

Wu,

Chen,

Liu

et al. 2023

Coatings

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The special surface appearance of complex surfaces restricts the coating film quality of spraying. The study of the atomization and film formation characteristics of typical complex surfaces, as well as the spraying mechanism, is essential for planning the spraying robotic trajectory and improving the spraying efficiency. In this paper, modeling and characteristics of the atomization and film formation process, based on CFD numerical simulations in previous studies, are systematically reviewed, focusing especially on airless spraying. In addition, the advantages and disadvantages of the existing research from the perspective of numerical models and methods are discussed. Finally, a further research direction for spraying on complex surface is prospected. Overall, a comprehensive and up-to-date review of spray atomization and film formation characteristics is considered valuable to practitioners and researchers in these fields, and will facilitate the further application of robotic spraying in the mechanical, automotive, marine, aerospace, petrochemical and other industries.

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Section: Research On Film Formation Characteristics and Mechanismsmentioning

confidence: 99%

A Review of the Developments of the Characteristics and Mechanisms of Airless Spraying on Complex Surfaces

Wu,

Chen,

Liu

et al. 2023

Coatings

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“…Balancing improved productivity with the achievement of high-quality spraying operations is crucial in the realm of robot spraying. Key evaluation indices for spraying quality primarily encompass the paint layer thickness and surface roughness of the sprayed components [8]. The current focal point of research in the robotic spraying process direction lies in establishing the role of the spraying parameter model and integrating spraying process modeling with spraying trajectory planning.…”

Section: Introductionmentioning

confidence: 99%

Research on Spraying Quality Prediction Algorithm for Automated Robot Spraying Based on KHPO-ELM Neural Network

Ling,

Zhang,

et al. 2024

Machines

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In the intelligent transformation of spraying operations, the investigation into the robotic spraying process holds significant importance. The spraying process, however, falls within the realm of experience-driven technology, characterized by high complexity, diverse parameters, and coupling effects. Moreover, the quality of manual spraying processes relies entirely on manual experience. Thus, the crux of the intelligent transformation of spraying robots lies in establishing a mapping model between the spraying process and the resultant spraying quality. To address the challenge of intelligently transforming empirical spraying processes and achieving the mapping from the spraying process to spraying quality, an algorithm employing an enhanced extreme learning machine-based neural network is proposed for predicting spraying process parameters with respect to the evaluation index of spraying quality. In this approach, an algorithmic model based on the Extreme Learning Machine (ELM) neural network is initially constructed utilizing five spraying process parameters: spraying speed, spraying height, spraying width pressure, atomization pressure, and oil spraying pressure. Two spraying quality evaluation indexes, namely average film thickness at the center point and surface roughness, are also incorporated. Subsequently, the prediction neural network is optimized using the K-means improved predator optimization algorithm (KHPO) to enhance the model’s prediction accuracy. This optimization step aims to improve the efficiency of the model in predicting spraying quality based on the specified process parameters. Finally, data collection and model validation for the spraying quality prediction algorithm are conducted using a designed robotic automated waterborne paint spraying experimental system. The experimental results demonstrate a significant reduction in the prediction error of the KHPO-ELM neural network model for the average film thickness center point, showcasing a decrease of 61.95% in comparison to the traditional ELM neural network and 50.81% in comparison to the BP neural network. Likewise, the improved neural network model yields a 2.31% decrease in surface roughness prediction error compared to the traditional ELM neural network and a substantial 54.0% reduction compared to the BP neural network. Consequently, the KHPO-ELM neural network, incorporating the prediction algorithm, effectively facilitates the prediction of multi-spraying process parameters for the center point of average film thickness and surface roughness in automated robot spraying. Notably, the prediction algorithm exhibits a commendable level of accuracy in these predictions.

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“…Any industry that requires drying, polymerization, or curing in manufacturing processes can use infrared radiation to speed up the process [10]. Currently, IR radiation is often used in the automotive industry to speed up the coating drying process, but more and more industries are turning to this solution [11]. For the industry, the simplest transition to this new technology is to apply IR radiation for drying coatings they already use.…”

Section: Introductionmentioning

confidence: 99%

Experimental Study of Atmospherically and Infrared-Dried Industrial Topcoats

Stojanović,

Logar,

Fatović

et al. 2023

Coatings

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In this paper, five different solvent-borne industrial topcoats were dried with infrared (IR) radiation and under atmospheric conditions. A comparison of physical, mechanical, chemical, and electrochemical properties of differently dried topcoats was made. The results of differential scanning calorimetry (DSC), Fourier-Transform Infrared Spectroscopy (FTIR), and adhesion of a topcoat to the metal substrate (determined by the pull-off test) indicate a higher degree of crosslinking of examined topcoats, which improves the coating’s protective properties. Scratch hardness was determined by the pencil hardness test. Impact resistance was examined with a falling-weight test. Changes in the shade of the coating were examined by visual inspection and using a gloss meter. The electrochemical measurements of open circuit potential (OCP) and electrochemical impedance spectroscopy (EIS) were conducted. The thermal stability of topcoats was tested by thermogravimetric analysis (TGA). The results show overall better properties of IR-dried topcoats. In addition, topcoats dry significantly faster when IR radiation is applied, which makes this drying method very interesting for industrial application.

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Review of coating and curing processes: Evaluation in automotive industry

Cited by 20 publications

References 199 publications

A Review of the Developments of the Characteristics and Mechanisms of Airless Spraying on Complex Surfaces

A Review of the Developments of the Characteristics and Mechanisms of Airless Spraying on Complex Surfaces

Research on Spraying Quality Prediction Algorithm for Automated Robot Spraying Based on KHPO-ELM Neural Network

Experimental Study of Atmospherically and Infrared-Dried Industrial Topcoats

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