Process modeling and parameter optimization of surface coatings using artificial neural networks (ANNs): State-of-the-art review

Paturi, Uma Maheshwera Reddy; Cheruku, Suryapavan; Geereddy, Satwik Reddy

doi:10.1016/j.matpr.2020.08.695

Cited by 20 publications

(7 citation statements)

References 109 publications

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“…They reviewed the processing with ANNs which can control coating thicknesses, hardness, microstructure and particles, tribological properties, roughness, amphiphobic surface properties, and so on. 235) The perspective visions written by Mauer and Moreau are that monitoring and controlling thermal spray processes are extremely challenging since these processes involve a large number of variables, some of them not being well-controlled such as the electrode wear. From this view, future thermal spray production units could comprise a series of process diagnostic tools for monitoring on-line key extrinsic and intrinsic spray parameters.…”

Section: Spray Coatingmentioning

confidence: 99%

Science-based, data-driven developments in plasma processing for material synthesis and device-integration technologies

Kambara

Kawaguchi

Lee

et al. 2022

Jpn. J. Appl. Phys.

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Low-temperature plasma processing technologies is essential for material synthesis, device fabrication, and surface treatment. The development of plasma-related products and services requires an understanding of the multiscale complex behaviors of plasma and the hierarchical integration of plasma generation, energy and mass transports through sheath region, surface reactions, and other processes. The importance of science-based and data-driven approaches to controlling systems is argued. The state-of-the-art of deep learning, machine learning, and artificial intelligence in low-temperature plasma science and technology is reviewed. In this review, the requirements and challenges for plasma parameter prediction and processing recipe discovery are asserted by researchers in the fields of material science and plasma processing. It also outlined a science-based, data-driven approach for development of virtual metrology in plasma processes.

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Section: Spray Coatingmentioning

confidence: 99%

Science-based, data-driven developments in plasma processing for material synthesis and device-integration technologies

Kambara

Kawaguchi

Lee

et al. 2022

Jpn. J. Appl. Phys.

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“…Consequently, utilizing field process parameter data and establishing a neural network-based prediction algorithm model incorporating multiple spraying process parameters emerges as the optimal solution to address the complexities of the field environment, offering high robustness, adaptability, and rapid iteration capabilities. This approach using neural networks for predicting and analyzing the quality of the spraying process presents advantages that the simulation model research method lacks [15].…”

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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“…31 Surface coating is widely used to optimize the surface morphology, wear resistance, and fatigue strength of the substrate without changing its properties. 32 As a typical surface coating material, cermet performs well in machining, hydraulic and other fields. Hong 33 and Lee and Hong 34 were the first to apply cermet coating on the cylinder block/valve plate interface to study its tribological properties at low rotational speed.…”

Section: Introductionmentioning

confidence: 99%

Tribological characteristics of hard-to-hard matching materials of cylinder block/valve plate interface in electro-hydrostatic actuator pumps

Fu,

Lyu,

Kang

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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Axial piston pumps, serving as the essential power component in electro-hydrostatic actuators (EHA), play a crucial role in achieving advanced actuation by ensuring high efficiency and reliability. The cylinder block/valve plate interface, which is the greatest contact area inside the axial piston pump, significantly affects both leakage and energy dissipation. The interface experiences significant friction loss and severe wear in aerospace applications, primarily due to the rapidly changing mixed and boundary lubrication conditions under a wide range of speed and pressure. Therefore, to improve the efficiency and reliability of electro-hydrostatic actuators, strengthening the cylinder block/valve plate interface is a key problem to overcome. Previous studies have demonstrated that the utilization of soft-to-hard matching materials enhances the friction characteristics of the interface, but it also results in relatively severe wear on the soft material. Therefore, in this study, we propose the concept of employing hard-to-hard matching materials as a strategy to mitigate wear in axial piston pumps. An optimized friction simulation test apparatus is adopted, which contains a simulated piston structure to take the piston stirring effect into consideration. Experimental results show that the implementation of hard-to-hard materials leads to a substantial improvement in the wear resistance of the interface under a wide range of rotational speed (300–6000 rpm). The wear depths of the TiAlN/TiAlN (hard-to-hard) matching material are only 28.6%~34.7% of the tin bronze/TiAlN (soft-to-hard) matching material. Besides, compared with traditional tin bronze/nitrided (soft-to-hard) matching material, the hard-to-hard matching materials improve both friction and wear performance. The hard-to-hard matching materials provide a novel approach to reinforce the cylinder block/valve plate interface, and thus to promote volumetric efficiency and extend the service life of the axial piston pump.

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Process modeling and parameter optimization of surface coatings using artificial neural networks (ANNs): State-of-the-art review

Cited by 20 publications

References 109 publications

Science-based, data-driven developments in plasma processing for material synthesis and device-integration technologies

Science-based, data-driven developments in plasma processing for material synthesis and device-integration technologies

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

Tribological characteristics of hard-to-hard matching materials of cylinder block/valve plate interface in electro-hydrostatic actuator pumps

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