A general-purpose spray coating deposition software simulator

Tzinava, Maria; Delibasis, Konstantinos K.; Allcock, Bryan; Kamnis, Spyros

doi:10.1016/j.surfcoat.2020.126148

Cited by 18 publications

(2 citation statements)

References 22 publications

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“…New capabilities powered by modern ML techniques, allow for combined real-time data, physical dependency models and intelligence from different platforms. The ability to simulate, measure, predict and improve assets plays a vital role in smart factories of the future [178]. Next generation AE signal processing technologies are expected to drive continuous product improvement and profitability by identifying gaps in performance, diagnosing deficiencies, correcting and reversing negative trends, reducing cost, improving yields, and maintaining equipment reliability.…”

Section: Discussionmentioning

confidence: 99%

Roadmap on signal processing for next generation measurement systems

Iakovidis

Ooi

Kuang

et al. 2021

Meas. Sci. Technol.

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Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.

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Section: Discussionmentioning

confidence: 99%

Roadmap on signal processing for next generation measurement systems

Iakovidis

Ooi

Kuang

et al. 2021

Meas. Sci. Technol.

Self Cite

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“…Importantly, unlike the single-track modelling case, a data-driven overlapping-track model should incorporate the explicit information of a depositing surface to account for the interaction with the existing deposition [23][24][25]. This explicit approach is particularly important because deposition onto complex, non-planar surfaces is frequently seen in practice (e.g., repair applications [26,27]), and the learning capability of data-driven modelling can handle such complex and various deposition scenarios as new scenarios are encountered in manufacturing processes. Another aspect needed for a data-driven overlapping-track model is the greater level of physical insights observed in the mathematical counterparts (e.g., Gaussian distribution of jetted powder in CSAM [12][13][14]).…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Overlapping Track Profile Modelling in Cold Spray Additive Manufacturing

Ikeuchi

Vargas-Uscategui

King

et al. 2023

International Thermal Spray Conference

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Cold spray additive manufacturing is an emerging solid-state deposition process that enables large-scale components to be manufactured at high production rates. Control over geometry is important for reducing the development and growth of defects during the 3D build process and improving the final dimensional accuracy and quality of components. To this end, a machine learning approach has recently gained interest in modelling additively manufactured geometry; however, such a data-driven modelling framework lacks the explicit consideration of a depositing surface and domain knowledge in cold spray additive manufacturing. Therefore, this study presents surface-aware data-driven modelling of an overlapping-track profile using a Gaussian Process Regression model. The proposed Gaussian Process modelling framework explicitly incorporated two relevant geometric features (i.e., surface type and polar length from the nozzle exit to the surface) and a widely adopted Gaussian superposing model as prior domain knowledge in the form of an explicit mean function. It was shown that the proposed model is able to provide better predictive performance than the Gaussian superposing model alone and purely data-driven Gaussian Process model, providing consistent overlapping-track profile predictions at all overlapping ratios. By combining accurate prediction of track geometry with toolpath planning, it is anticipated that improved geometric control and product quality can be achieved in cold spray additive manufacturing.

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