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Research on Rock Powder Coating Ratio based on Artificial Neural Network

Published: 16 April 2024 Publication History

Abstract

Stone powder coating as a mixture of spraying materials has appeared more and more in practical engineering, but there are still problems in working performance, mechanical properties and durability in the process of utilization. Existing investigations are concentrated on certain ratio of rock powder, which is unreasonable and the performance of powder coating is insufficient. In this work, we propose a novel artificial neural network to train the historical performance data from the rock powder coating ratio, which can assist the neural cell to obtain the diverse information form these ratio and corresponding performances. After training the model, the neural network can provide the reasonable ratio for rock powder to reach the best painting performance. From our extensive simulation results, we can significantly observe that the model can automatically provide the optimal powder ratio when provide novel materials and obtain the sufficient hardness performances in the realistic applications.

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    ICMLCA '23: Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application
    October 2023
    1065 pages
    ISBN:9798400709449
    DOI:10.1145/3650215
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 16 April 2024

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