借助人工智能开发镍-B 涂层

IF 2.7 4区 材料科学 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY
Subhash Kumar, Arun Kumar Kadian, Mukesh Sharma, Anil C. Mahato, Arkadeb Mukhopadhyay
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引用次数: 0

摘要

据报道,在不同的研究工作中,不同的镀液成分会产生具有优异耐磨性的无电解镍-B 镀层。如果能建立一个数据库,使用人工智能(AI)对其进行训练和优化,就能为科学界和工业界提供一个与镀液参数和涂层性能相关的现成模型。在这项工作中,我们努力列出了一些工作中报告的成分,使用人工神经网络(ANN)对其进行了训练,并使用非优势排序遗传算法(NSGA)找到了可提高涂层沉积和硬度的最佳条件。预测的镀层与实验结果具有良好的相关性(R2 ∼ 1)。在非晶态结构中出现了常见的结节形态。沉积硬度为 950-1075 HV0.1,沉积速率为 10 µm/h ,第一临界破坏载荷 (Lc) 为 24 N。因此,这项工作可作为预测涂层质量的初步模型,而无需进行大量实验,从而节省了成本和时间,并具有很高的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of Ni-B coating through the aid of artificial intelligence
Electroless Ni-B coatings with excellent wear resistance have been reported by widely varying bath compositions in different research works. If a database can be made, trained using artificial intelligence (AI) and optimized, a readily available model with correlation of bath parameters and coating properties would be available for the scientific community and industries. In this work, an effort has been made to list the composition reported in some works, train them using artificial neural network (ANN) and find an optimal condition leading to higher deposition and hardness of the coatings using non-dominated sorting genetic algorithm (NSGA). The predicted bath has good correlation with experimental results (R2 ∼ 1). The usual nodular morphology was seen with amorphous structure. A high as-deposited hardness of 950–1075 HV0.1, ∼10 µm/h deposition rate and first critical load of failure (Lc) > 24 N was detected. Thus, this work serves as a preliminary model for predicting coating with enhanced quality without having to perform numerous experiments thereby saving cost and time with substantial accuracy.
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来源期刊
Materials Letters
Materials Letters 工程技术-材料科学:综合
CiteScore
5.60
自引率
3.30%
发文量
1948
审稿时长
50 days
期刊介绍: Materials Letters has an open access mirror journal Materials Letters: X, sharing the same aims and scope, editorial team, submission system and rigorous peer review. Materials Letters is dedicated to publishing novel, cutting edge reports of broad interest to the materials community. The journal provides a forum for materials scientists and engineers, physicists, and chemists to rapidly communicate on the most important topics in the field of materials. Contributions include, but are not limited to, a variety of topics such as: • Materials - Metals and alloys, amorphous solids, ceramics, composites, polymers, semiconductors • Applications - Structural, opto-electronic, magnetic, medical, MEMS, sensors, smart • Characterization - Analytical, microscopy, scanning probes, nanoscopic, optical, electrical, magnetic, acoustic, spectroscopic, diffraction • Novel Materials - Micro and nanostructures (nanowires, nanotubes, nanoparticles), nanocomposites, thin films, superlattices, quantum dots. • Processing - Crystal growth, thin film processing, sol-gel processing, mechanical processing, assembly, nanocrystalline processing. • Properties - Mechanical, magnetic, optical, electrical, ferroelectric, thermal, interfacial, transport, thermodynamic • Synthesis - Quenching, solid state, solidification, solution synthesis, vapor deposition, high pressure, explosive
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