説明:
(abstract)Electrodeposition-based metal coating techniques are used to manufacture various industrial products and rely on the quantitative control of the physical properties of the coating layers, such as electrical conductivity, surface roughness, and hardness. To clarify the experimental conditions required to realize the desired physical properties of metal coating layers and shed light on the complex mechanism of the involved reactions, we prepared a custom-built experimental dataset (60 conditions) on the surface roughness of electrodeposited thin copper films and submitted it to an open-access data repository. Data-driven analysis revealed that surface roughness is strongly affected by the deposition temperature, current, and interelectrode distance.
権利情報:
キーワード: electrodeposited copper film, surface roughness, machine learning
刊行年月日: 2024-12-31
出版者: Taylor & Francis
掲載誌:
研究助成金:
原稿種別: 出版者版 (Version of record)
MDR DOI:
公開URL: https://doi.org/10.1080/27660400.2024.2416889
関連資料:
その他の識別子:
連絡先:
更新時刻: 2024-10-31 16:30:15 +0900
MDRでの公開時刻: 2024-10-31 16:30:16 +0900
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Predicting the surface roughness of an electrodeposited copper film using a machine learning technique.pdf
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