تولید سطوح آبگریز با پوشش‌دهی اکسیدقلع بر روی پایه آلومینیم: مدل‌سازی و تحلیل با استفاده از شبکه عصبی مصنوعی

نوع مقاله : مقاله پژوهشی

نویسندگان

دانشکده مهندسی شیمی، نفت و گاز، دانشگاه علم و صنعت ایران، تهران، ایران

چکیده

در این پژوهش، بذرلایه‌های مختلف با روش غوطه‌وری بر روی پایه آلومینیم کشت شدند. لایه نشانی حمام شیمیایی نیز برای پوشش‌دهی اکسید قلع بر روی بذرها مورد استفاده قرارگرفته است. 18 آزمایش تجربی با استفاده از طراحی آزمایش تاگوچی طرح ریزی گردید. با توجه به نتایج، از بین نوع فعال سطح و غلظت فعال سطح در بذرلایه، تعداد لایه‌های بذرلایه، جنس بذرلایه و غلظت پیش‌ماده، غلظت فعال سطح بیشترین تاثیر را بر روی ترشوندگی سطح داشته است. همچنین برای مدل‌سازی و بهینه‌سازی فرآیند از شبکه عصبی پرسپترون چند لایه استفاده شده است. شبکه عصبی بهینه با 4 نورون در لایه میانی اول و 5 نورون در لایه میانی دوم استخراج شد. پیش‌بینی زاویه تماس قطره آب در نقطه بهینه (بیشترین آبگریزی) با استفاده از طراحی تاگوچی حدود ° 142 و با استفاده از شبکه عصبی مصنوعی حدود ° 132 به دست آمد. مقدار تجربی بدست آمده برای آن نیز ° 137 می­باشد که نشان دهنده حدود 3.5 % خطای پیش‌بینی می­باشد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Hydrophobic Surface Fabrication with Tin Oxide on Al-substrate: Modelling and Assessment Via Artificial Neural Network

نویسندگان [English]

  • S. M. Sadati Tilebon
  • R. Norouzbeigi
Faculty of Chemical Engineering, Oil and Gas, Iran University of Science and Technology, Tehran, Iran.
چکیده [English]

Surface quality is a key point for expressing the specifications of tools that these specifications are improvable by coating methods. In this study, seed layers was deposited on Al - substrate by dip coating process and chemical bath deposition was used for tin oxide coating over deposited seed layers. Taguchi L18 was utilized for designing the experiments and preparation of required samples for studying the effective parameters (surfactant type and concentration in seed layer, seed layer deposition cycles, and seed layer nature and concentration of precursors). Furthermore, surfactant concentration showed the highest effect on surface wettability. In addition, artificial neural network (ANN) was used for modelling and optimization of process. A multilayer perceptron (MLP) ANN with 4 and 5 neurons in the first hidden layer and second hidden layer, respectively, was obtained as the best network. Optimum point prediction of 142° and 132° was calculated by Taguchi design and ANN modelling, respectively. However, prediction error of 3.5% was calculated comparing with experimental results (137°). 

کلیدواژه‌ها [English]

  • Hydrophobic surface
  • Tin oxide
  • Seed layer
  • Taguchi design
  • Artificial Neural Network
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