مقایسه روش های رگرسیون، کلونی زنبورعسل و شبکه عصبی مصنوعی در پیش بینی پرشدگی قالب در مهرزنی صفحات دوقطبی پیل سوختی

نوع مقاله : مقاله پژوهشی

نویسندگان

وحید مدانلو

احمد مشایخی

بهنام اخوندی

دانشکده مهندسی مکانیک، دانشگاه صنعتی سیرجان، سیرجان، ایران

10.22067/jacsm.2023.84567.1205

چکیده

افزایش عمق میکروکانال‌های صفحات دوقطبی فلزی در پیل های سوختی غشاء پروتون منجر به افزایش بازده خواهد شد. در این پژوهش، از فرآیند مهرزنی برای ساخت صفحات دوقطبی از جنس تیتانیوم خالص تجاری با الگوی شیاری موازی مستقیم استفاده‌شده است. تأثیر پارامترهای فرآیند شامل لقی قالب، سرعت شکل دهی و ضریب اصطکاک بین ورق و قالب بر روی نرخ پرشدگی و میزان نازک شدگی صفحات دوقطبی بررسی شد. آزمایش های لازم با استفاده از روش رویه پاسخ طراحی، با استفاده از مدل اجزای محدود صحت سنجی شده اجرا و خروجی های موردنظر استخراج شدند. سپس با استفاده از روش رگرسیون، یک معادله درجه دوم برای پیش بینی نرخ پرشدگی بر اساس پارامترهای ورودی ارائه شد. در ادامه با استفاده از الگوریتم کلونی زنبورعسل مصنوعی، ضرایب معادله مذکور بهبودیافته و میزان خطای آن در حدود 53% کاهش یافت. در پایان نیز از یک شبکه عصبی مصنوعی برای پیش بینی نرخ پرشدگی استفاده شد. نتایج نشان داد که شبکه عصبی پیشنهادی بسیار مؤثر بوده و با دقت بسیار بالایی نرخ پرشدگی میکروکانال را تقریب می زند.

کلیدواژه‌ها

صفحات دوقطبی تیتانیومی

عمق شکلدهی

فرآیند مهرزنی

الگوریتم کلونی زنبورعسل مصنوعی

شبکه عصبی مصنوعی

موضوعات

• شکل دهی

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عنوان مقاله English

Comparison of regression, bee colony, and artificial neural network for predicting die filling in stamping of bipolar plates fuel cell

نویسندگان English

Vahid Modanloo

Ahmad Mashayekhi

Behnam Akhoundi

Mechanical Engineering Department, Sirjan University of Technology, Sirjan, Iran

چکیده English

Increasing the depth of microchannels on metallic bipolar plates (BPPs) in PEM fuel cells leads to an increase in the efficiency. In this research, the stamping process has been applied for manufacturing of the BPPs made of commercially pure titanium with a direct parallel flow field. The effect of process parameters including die clearance, forming speed, and sheet/die friction coefficient on the filling rate and thinning of the BPPs was investigated. The required tests were designed via the response surface method (RSM), implemented by a validated finite elements (FE) model, and the desired outputs were extracted. Then, a quadratic equation was presented for predicting the filling rate based on the input parameters using the regression method. In the following, using the artificial bee colony algorithm, the coefficients of the mentioned equation were enhanced and its error was decreased almost by 53%. Finally, an artificial neural network (ANN) was used to predict the filling rate. The results demonstrated that the proposed ANN model is very effective and approximates the filling rate of the microchannel with high accuracy.

کلیدواژه‌ها English

Titanium bipolar plates

Forming depth

Stamping process

Artificial bee colony algorithm

Artificial neural network

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