عوامل تعیین‏کنندة مدول الاستیسیته و مدول گسیختگی تختة خرده‌چوب بر اساس داده‏های اطلاعات پایه

نوع مقاله: مقاله پژوهشی

نویسنده

دانشیار گروه علوم و صنایع چوب و کاغذ، دانشکدة منابع طبیعی، دانشگاه زابل، زابل، ایران

چکیده

مقدار مدول الاستیسیته و گسیختگی تختة خرده‌چوب بر اساس عوامل تولیدی کنترل می‌شود. حال سؤال اساسی این است که عوامل تعیین‌کنندة خواص خمشی تختة خرده‌چوب کدام‌اند؟ داده‏های پایة این تحقیق شامل 13 متغیر مشترک اندازه‌گیری‌شده با 100 تکرار در منابع علمی معتبر داخلی است. روش‌های مدل‏سازی خطی و مدل‏سازی غیر‌خطی آزمون گاما، ام، و الگوریتم ژنتیک و شبکة عصبی برای آزمون سؤال استفاده شد. عوامل تعیین‌کنندة خواص خمشی تختة خرده‌چوب شاملِ 1. نوع مواد مصرفی، 2. جرم مخصوص خشک مواد اولیه با میانگین‌گیری ساده، 3. جرم مخصوص خشک مواد اولیه با میانگین‌گیری وزنی، 4. مقدار درصد چسب اورة فرمالدئید، 5. جرم مخصوص تختة تولیدی، 6. زمان پرس، 7. دمای پرس، و 8. فشار پرس‌ هستند و به غیر از این عوامل، ضخامت تختة تولیدی برای مدول گسیختگی و نیز درصد اختلاط مواد چوبی و منابع لیگنوسلولزی غیر‌چوبی و مقدار درصد کلرید آمونیوم برای مدول الاستیسیته مهم‌اند. مدل‏های الگوریتم ژنتیک و شبکة عصبی نشان می‏دهد که عوامل تعیین‌کنندة مذکور مخصوصاً جرم مخصوص خشک مواد اولیه با میانگین‌گیری وزنی برای مدول گسیختگی و درصد چسب برای مدول الاستیسیته قابلیت کنترل کیفی تختة خرده‌چوب را دارند و برخی عوامل دیگر را می‏توان ثابت در نظر گرفت. درصد مطلق خطای مدل پیش‌بینی شبکة عصبی برای مدول گسیختگی برابر 644/5 و برای مدول الاستیسیته برابر 91/4 است.

کلیدواژه‌ها


عنوان مقاله [English]

Determinants of Modulus of Rupture and Modulus of Elasticity of Particleboards on the basis of Data base

نویسنده [English]

  • Ali Bayatkashkoli
Associate Professor, Paper and Wood Technology and Sciences Department, Natural Resources Faculty, University of Zabol, Zabol, Iran
چکیده [English]

The MOE and MOR are controlled by production variables of particleboard process. Now, the basic question is which of the particleboard variables is effective on bending strength property? 13 variables of internal scientific resources were measured with 100 repeats. The study steps include the following; liner regression or stepwise, Genetic algorithm, and Artificial Neural Network. The number of effective variables was selected from the output of the stepwise procedure and then modeling of these variables using WinGamma and Mathlab. The number of effective variables in the modulus of elasticity and modulus of rupture are equal to 10 and 9, respectively. As the results of Gamma test shows, effective variables of bending strength are as following: 1. Type of wooden raw material, 2. specific gravity of raw material by simple averaging, 3. specific gravity of raw material by weighted averaging, 4. UF percent, 5. particleboard density, 6,7, and 8. Time, tempreture, and pressure of press. In the other hand, particleboard thickness of MOR and also, percent mixture of wood and non-wood lignocellulosic materials and NH4CL percent for MOE are important. Results of Genetic algorithm and Neural Network were showed that some variables can be kept constant and particleboard properties are controlled by these effective variables, but specific gravity of raw material by weighted averaging for MOR and UF percent of MOE have the strongest effect. Result of BFGS Neural Network has shown that mean absolute percent error of MOR and MOE are equal 5.644% and 4.91%, respectively.

کلیدواژه‌ها [English]

  • Artificial Neural Network
  • Gamma test
  • Genetic Algorithm
  • MOE
  • MOR
  • Particleboard
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