کاربست فراترکیب (متاسنتز) در شناسایی روش‌های الگوریتم‌های مبتنی بر داده جهت عیب یابی فرآیند تولید پلی پروپیلن
محورهای موضوعی : مدیریت صنعتیسلیمان گل پور کنده 1 , رضا رمضانی خورشید دوست 2 , محمدرضا کاباران زاده قدیم 3
1 - دانشجوی دکتری، گروه مدیریت صنعتی، واحد تهران مرکز، دانشگاه آزاد اسلامی، تهران، ایران
2 - استادیار، گروه مهندسی صنایع و سیستم های مدیریت، دانشگاه صنعتی امیرکبیر، تهران، ایران
3 - دانشیار، گروه مدیریت، واحد تهران مرکز،دانشگاه ازاد اسلامی، تهران، ایران
کلید واژه: الگوریتم‌های مبتنی بر داده, هوش مصنوعی, عیب یابی فرآیند تولید پلی پروپلین, متاسنتز (فراترکیب).,
چکیده مقاله :
این پژوهش به دنبال شناسایی روش‌های الگوریتم‌های مبتنی بر داده جهت عیب یابی فرآیند تولید پلی پروپلین بوده است. محقق با استفاده از رویکرد مرور نظام‌مند و فراترکیب، به تحلیل نتایج و یافتههای محققین قبلی پرداخت و با انجام گام‌های 7 گانه روش ساندلوسکی و باروسو، به شناسایی عوامل مؤثر پرداخته است. از بین 500 مقاله، 34 مقاله بر اساس روش CASP انتخاب شد. در این زمینه به‌منظور سنجش پایایی و کنترل کیفیت، از شاخص کاپا برای ارزیابی شاخصهای شناسایی‌شده استفاده شد که مقدار آن برای شاخصهای شناسایی‌شده در سطح توافق عالی تشخیص داده شد. نتایج حاصل از تحلیل داده‌های گرداوری شده در نرم‌افزار ATLAS TI تحلیل شدند. بر اساس کدگذاری انجام‌شده، 9 الگوریتم و تکنیک‌های مرتبط آنها شناسایی شدند. الگوریتم‌های شناسایی‌شده عبارت‌اند از: مدل‌های ARIMA، الگوریتم‌های مبتنی بر دسته بندی، درختان تصمیم، شبکه‌های پویا بیز، مدل‌های هیبرید، الگوریتم‌های مبتنی بر نمونه، مدل‌های متغیر پنهان، شبکه‌های هوش مصنوعی و مدل‌های مبتنی بر قانون. حدود 84 درصد از مطالعات انتخاب شده از تکنیک‌های یادگیری ماشین متعلق به یکی از چهار دسته استفاده کردند: درخت‌های تصمیم، شبکه‌های عصبی مصنوعی، مدل‌های ترکیبی و مدل‌های متغیر پنهان. راندمان محاسباتی نیز به عنوان یک مزیت مهم برای الگوریتم‌های یادگیری ماشین دیده می‌شود.
This study aimed to identify data-based algorithmic methods for troubleshooting the polypropylene production process. Employing a systematic review approach, we analyzed the findings of prior researchers and identified effective factors using the Seven-step method of Sandelowski and Barroso. Out of 500 articles, 34 were selected using the CASP method. To assess reliability and quality control, we utilized the Kappa index, which demonstrated excellent agreement for the identified indicators. Data analysis was conducted using ATLAS TI software, leading to the identification of 9 algorithms and their associated techniques, including ARIMA models, classification-based algorithms, decision trees, dynamic Bayesian networks, hybrid models, sample-based algorithms, hidden variable models, artificial intelligence networks, and rule-based models. Notably, 84% of the selected studies employed machine learning techniques falling into one of four categories: decision trees, artificial neural networks, hybrid models, and latent variable models. Furthermore, computational efficiency is recognized as a significant advantage for machine learning algorithms.
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