نشریه فناوری آزمون‌های غیرمخرب

نشریه فناوری آزمون‌های غیرمخرب

طراحی مدل ترکیبی داده‌محور و طبقه‌بندی جهت انتخاب روش بهینه آزمون‌های غیرمخرب در بازرسی جوش

نوع مقاله : مقاله پژوهشی

نویسندگان
1 گروه مهندسی صنایع، دانشکده فنی و مهندسی، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران،
2 گروه علوم و مهندسی آب، دانشکده موضوعی کشاورزی، آب، غذا و فراسودمندها، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.
چکیده
انتخاب روش مناسب آزمون غیرمخرب (NDT) برای بازرسی جوش، نقش مهمی در ارتقاء کیفیت فرایند بازرسی، کاهش خطاهای انسانی و بهینه‌سازی هزینه‌های عملیاتی در صنایع مختلف از جمله انرژی، حمل‌ونقل، خودروسازی، هوافضا و نفت و گاز دارد. در این پژوهش، با بهره‌گیری از رویکرد یادگیری ماشین که یکی از اجزاء هوش مصنوعی است، یک مدل ترکیبی داده‌محور و طبقه‌بندی طراحی شده است که توانایی پیش‌بینی روش بهینه آزمون غیرمخرب (NDT) را برای هر نمونه جوش، بر اساس مجموعه‌ای از ویژگی‌های فنی نظیر نوع جوش، ضخامت، جنس فلز پایه، ساختار قطعه و میزان دسترسی به ناحیه جوش دارد. برای طراحی این مدل، تعداد ۵۰۰ رکورد داده معتبر شامل نتایج آزمون‌های رایج مانند UT، MT، RT، PT و ET گردآوری شده و پس از انجام مراحل پیش‌پردازش، از جمله بررسی داده‌های گمشده، نرمال‌سازی و کدگذاری متغیرهای طبقه‌ای، از الگوریتم‌های متنوع شامل جنگل تصادفی (RF)، ماشین بردار پشتیبان (SVM)، نزدیک‌ترین همسایه (KNN) و XGBoost در محیط برنامه‌نویسی Python برای مدل‌سازی استفاده شد. فرایند ارزیابی مدل‌ها با بهره‌گیری از تکنیک اعتبارسنجی متقابل ۱۰ لایه‌ای (10-fold cross-validation) و تنظیم بهینه فراپارامترها از طریق روش جستجوی شبکه‌ای (GridSearchCV) انجام گرفت. نتایج نشان داد مدل XGBoost با دقت ۹۰ درصد و میانگین شاخص‌های صحت، پوشش و امتیاز F1 بالاتر از سایر مدل‌ها، عملکرد قابل توجهی داشته و در برخی موارد، حتی از پیش‌بینی‌های کارشناسان انسانی نیز دقیق‌تر عمل کرده است. همچنین، خروجی مدل با استانداردهای صنعتی نظیر ASME Section V و ASTM E-Series در زمینه بازرسی جوش کاملاً هم‌راستا بوده و دارای اعتبار و کاربردپذیری عملی بالایی است.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Design of a Hybrid Data-Driven Classification Model for Optimal Selection of Non-Destructive Testing Methods in Weld Inspection

نویسندگان English

Amirmahdi Nazari Gharibdoosti 1
Mahdi Sarai Tabrizi 2
1 Department of Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran.
2 Department of Water Engineering and Sciences, Islamic Azad University, Science and Research Branch, Tehran, Iran.
چکیده English

Selecting the most suitable Non-Destructive Testing (NDT) method in industries such as energy, transportation, automotive, aerospace, and oil and gas significantly contributes to quality improvement, minimizing human errors, and reducing operational costs. In this study, a smart, data-driven classification model is developed using Machine Learning (ML) techniques to recommend the most appropriate Non-Destructive Testing method for weld inspection, based on technical parameters such as weld type, thickness, base material, structural complexity, and accessibility to the weld area. A dataset containing 500 real-world Non-Destructive Testing records, including Ultrasonic Testing (UT), Eddy Current Testing (ET), Magnetic Particle Testing (MT), Radiographic Testing (RT), and Liquid Penetrant Testing (PT), was collected and preprocessed through steps such as normalization, encoding of categorical features, and missing value handling. To evaluate the model, four widely used classification algorithms—Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGBoost)—were implemented in Python. Model performance was assessed using 10-fold cross-validation to ensure robustness and generalization, and hyperparameters were tuned via a grid search strategy for optimal configuration. The results demonstrated that the XGBoost model outperformed the other classifiers, achieving 90% overall accuracy along with superior precision, recall, and F1-score values. Moreover, the model exceeded the average performance of human experts by approximately six percent in predicting the correct Non-Destructive Testing method for samples with different material types, geometries, and weld configurations. The model’s predictions were consistent with international standards such as ASME Section V (2023) and ASTM E-Series, confirming its technical reliability and compliance. The proposed approach integrates both data-driven insights and the predictive power of classification algorithms, making it an effective and practical decision-support tool for selecting Non-Destructive Testing methods in welding applications. It enhances inspection planning, reduces variability caused by human judgment, and ensures more consistent and reliable assessments across different inspection scenarios.

کلیدواژه‌ها English

Non-Destructive Inspection
Weld Inspection
Machine Learning
XGBoost
Data-Driven Modeling
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  • تاریخ دریافت 06 دی 1403
  • تاریخ بازنگری 22 اردیبهشت 1404
  • تاریخ پذیرش 09 خرداد 1404