مهندسی ساخت و تولید ایران

مهندسی ساخت و تولید ایران

اجرای الگوریتم‌ یادگیری ماشین پیش‌بینی استحکام کششی و مدول یانگ قطعات ساخت افزودنی بر پایه اکستروژن ذوبی مواد

نوع مقاله : مقاله پژوهشی

نویسنده
پژوهشکده مهندسی مکانیک، پژوهشگاه فناوری‌های نوین، سازمان پژوهش‌های علمی و صنعتی ایران، تهران، ایران
10.22034/ijme.2025.551862.2139
چکیده
فناوری‌ ساخت افزودنی، هوش مصنوعی و الگوریتم‌های یادگیری ماشین از زمینه‌های فنی مهندسی هستند که در سال‌های اخیر به طور گسترده مورد استفاده قرار گرفته‌اند. از این‌رو مقاله حاضر به برقراری ارتباط بین این زمینه های نوین در طراحی و ساخت و تولید قطعات توجه دارد. با توسعه مواد جدید و نیاز روزافزون مهندسین و طراحان به پیش‌بینی خصوصیات مکانیکی قطعات پرینت‌شده با فرآیند لایه‌گذاری، به ایجاد دادگان و گسترش الگوریتم‌ یادگیری ماشین جهت پیش‌بینی خواص قطعات ساخته‌شده به روش اکستروژن، پرداخته شده است. با تحلیل و دسته‌بندی نتایج آزمون کشش روی قطعات چاپ سه بعدی، داده‌های مناسب جهت پیاده‌سازی الگوریتم‌های یادگیری ماشین ایجاد شد. سپس شبکه‌های مختلف تحت الگوریتم‌های طبقه‌بندی‌کننده مانند درخت تصمیم‌گیری، دستگاه بردار پشتیبان، و نزدیک‌ترین همسایه‌های k برای پیش‌بینی استحکام کششی و مدول یانگ قطعات تولید‌شده با ورودی‌های متفاوت دستگاه‌ چاپگر بر پایه اکستروژن ذوبی و لایه‌نشانی مواد، ایجاد شده و طبق معیارهای استاندارد، دقت آن اندازه‌گیری شد. در نهایت مدل تولید‌شده توسط الگوریتم طبقه‌بندی‌کننده درخت تصمیم‌گیری با به‌دست‌آوردن نمرات دقت‌سنجی بالا (دقت: 90.9%، سطح زیر منحنی:0.92، نمرهF1: 0.88) دارای قابلیت مناسب برای استفاده در پیش‌بینی استحکام کششی مواد شناخته شد. از نتایج این پژوهش می‌توان جهت بهینه‌سازی طراحی و همچنین تولید پایگاه‌های داده آزمایشی قابل‌قبول بهره گرفت چون می‌توان گفت مسیر آینده طراحی و ساخت دیجیتال با شناخت بهتر علم داده و ابزارهای شبیه‌سازی خواص نهایی دسته بندی شده قابل تصور است.
کلیدواژه‌ها

عنوان مقاله English

Applying machine learning algorithms to predict tensile strength and young's modulus of parts produced by extrusion-based additive manufacturing

نویسنده English

Amin Jabbari
Mechanical Engineering Research Institute, Institute of New Technologies, Iranian Research Organization for Science and Technology, Tehran, Iran
چکیده English

Recent advancements in additive manufacturing (AM), artificial intelligence (AI), and machine learning (ML) have significantly influenced engineering practices, particularly in the design and fabrication of complex components. As the demand grows for predictive capabilities in assessing the mechanical properties of parts designed and produced via layer-by-layer 3D printing, machine learning algorithms offer promising solutions. This study examines the application of classification models to predict the tensile strength and Young’s modulus of components manufactured via fused deposition modeling (FDM) under various process parameters. Tensile test data were systematically analyzed to train decision tree, support vector machine (SVM), and k-nearest neighbor (KNN) models. Among these, the decision tree algorithm exhibited the highest predictive accuracy (90.9%), with an AUC of 0.92 and an F1 score of 0.88. These findings underscore the value of data-driven methodologies in enhancing engineering design processes and establishing reliable experimental databases, contributing to the evolution of intelligent digital manufacturing systems.

کلیدواژه‌ها English

3D Printing
Data Science
Artificial Intelligence
Fused Filament Fabrication
Material Properties
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