مهندسی ساخت و تولید ایران

مهندسی ساخت و تولید ایران

تحلیل آماری و بهینه‌سازی متغیرهای فرآیند ساخت افزایشی اصطکاکی اغتشاشی نانو کامپوزیت آلومینیوم سیلیکون کاربید با استفاده از روش سطح پاسخ

نوع مقاله : مقاله پژوهشی

نویسندگان
1 دانشجوی دکتری، گروه مهندسی مکانیک، دانشگاه تربیت دبیر شهید رجائی، تهران، ایران
2 دانشیار، گروه مهندسی مکانیک، دانشگاه تربیت دبیر شهید رجائی، تهران، ایران
3 استادیار، گروه مهندسی مکانیک، دانشگاه تربیت دبیر شهید رجائی، تهران، ایران
چکیده
در سال­‌های اخیر ساخت افزایشی اهمیت چشم‌گیری در زمینه تولید بدست آورده است. بیشتر تکنولوژی‌های ساخت افزایشی فلزات، شامل فرآیندهای ذوب و انجماد هستند که سبب ایجاد چالش‌های متالورژیکی می‌شوند. ساخت افزایشی اصطکاکی اغتشاشی یک روش ساخت افزایشی حالت جامد جدید می­‌باشد که با چالش‌های متالورژیکی رایج در روش‌های ذوبی معمول مواجه نیست. از این فرآیند می‌­توان برای ساخت نانوکامپوزیت‌های آلومینیوم سیلیکون کاربید که در صنایع مختلف از جمله نظامی، هوافضا، خودروسازی و ... کاربردهای فراوانی دارند استفاده نمود. هدف اصلی این تحقیق، تحلیل تجربی اثر متغیرهای سرعت چرخشی ابزار، سرعت پیشروی ابزار و تعداد پاس‌ها  در فرآیند ساخت افزایشی اصطکاکی اغتشاشی بر ریزسختی و میزان سایش نانوکامپوزیت آلومینیوم سیلیکون کاربید تولید شده با این روش می­‌باشد. بدین منظور، از روش سطح پاسخ و نرم افزار مینی­‌تب برای طراحی آزمایش‌­ها، تجزیه و تحلیل آماری و نیز بهینه‌­سازی متغیرها استفاده شد. تنظیمات بهینه‌سازی چند منظوره با هدف افزایش ریزسختی و کاهش میزان سایش انتخاب شدند. ترکیب مقادیر بهینه متغیرها جهت دستیابی به اهداف بهینه‌­سازی تعیین و نتایج بدست آمده صحت سنجی شد. نتیجه بهینه‌سازی با  مطلوبیت 0/87 در سرعت چرخش ابزار 1000 دور بر دقیقه، سرعت پیشروی ابزار 50 میلی‌متر بر دقیقه و تعداد پاس ‌یک حاصل شد. پاسخ‌های بهینه پیش‌­بینی شده 104 ویکرز برای ریزسختی و 0/013 گرم برای سایش در مقایسه با نتایج تجربی از در صد خطای کمی برخوردار بودند..
کلیدواژه‌ها

عنوان مقاله English

Statistical analysis and optimization of friction stir additive manufacturing process variables for aluminum silicon carbide nanocomposite using the response surface method

نویسندگان English

Mohammad Sharifizadeh 1
Nasrollah Bani Mostafa Arab 2
Amir Refahi Oskouei 3
1 PhD Student, Department of Mechanical Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran
2 Associate Professor, Department of Mechanical Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran
3 Assistant Professor, Department of Mechanical Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran
چکیده English

In recent years, additive manufacturing has gained significant importance in the field of production. Most of the additive manufacturing technologies for metals involve melting and solidification processes, leading to metallurgical challenges. The friction stir additive manufacturing process is a novel solid-state method that does not face the common metallurgical challenges associated with the traditional melting methods. This process can be used for the production of aluminum-silicon carbide nanocomposites that find many applications in industries such as military, aerospace, automotive, etc. The primary objective of this research is to experimentally analyze the effect of tool rotational speed, tool traverse speed, and number of passes in friction stir additive manufacturing on the microhardness and wear amount of aluminum-silicon carbide nanocomposite manufactured by this method. To this end, the response surface method and Minitab software were used for experimental design, statistical analysis, and optimization of the parameters. Multi-objective optimization settings were selected to increase microhardness and reduce wear. The combinations of optimal values of the parameters were determined to achieve the optimization goals, and the results were validated. The optimization result with 0.87 desirability is obtained with a tool rotational speed of 1000 rpm, a tool traverse speed of 50 mm/min, and one pass. The predicted optimal responses of 104 Vickers for microhardness and 0.013 gr for wear had a small percentage of error compared to the experimental results.

کلیدواژه‌ها English

Additive Manufacturing
Nanocomposite
Response Surface Method
Optimization
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