Analysis and prediction of weld bead volume and hardness in laser welding of low carbon dual phase steel
Publish place: Third National Conference and First International Conference on Applied Research in Electrical, Mechanical and Mechatronics Engineering
Publish Year: 1394
نوع سند: مقاله کنفرانسی
زبان: English
View: 484
This Paper With 8 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
ELEMECHCONF03_0699
تاریخ نمایه سازی: 9 مرداد 1395
Abstract:
In this present study, Nd:YAG laser welding of a dual phase steel has been carried out. Due to the importance of weld bead volume and hardness in weld properties such as mechanical and metallurgical behavior, the goal was to develop mathematic models to predict the weld bead volume and hardness of result welds. The independent variables were voltage, frequency, and pulse duration and beam diameter. Polynomial equations for predicting the weld bead volume and hardness were developed and analysis of variance for significance of the models, the test for significance on individual model coefficients and the lack of fit test were performed using design expert software. It was founded that voltage has the main effect on the both weld bead volume and microhardness of result welds and frequency and pulse duration have the next main effects on the responses respectively and analysis of variance showed that developed models are adequate. No significant effect showed by beam diameter due to reciprocal effect. The developed models were checked for the adequacy and accuracy of prediction. Confirmatory tests showed that the models are adequate for prediction of weld bead volume and hardness of result welds.
Keywords:
Authors
Abozar Pouzesh
Department of Mechanical Engineering, Gachsaran Branch, Islamic Azad University, Gachsaran, Iran.
Mahmud Hazratinezhad
Department of Mechanical Engineering, Gachsaran Branch, Islamic Azad University, Gachsaran, Iran.
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :