A Non-destructive Ultrasonic Testing Approach for Measurement and Modelling of Tensile Strength in Rubbers

سال انتشار: 1399
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 142

فایل این مقاله در 7 صفحه با فرمت PDF قابل دریافت می باشد

استخراج به نرم افزارهای پژوهشی:

لینک ثابت به این مقاله:

شناسه ملی سند علمی:

JR_IJE-33-12_016

تاریخ نمایه سازی: 6 اردیبهشت 1400

چکیده مقاله:

Currently, non-destructive testing is widely used to investigate various mechanical and structural properties of materials. In the present study, non-destructive ultrasonic testing was applied to study the relationship between the tensile strength value and the velocity of longitudinal ultrasonic waves. For this purpose, fourteen specimens of composites with different formulations were prepared. The tensile strength of the composites and the velocity of longitudinal ultrasonic waves inside them was measured. The relevance vector machine regression analysis, as a new methodology in supervised machine learning, was used to define a mathematical expression for the functional relationship between the tensile strength and the velocity of longitudinal ultrasonic waves. The accuracy of the mathematical expression was tested based on standard statistical indices, which proved the expression to be an efficient model. Based on these results, the developed model has the capability of being used for the online measurement of the tensile strength of rubber with the proposed formulation in the rubber industry.

کلیدواژه ها:

Longitudinal Ultrasonic Waves' Velocity Relevance Vector Machine ، rubber ، tensile strength

نویسندگان

A. Foorginejad

Department of Mechanical Engineering, Birjand University of Technology, Birjand, Iran

M. Taheri

Department of Mechanical Engineering, Tarbiat Modares University, Tehran, Iran

N. Mollayi

Department of Computer Engineering, Birjand University of Technology, Birjand, Iran

مراجع و منابع این مقاله:

لیست زیر مراجع و منابع استفاده شده در این مقاله 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 مقاله لینک شده اند :
  • 1.     Stegemann, D., Raj, B., and Bhaduri, A. “NDT ...
  • 2.     Yilmaz, T., Ercikdi, B., Karaman, K., and Külekçi, ...
  • 3.     Morrison, D. S., and Abeyratne, U. R. “Ultrasonic ...
  • 4.     Vasanelli, E., Colangiuli, D., Calia, A., Sileo, M., ...
  • 5.     Hynes, N. R. J., Nagaraj, P., and Sujana, ...
  • 6.     Agrawal, M., Prasad, A., Bellare, J. R., and ...
  • 7.     Liu, Y., Song, Y., Li, X., Chen, C., ...
  • 8.     Li, X., Han, X., Arguelles, A. P., Song, ...
  • 9.     Foorginejad, A., Taheri, M., and Mollayi, N. “Measurement ...
  • 10.   Foorginejad, A., Taheri, M., Mollayi, N., and Shiva, ...
  • 11.   Provost, F. “Glossary of Terms.” Journal of Machine ...
  • 12.   Bishop, C. M. Pattern Recognition and Machine Learning. ...
  • 13.   Cortes, C., and Vapnik, V. “Support-vector networks.” Machine ...
  • 14.   Meyer, D., Leisch, F., and Hornik, K. “The ...
  • 15.   Tipping, M. E. “The relevance vector machine.” In ...
  • 16.   ASTM 624-00: Standard test method for tear strength ...
  • 17.   Tipping, M. E. “Sparse Bayesian Learning and the ...
  • 18.   Mackay, D. J. C. “Introduction to Gaussian processes.” ...
  • 19.   Tipping, M. E. “Bayesian inference: An introduction to ...
  • 20.   Tipping, M. E. “SPARSEBAYES V1.1: A Baseline Matlab ...
  • نمایش کامل مراجع