کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
11003627 | 1461458 | 2018 | 22 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Surface roughness measurement method based on multi-parameter modeling learning
ترجمه فارسی عنوان
روش اندازه گیری زبری سطح بر مبنای یادگیری مدل سازی چند پارامتر
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کلمات کلیدی
استخراج ویژگی، جنگل تصادفی اطلاعات متقابل، یادگیری سختی
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
کنترل و سیستم های مهندسی
چکیده انگلیسی
To improve the accuracy and efficiency of the existing roughness measurement methods, we propose a new surface roughness measurement technique based on multi-parameter modelling learning. First, multi-feature descriptor is constructed through speckle feature, grey feature and Tamura texture feature. Then, an identification reasoning method based on ACC-random forest was proposed to determine the work-piece classification. Finally, to realize surface roughness measurement efficiently, a multi-parameter learning model is established. Through establishment and optimization of multi-parameter surface roughness modeling, the value of surface roughness can be measured accurately. Thus, not only the class of work-piece be classified, also the value of surface roughness can be measured. Our proposed method breaks through the limitations of existing methods, which are based on several roughness measurement models for different classes of work-pieces. The experimental results indicate that our proposed method significantly outperform the state-of-the-art methods in terms of classification accuracy and measurement error rate.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Measurement - Volume 129, December 2018, Pages 664-676
Journal: Measurement - Volume 129, December 2018, Pages 664-676
نویسندگان
Suting Chen, Rui Feng, Chuang Zhang, Yanyan Zhang,