کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10226531 | 1701275 | 2018 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Space-filling scan paths and Gaussian process-aided adaptive sampling for efficient surface measurements
ترجمه فارسی عنوان
مسیرهای اسکن پر شدن فضایی و نمونه گیری سازگار گاوس با استفاده از فرآیند برای اندازه گیری سطح کارایی
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کلمات کلیدی
نمونه گیری سازگار، اندازه گیری سطح، منحنی هیلبرت، اسکن اسپیرال،
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی صنعتی و تولید
چکیده انگلیسی
A method combining space-filling scan paths and adaptive sampling is proposed for surface measurements. Scan paths including a fractal Hilbert curve and a spiral pattern are mainly investigated. The adaptive sampling is based on iterative Gaussian process (GP) inference. Sampling positions are intelligently suggested along the scan path and the final sampled data are trained in a GP-model to reconstruct the entire topography. Simulations and experiments on different surfaces demonstrated the capability of the proposed method. When the special scan paths are employed alone, the required data amount is reduced to about 10%-13% of the uniform sampling and the relative error of surface reconstruction is within 10%. If the GP-aided adaptive sampling is further integrated, the data amount can be reduced to approximately 3%-4%. In addition, time-consumption in scanning is significantly eliminated. Compared with the raster scan, the integration of special scan paths and GP-aided adaptive sampling has several prominent advantages such as eliminating data amount, preserving surface reconstruction accuracy, maintaining a single-pass scan and saving time-cost. The measurement method has a potential application in situations where the efficiency is of critical importance.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Precision Engineering - Volume 54, October 2018, Pages 412-419
Journal: Precision Engineering - Volume 54, October 2018, Pages 412-419
نویسندگان
Chengfei Yang, Chaoyang Peng, Yuhang Chen, Tingting Luo, Jiaru Chu,