Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 197
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 271
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3165
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 597
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 511
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 317
Function: require_once
98%
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2 minutes
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Among the 26 roughness parameters described in ISO 25178 standard, the parameters used to characterize surface performance in characterization parameter set () lack scientificity and unity, resulting in application confusion. The current comes from empirical selection or small sample experiments, thus featuring low generality. A new method for constructing in rough surfaces is proposed to solve the above issues. Based on a data mining method, statistical theory, and roughness parameters definitions, the 26 roughness parameters are divided into and redundant parameter sets () with the help of reconstructed surfaces and machining experiments, and the mapping relationships between and are established. The research shows that accounts for 50%, and , of great significance for surface performance, and has the ability to fully cover surface topography information. The birth of provides an accurate parameter set for the subsequent study of different surface performance, and it provides more effective parameters for evaluating the workpiece surface performance from the same batch.
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Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9457287 | PMC |
http://dx.doi.org/10.3390/ma15175971 | DOI Listing |