Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
While many studies for material extrusion-based additive manufacturing (AM) of polymers focus on experimental approaches to evaluate relevant performance measures from process parameters, there is a lack of discussion to connect experimental results with useful applications. Also, one of the major deficiencies in the application literature is a trade-off analysis between energy costs and cycle time (time to produce an item from the beginning to the end) since improving these two measures simultaneously is challenging. Thus, this paper proposes an energy simulation method for performing a trade-off analysis between energy costs and cycle time using combinations of major AM process parameters for material extrusion. We conduct experiments using carbon fiber-reinforced poly-ether-ether-ketone (CFR-PEEK), which is increasingly used in material extrusion. From experimental results, we build a power model in which power (kW) is derived as a linear function of material addition rates (MAR). This MAR regression model is then used in a proposed simulation model that integrates discrete event simulation and numerical simulation. In our simulation case study of 50 machines and 40 scenarios, we investigate trade-offs between energy costs and cycle time with three control policies (P, P, and P) that allow 1, 25, or 50 machines to start heating, respectively. The trade-off analysis results show that P can be preferred when a balance between cycle time and energy costs is pursued, while P or P can be chosen if either energy cost (with P) or cycle time (with P) is more important than the other measure. Moreover, we find that the machine utilization, variability, and product volume have significant effects on energy costs and cycle time.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8933047 | PMC |
http://dx.doi.org/10.1007/s00170-022-08967-x | DOI Listing |
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