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dc.contributor.authorSong, Chunlei
dc.contributor.authorJin, Xiaoliang
dc.date.accessioned2024-12-24T19:11:38Z
dc.date.available2024-12-24T19:11:38Z
dc.date.copyright7/5/2024 12:00:00 AM
dc.date.issued2024
dc.identifier.issn1087-1357
dc.identifier.othermanu_146_9_091006.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4303467
dc.description.abstractThe machining mechanics of carbon fiber reinforced polymer (CFRP) materials are influenced by the coupled effects of the workpiece anisotropy, tool edge geometry, and cutting parameters. Predicting the chip formation mechanism is crucial for optimizing cutting parameters, reducing tool wear, and improving efficiency and surface quality. This study quantitatively evaluates the effect of main CFRP failure criteria on the chip formation mechanism in modeling the machining mechanics of CFRP. The results show that the Hashin–Puck and Dávila criteria excel at capturing chip formation across all fiber orientations because of the incorporation of the “internal friction” concept, while others only achieve accurate predictions in specific fiber orientation ranges due to improper shear strength consideration. The sources of the prediction similarities, differences, and limitations of failure criteria are experimentally validated. Sensitivity analyses quantitatively determine the effect of the tool rake angle on the machining energy consumption and cutting forces across the fiber orientation range. This research can be used to select the optimal failure criteria, design proper cutting tool geometry, and inform the cutting parameter choices for CFRP machining operations.
dc.publisherThe American Society of Mechanical Engineers (ASME)
dc.titleEffects of Failure Criteria on Quantitatively Determining the Machining Mechanics for CFRP With Different Tool Rake Angles
dc.typeJournal Paper
dc.journal.volume146
dc.journal.issue9
dc.journal.titleJournal of Manufacturing Science and Engineering
dc.identifier.doi10.1115/1.4065726
dc.journal.fristpage91006-1
dc.journal.lastpage91006-12
dc.page12
dc.treeJournal of Manufacturing Science and Engineering:;2024:;volume( 146 ):;issue: 009
dc.contenttypeFulltext


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