2019
DOI: 10.1007/s00170-019-03643-z
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A predictive model on surface roughness during internal traverse grinding of small holes

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Cited by 13 publications
(2 citation statements)
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“…Further improvements in the mechanical properties of the SRM with CCC by adding fillers or reinforcing additives, such as wollastonite, glass fibers, carbon fibers, glass sphere, molybdenum disulfide [ 66 , 67 , 68 , 69 ], zirconia [ 70 , 71 , 72 ], silica sand, or silicon nitride [ 73 , 74 , 75 ] into the SRB is also an important research issue. In addition, the internal surface of CCC has high surface roughness [ 76 , 77 , 78 ] and result in reduction in mold life due to stress concentration [ 79 , 80 , 81 , 82 ]. Thus, surface improvement of the CCC by abrasive flow machining [ 83 ], electrochemical polishing [ 84 ], chemical polishing [ 85 ], laser polishing [ 86 ], ultrasonic cavitation abrasive finishing [ 87 ], or abrasive blasting [ 88 ] is also an important research topic.…”
Section: Resultsmentioning
confidence: 99%
“…Further improvements in the mechanical properties of the SRM with CCC by adding fillers or reinforcing additives, such as wollastonite, glass fibers, carbon fibers, glass sphere, molybdenum disulfide [ 66 , 67 , 68 , 69 ], zirconia [ 70 , 71 , 72 ], silica sand, or silicon nitride [ 73 , 74 , 75 ] into the SRB is also an important research issue. In addition, the internal surface of CCC has high surface roughness [ 76 , 77 , 78 ] and result in reduction in mold life due to stress concentration [ 79 , 80 , 81 , 82 ]. Thus, surface improvement of the CCC by abrasive flow machining [ 83 ], electrochemical polishing [ 84 ], chemical polishing [ 85 ], laser polishing [ 86 ], ultrasonic cavitation abrasive finishing [ 87 ], or abrasive blasting [ 88 ] is also an important research topic.…”
Section: Resultsmentioning
confidence: 99%
“…Li et al [12][13] analyzed the surface roughness during grinding superalloy FGH96, established the prediction model of grinding ratio, and analyzed the effect of grinding parameters on wheel wear and surface roughness. Su et al [14] established a predictive model of the roughness during internal traverse grinding; the model considered the deflection of tool and interference of grooves. Liu et al [15] proposed an analytical method for predicting surface roughness in precision grinding; the method considered the distribution of protrusion heights of abrasives, the grinding depth, the grinding velocity and feeding rate.…”
Section: Introductionmentioning
confidence: 99%