2018
DOI: 10.1007/s00170-018-1933-x
|View full text |Cite
|
Sign up to set email alerts
|

Effect of edge hone radius on chip formation and its microstructural characterization in hard milling of AISI H13 steel

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

0
3
0

Year Published

2018
2018
2024
2024

Publication Types

Select...
9

Relationship

0
9

Authors

Journals

citations
Cited by 12 publications
(5 citation statements)
references
References 26 publications
0
3
0
Order By: Relevance
“…One option is milling the dies. Here, parameters such as type of tool and its geometry [ 5 , 6 , 7 , 8 ], the machining strategy [ 9 , 10 ] and the operational conditions [ 11 , 12 ] play an essential role on its final surface properties, and these parameters are tightly related to the service efficiency of the die (for example, fatigue resistance). Another option is the Electrical Discharge Machining (EDM) of the dies [ 13 ].…”
Section: Introductionmentioning
confidence: 99%
“…One option is milling the dies. Here, parameters such as type of tool and its geometry [ 5 , 6 , 7 , 8 ], the machining strategy [ 9 , 10 ] and the operational conditions [ 11 , 12 ] play an essential role on its final surface properties, and these parameters are tightly related to the service efficiency of the die (for example, fatigue resistance). Another option is the Electrical Discharge Machining (EDM) of the dies [ 13 ].…”
Section: Introductionmentioning
confidence: 99%
“…The authors found that when edge radius increases, cutting forces, plastic deformation and compressive residual stress increase whereas the surface roughness decreases to a certain limiting value. Another study by Li et al [12], investigating the hard milling of AISI H13 steel explored the effect of edge hone radius on the chip formation mechanisms. The results showed that when the edge hone radius increases, the chip segmentation intensity and frequency increases, leading to high cutting force fluctuations.…”
Section: Introductionmentioning
confidence: 99%
“…Analysis of variance (ANOVA) for dimensional deviation is shown in table 3 for R410a coolant fluid. According to confidence level %95, the value P for each parameter that is less than 0.05 indicates that the parameter under consideration is statistically significant and effective in the process [51,52]. The P value of the model (except Feed rate) is less than 0.05, which indicates a good fit of the model with experimental results (zero values in tables mean P less than 0.0001).…”
Section: Data Analysis For Dimensional Deviationmentioning
confidence: 99%
“…Despite numerous studies on machining steels, particularly AISI 1045 steel [51][52][53][54][55][56][57][58][59][60][61][62][63][64][65], to the best of the authors' knowledge, the use of cryogenic coolant R410A and its comparison with water-soluble cutting oil (WSCO) fluid have not been observed in any study. The process variables included cutting speed (four levels), cutting depth (three levels), and feed rate (three levels), and the measurement parameters were extensively and precisely conducted in three categories: tool wear, dimensional deviation, and surface roughness.…”
Section: Introductionmentioning
confidence: 99%