2021
DOI: 10.1016/j.ceramint.2020.10.033
|View full text |Cite
|
Sign up to set email alerts
|

Machining behaviors of glass-ceramics in multi-step high-speed grinding: Grinding parameter effects and optimization

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
7
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
6
1
1

Relationship

0
8

Authors

Journals

citations
Cited by 29 publications
(7 citation statements)
references
References 38 publications
0
7
0
Order By: Relevance
“…It is evident from the higher grinding ratio and high MRR. 12 Wheel loss of brown wheels was much lesser when compared with wheel loss of white wheels. This study is henceforth narrowed down to brown wheels with a bond A, and their performance is evaluated in a cylindrical grinding machine, as shown in Table 3.…”
Section: Resultsmentioning
confidence: 95%
See 1 more Smart Citation
“…It is evident from the higher grinding ratio and high MRR. 12 Wheel loss of brown wheels was much lesser when compared with wheel loss of white wheels. This study is henceforth narrowed down to brown wheels with a bond A, and their performance is evaluated in a cylindrical grinding machine, as shown in Table 3.…”
Section: Resultsmentioning
confidence: 95%
“…9 By extending the traditional machine tool settings modification just considering geometric correctness to the case of collaborative optimization, an adaptive collaborative control model is constructed 10 Sharmin et al 11 developed a response surface methodology (RSM) model to optimize the machining input parameters to minimize the temperature formed during the grinding process. Li et al 12 have performed a comparison of various existing grinding processes and presented that multi-step high-speed grinding technology was preferred over the other techniques by confirming the system's distinctive advantages and application prospects.…”
Section: Introductionmentioning
confidence: 97%
“…The capability of the empirical models was also verified using descriptive statistics. Given that this ratio was above 4, it can be argued that the developed models have strong enough signals to be used for optimization [21] .…”
Section: š‘ƒ š‘ = š¹ š‘ * š‘£ š‘mentioning
confidence: 99%
“…Zhang et al [21] also found that ultrasonic vibrations effectively reduced the machined surface roughness by 9% compared with conventional grinding. Ping et al [22,23] discovered that increasing the grinding wheel speed improved the surface quality and reduced the machined surface roughness. Wang et al [24] concluded that the surface quality was primarily determined by brittle fractures, grooves, and pits on the processed material surface.…”
Section: Introductionmentioning
confidence: 99%