Pregled bibliografske jedinice broj: 1235096
Modelling surface roughness in finish turning as a function of cutting tool geometry using the response surface method, Gaussian process regression and decision tree regression
Modelling surface roughness in finish turning as a function of cutting tool geometry using the response surface method, Gaussian process regression and decision tree regression // Advances in Production Engineering & Management, 17 (2022), 3; 367-380 doi:10.14743/apem2022.3.442 (međunarodna recenzija, članak, znanstveni)
CROSBI ID: 1235096 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Modelling surface roughness in finish turning as a
function
of cutting tool geometry using the response
surface method,
Gaussian process regression and decision tree
regression
(Modelling surface roughness in finish turning as a
function of cutting tool geometry using the
response
surface method, Gaussian process regression and
decision tree regression)
Autori
Vukelic, Djordje ; Simunovic, Katica ; Kanovic, Zeljko ; Saric, Tomislav ; Doroslovacki, Ksenija ; Prica, Miljana ; Simunovic, Goran
Izvornik
Advances in Production Engineering & Management (1854-6250) 17
(2022), 3;
367-380
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
Turning ; Tool geometry ; Modelling ; Surface roughness ; Response surface method ; Decision tree regression ; Gaussian process regression
Sažetak
In this study, the modelling of arithmetical mean roughness after turning of C45 steel was performed. Four parameters of cutting tool geometry were varied, i.e.: corner radius r, approach angle κ, rake angle γ and inclination angle λ. After turning, the arithmetical mean roughness Ra was measured. The obtained values of Ra ranged from 0.13 μm to 4.39 μm. The results of the experiments showed that surface roughness improves with increasing corner radius, increasing approach angle, increasing rake angle, and decreasing inclination angle. Based on the experimental results, models were developed to predict the distribution of the arithmetical mean roughness using the response surface method (RSM), Gaussian process regression with two kernel functions, the sequential exponential function (GPR-SE) and Mattern (GPR-Mat), and decision tree regression (DTR). The maximum percentage errors of the developed models were 3.898 %, 1.192 %, 1.364 %, and 0.960 % for DTR, GPR-SE, GPR-Mat, and RSM, respectively. In the worst case, the maximum absolute errors were 0.106 μm, 0.017 μm, 0.019 μm, and 0.011 μm for DTR, GPR-SE, GPR-Mat, and RSM, respectively. The results and the obtained errors show that the developed models can be successfully used for surface roughness prediction.
Izvorni jezik
Engleski
Znanstvena područja
Strojarstvo
POVEZANOST RADA
Ustanove:
Strojarski fakultet, Slavonski Brod,
Sveučilište u Slavonskom Brodu
Citiraj ovu publikaciju:
Časopis indeksira:
- Current Contents Connect (CCC)
- Web of Science Core Collection (WoSCC)
- Science Citation Index Expanded (SCI-EXP)
- SCI-EXP, SSCI i/ili A&HCI
- Scopus