Influence of Support Vector Regression (SVR) on Cryogenic Face Milling
نویسندگان
چکیده
The paper aims to investigate the processing execution of SS316 in manageable machining cooling ways such as dry, wet, and cryogenic (LN2-liquid nitrogen). Furthermore, “one parametric approach” was utilized study influence carry out comparative analysis LN2over dry wet conditions. Response surface methodology (RSM) is incorporated build a relationship model among considered independent variables (spindle speed: (S, rpm), feed rate (F, mm/min), depth cut (doc) (D, mm)) dependent variable (surface roughness (Ra)). Since there involvement more than one variable, generation regression equation “multiple linear regression.” Based on attained coefficient value respective impact identified. results states revealed that LN2 yielded better finish with up 64.9%, 54.9% over machining, respectively, indicating benefits for achieving Ra. benchmark function proposed mode hybrid-bias (BNN-SVR) algorithm showcases propensity emerge local minimum coincide optimal target value. performance prevalent new ability fetch partially trained weights from BNN into SVR model, thus leading conversion static learning capability dynamic capability. performances adopted prediction approaches are compared through range error deviation, i.e., (RA: 3.95%–8.43%), (BNN: 2.36%–5.88%), (SVR: 1.04%–3.61%), respectively. Hybrid-bias best suitable it provides significant evidence by attaining less predicting However, surpasses RSM because convergence factor narrow margin error.
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ژورنال
عنوان ژورنال: Advances in Materials Science and Engineering
سال: 2021
ISSN: ['1687-8434', '1687-8442']
DOI: https://doi.org/10.1155/2021/9984369