Abstract
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 to study the influence and carry out the comparative analysis of LN2over dry and LN2over wet machining conditions. Response surface methodology (RSM) is incorporated to build a relationship model among the considered independent variables (spindle speed: (S, rpm), feed rate (F, mm/min), and depth of cut (doc) (D, mm)) and the dependent variable (surface roughness (Ra)). Since there is the involvement of more than one independent variable, the generation of regression equation is “multiple linear regression.” Based on the attained coefficient value of the independent variable, the respective impact on surface roughness is identified. The results of comparative analysis of LN2over dry and LN2over wet machining states revealed that LN2 machining yielded better surface finish with up to 64.9%, 54.9% over dry and wet machining, respectively, indicating the benefits of LN2 for achieving better Ra. The benchmark function of the proposed mode hybrid-bias (BNN-SVR) algorithm showcases the propensity to emerge out of the local minimum and coincide with the optimal target value. The performance of the (BNN-SVR) is a prevalent new ability to fetch the partially trained weights from the BNN model into the SVR model, thus leading to the conversion of static learning capability to dynamic capability. The performances of the adopted prediction approaches are compared through a range of attained error deviation, i.e., (RA: 3.95%–8.43%), (BNN: 2.36%–5.88%), (SVR: 1.04%–3.61%), respectively. Hybrid-bias (BNN-SVR) is the best suitable prediction model as it provides significant evidence by attaining less error in predicting Ra. However, SVR surpasses BNN and RSM approaches because of the convergence factor and narrow margin error.
Highlights
SS316 stainless steel has arisen and plays a vital role in manufacturing automotive, aerospace, valves, pipes, medical, coastal architectural fittings, marine, chemical industries, thermal power plants, mining industries, etc. e chemical structure of SS316 encloses an accumulation of molybdenum, adding the enriched corrosion resistance property
It likewise influences a few practical characteristics of parts w.r.t. to friction between the tool and work samples, tool wear, heat transmission, and the ability to distribute and hold a lubricant, coating, etc. us, surface quality expectation assumes a huge part in the machining business for the appropriate determination and control of machining boundaries and enhancement of cutting conditions. e introduction section is illustrated in two subsections, first towards the cryogenic machining and impact of cutting fluids over response parameters and in the second section, Advances in Materials Science and Engineering involvement of prediction techniques and estimation of the performance of these forecasting techniques
Stainless Steel SS316 (AISI 316) was used as a sample workpiece with the size 100 mm × 40 mm × 10 mm. e face milling operation was conducted on CNC vertical Spark DTC milling machine [39, 40], utilizing cutter diameter 50 mm WIDIA M690 with PVD coated inserts TiAlNSDMT 1204PDR-MH-PA120. e experiments were carried for three machining conditions: conventional wet, dry, and LN2
Summary
SS316 stainless steel has arisen and plays a vital role in manufacturing automotive, aerospace, valves, pipes, medical, coastal architectural fittings, marine, chemical industries, thermal power plants, mining industries, etc. e chemical structure of SS316 encloses an accumulation of molybdenum, adding the enriched corrosion resistance property. E achieved data was applied to develop models employing response surface methodology (RSM), ANN, and support vector regression (SVR) strategies. ANN and SVR models were illustrated to predict the cutting force and surface roughness in turning 4140 steel [28]. Surface quality and cutting forces were estimated in slot milling of 7075-T6 aluminum alloy with ANN and SVR strategies [29]. E results attained showcase that the BNN-SVR model predicts surface roughness with reasonably high accuracy compared to other similar studies described in the literature due to its ability to achieve better convergence and global optimum. ANN and machine learning (hybrid bias: BNN-SVR) models are proposed for the best selection of machining parameters and optimization of cutting conditions.
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