Influence of Support Vector Regression (SVR) on Cryogenic Face Milling

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.

Effects of shallow cryogenic treatment on surface characteristics and machinability factors in hard turning of AISI 4140 steel

“…[39][40][41][42][43][44] However, the number of studies that applied cryogenic treatment on the workpiece material is limited. [45][46][47][48] Kara et al 45 evaluated the effect of different kinds of heat and cryogenic treatments on tool wear and surface roughness in hard turning of AISI D2 steel using ceramic inserts. They used three samples namely, deep cryogenic treatment with a tempering (DCTT-36), deep cryogenic treatment (DCT-36), and conventional heat treatment (CHT) having 62 HRC hardness.…”

“…39–44 However, the number of studies that applied cryogenic treatment on the workpiece material is limited. 45–48…”

SoC-Based Early Failure Detection System Using Deep Learning for Tool Wear

“…Ye et al [17] presented a real-time data fusion method using a fuzzy approach, and an artificial neural network with an evolutionary algorithm was used for pattern recognition and tool wear prediction [18], [19]. Other articles based on support vector machine/regression have been presented for milling surfaces and roughness estimation [21]- [23].…”

Study on Multifactor Control Experiment and Design of Quality Detection System for Yuba Skin Production

“…e system uses the STC51 single-chip microcomputer as the lower computer, and the LabVIEW control system is designed as the upper computer to accurately control a variety of production process conditions, and the image processing technology is used to detect the quality of Yuba skin intelligently, improving the intelligent degree of Yuba skin production machine [8,9]. At the same time, the optimized process parameters are determined by an orthogonal test, and the yield and quality are improved [10].…”