Experimental studies of machining parameters on surface roughness, flank wear, cutting forces and work piece vibration in boring of AISI 4340 steels: modelling and optimization approach

Kumar, Kaladari Adarsha; Chanamala, Ratnam; Rao, K. Venkata; Murthy, B. S.

doi:10.1007/s42452-018-0026-7

Cited by 19 publications

(6 citation statements)

References 26 publications

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“…The laser-cutting process, roughness optimization according to the laser input parameters and quality evaluation of the cut surfaces were studied in a number of papers. The influence of the laser-cutting parameters on R a was studied by Mesko et al [16] for S235JR steel, by Rao and Murthy [17] for stainless steel, by Adarsha Kumar [18] for AISI 4340 steel. Riveiro et al [19] studied the interaction between the assistant gas and the part as well as the role of the nozzle on R a for AISI 4340 steel.…”

Section: Methods and Experimentalmentioning

confidence: 99%

Mathematical Modelling Study of Hardox400 Steel Parts’ Roughness and Hardness, Cut with CO2 Laser

Mileșan

Girdu

Cîrțînă

et al. 2020

SV-JME

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This study supports the research in the field of laser cutting of steel sheets HARDOX400 (H400), a less well-known and analysed material. An H400 sheet with a thickness of 10 mm was used; a cutting plan was prepared according to Taguchi's method; the input parameters (laser power, assistant gas pressure and cutting speed) were varied according to a specific rule, producing nine pieces. The initial factorial experiment was replicated four times under the same conditions as the initial one. The values for the cutting width between the piece and the plate, the hardness, and the roughness of the 45 resulting pieces were measured. The results were interpreted for each measured parameter, and by using design of experiments (DOE) from the Statistics 7.0 software, we obtained the description of roughness (R a ) in terms of power and speed using Response Surface Method (RSM) for prediction and correlation formula. Highlights• Indication of the mathematical expression of the roughness R a from algebraic and differential points of view. • Description of the cutting parameters of the CO 2 laser installation for cutting a Hardox400 plate according to a cutting plan, consisting of 45 pieces. • The use of the response surface and the three dimensional (3D) graph to determine the roughness R a depending on the input parameters, laser power, and cutting speed. • Establishing the formula for correlating the roughness R a according to the laser power and the cutting speed within the linear and quadric model, respectively the statistical calculation within the linear and quadratic interaction.

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Section: Methods and Experimentalmentioning

confidence: 99%

Mathematical Modelling Study of Hardox400 Steel Parts’ Roughness and Hardness, Cut with CO2 Laser

Mileșan

Girdu

Cîrțînă

et al. 2020

SV-JME

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“…SVM was proven to give an effective prediction model for several of machining processes. Kumar et al [20] developed prediction models of Ra, cutting force, tangential force and tool wear in the boring process using SVM and response surface methodology (RSM). The machining experiment was carried out using full factorial design with workpiece AISI 4340 steels and multi-layered coated carbide electrode.…”

Section: B Proposed Prediction Modelmentioning

confidence: 99%

“…From the review of the previous studies implemented by previous researchers, it can be concluded that SVM is able to give effective prediction results for conventional and modern machining processes. Furthermore, SVM has outperformed other techniques such as RSM and ANN [20], [24]. In the current work, the SVM prediction model is developed to predict the Ra value for end milling processes.…”

Section: B Proposed Prediction Modelmentioning

confidence: 99%

Integrating Taguchi Method and Support Vector Machine for Enhanced Surface Roughness Modeling and Optimization

Deris,

Ali,

Sabri

et al. 2024

IJACSA

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End milling process is widely used in various industrial applications, including health, aerospace and manufacturing industries. Over the years, machine technology of end milling has grown exponentially to attain the needs of various fields especially in manufacturing industry. The main concern of manufacturing industry is to obtain good quality products. The machined products quality is commonly correlated with the value of surface roughness (Ra), representing vital aspect that can influence overall machining performance. However, finding the optimal value of surface roughness is remain as a challenging task because it involves a lot of considerations on the cutting process especially the selection of suitable machining parameters and also cutting materials and workpiece. Hence, this study presents a support vector machine (SVM) prediction model to obtain the minimum Ra for end milling machining process. The prediction model was developed with three input parameters, namely feed rate, depth of cut and spindle speed, while Ra is the output parameter. The data of end milling is collected from the case studies based on the machining experimental with titanium alloy, workpiece and three types of cutting tools, namely uncoated carbide WC-Co (uncoated), common PVD-TiAlN (TiAlN) and Supernitride coating (SNTR). The prediction result has found that SVM is an effective prediction model by giving a better Ra value compared with experimental and regression results.

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“…Therefore, studying the parameter optimization under the joint action of cutting noise, vibration, and force can more comprehensively understand the cutting mechanism and machining performance. Kumar et al (2019) determined the machining parameters of minimum surface roughness, side wear, cutting force, tangential force, and work-piece vibration based on the multi-objective optimization method. Dong et al (2013) established a cutting dynamics model considering work-piece deformation and tool vibration, and obtained the optimal cutting parameters through constraints and objective functions, including cutting force, cutting power, and cutting vibration.…”

Section: Introductionmentioning

confidence: 99%

Study on parameter optimization of noise-vibration-force-metal removal rate in TC4 titanium alloy milling

Shao

et al. 2022

Journal of Vibration and Control

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Aiming at the influence of noise, vibration, and force on machining efficiency and machining quality in the process of milling, this paper proposed an improved particle swarm optimization algorithm. The algorithm could optimize the multi-objective and multi-parameter integration model and obtain the optimal milling parameters under the specified milling conditions, so as to reduce milling noise, vibration, and force and improve machining efficiency. First, based on the theory of all factor experiment, the milling experiment of TC4 titanium alloy was carried out, and the relevant milling noise, vibration, and force signals were obtained. Then, based on the response surface analysis method, the influence of milling parameters on noise, vibration, and force was studied. Finally, a multi-objective optimization model based on maximum metal removal rate, minimum noise, minimum vibration, and minimum force was established, and the improved particle swarm optimization algorithm was used to solve the optimal milling parameters. The result shows that when the milling speed is 24.9588 m/min, the feed speed is 9.9324 mm/min, and the milling depth is 1 mm, the optimal milling state within the constraint conditions can be achieved.

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Experimental studies of machining parameters on surface roughness, flank wear, cutting forces and work piece vibration in boring of AISI 4340 steels: modelling and optimization approach

Cited by 19 publications

References 26 publications

Mathematical Modelling Study of Hardox400 Steel Parts’ Roughness and Hardness, Cut with CO2 Laser

Mathematical Modelling Study of Hardox400 Steel Parts’ Roughness and Hardness, Cut with CO2 Laser

Integrating Taguchi Method and Support Vector Machine for Enhanced Surface Roughness Modeling and Optimization

Study on parameter optimization of noise-vibration-force-metal removal rate in TC4 titanium alloy milling

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