Evaluation of hard coating materials performance on machinability issues and material removal rate during turning operations

Kaladhar, M.

doi:10.1016/j.measurement.2018.11.066

Cited by 19 publications

(9 citation statements)

References 32 publications

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“…Other researchers identified novel issues about carrier gas typologies, jets size and shape, AJ speed and SD. M. Kaladhar in [7] evaluated the general performance for the process based on MRR, surface finishing and geometrical tolerance of the work piece, including the ware rate of jets. Lastly, various important literature texts concentrate on the leading aspects of mechanisms in machines of the brittle and ductile resources or concerning the enhancement of systematic statistical-experimental techniques and Artificial Neural Networks (ANN), which facilitates the linking between the procedural setting of variables and the accuracy of machines based on the surface finish rate.…”

Section: F Applicationmentioning

confidence: 99%

Nonconventional Machining and Materials Processing: Proposed Design for Abrasive Jet Machining (AJM)

2021

JMC

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High degree Abrasive Jet (AJ) is applied in Abrasive Jet Machining (AJM) as a process that is based of Machine-driven Energy (MDE). The abrasives are applied in the removal of resources from work surfaces based on impact erosion. AJ is generated through the acceleration of very fine abrasive particles in considerably gas that has been pressurizes i.e. carrier gas. In this case, jets are utilized to transform this pressurized energy into kinetic energy, which is also directs jets to the work surfaces at an impingement angle. During the impact, rigid abrasive particles are used to slowly remove resources through erosion and with the assistance of brittle fracture. This paper evaluates and recommends a design for AJM. The various components have also been chosen after the relevant design assessment procedures have been done. The AJM model has been design with reference to the present components.

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Section: F Applicationmentioning

confidence: 99%

Nonconventional Machining and Materials Processing: Proposed Design for Abrasive Jet Machining (AJM)

2021

JMC

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“…The study purposed optimization for economical consumption of resources considering a lot of parameters along with MRR. Kaladhar [28] integrated cutting speed, feed rate, depth of cut, nose radius and coating types into the turning of AISI 304 steel. According to the author's result, a lower nose radius and the third level of cutting speed along with a maximum depth of cut and feed rate should be selected for maximum MRR.…”

Section: Introductionmentioning

confidence: 99%

Parametric Optimization for Cutting Forces and Material Removal Rate in the Turning of AISI 5140

et al. 2021

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The present paper deals with the optimization of the three components of cutting forces and the Material Removal Rate (MRR) in the turning of AISI 5140 steel. The Harmonic Artificial Bee Colony Algorithm (H-ABC), which is an improved nature-inspired method, was compared with the Harmonic Bee Algorithm (HBA) and popular methods such as Taguchi’s S/N ratio and the Response Surface Methodology (RSM) in order to achieve the optimum parameters in machining applications. The experiments were performed under dry cutting conditions using three cutting speeds, three feed rates, and two depths of cuts. Quadratic regression equations were identified as the objective function for HBA to represent the relationship between the cutting parameters and responses, i.e., the cutting forces and MRR. According to the results, the RSM (72.1%) and H-ABC (64%) algorithms provide better composite desirability compared to the other techniques, namely Taguchi (43.4%) and HBA (47.2%). While the optimum parameters found by the H-ABC algorithm are better when considering cutting forces, RSM has a higher success rate for MRR. It is worth remarking that H-ABC provides an effective solution in comparison with the frequently used methods, which is promising for the optimization of the parameters in the turning of new-generation materials in the industry. There is a contradictory situation in maximizing the MRR and minimizing the cutting power simultaneously, because the affecting parameters have a reverse effect on these two response parameters. Comparing different types of methods provides a perspective in the selection of the optimum parameter design for industrial applications of the turning processes. This study stands as the first paper representing the comparative optimization approach for cutting forces and MRR.

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“…Günümüz endüstrisinde talaşlı imalat uygulamalarında önemli bir yer tutan tornalama operasyonlarında, işleme ve kesme parametrelerinin optimizasyonu, araştırmacıların odaklandığı başlıca konulardan birisidir. Bu nedenle Fc ve Ra sonuçlarının istatiksel analizi, modellenmesi ve optimizasyonu için Taguchi metodu ve YSA kullanılması çok sayıda araştırmaya konu olmuştur [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20].…”

Section: Gi̇ri̇ş (Introduction)unclassified

“…Yazar, PVD kaplı kesici takımlar diğer kesici takımlara göre Ra ve takım aşınması açısından daha iyi performans sergilediğini bulmuştur. Geliştirdiği matematiksel modellerle elde etmiş olduğu tahmin sonuçları ile deneysel değerler birbirleriyle yakın değerler sergilediğini tespit etmiştir [4]. Viswanathan ve ark.…”

Section: Gi̇ri̇ş (Introduction)unclassified

Analysis of Experimental Values Obtained at Different Cutting Parameters and MQL Flows with S/N Ratios and ANN

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2021

Politeknik Dergisi

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Evaluation of hard coating materials performance on machinability issues and material removal rate during turning operations

Cited by 19 publications

References 32 publications

Nonconventional Machining and Materials Processing: Proposed Design for Abrasive Jet Machining (AJM)

Nonconventional Machining and Materials Processing: Proposed Design for Abrasive Jet Machining (AJM)

Parametric Optimization for Cutting Forces and Material Removal Rate in the Turning of AISI 5140

Analysis of Experimental Values Obtained at Different Cutting Parameters and MQL Flows with S/N Ratios and ANN

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