Performance evaluation of various cutting fluids using MQL technique in hard turning of AISI 4340 alloy steel

Das, Anshuman; Patel, Saroj Kumar; Biswal, Bibhuti Bhusan; Sahoo, N. C.; Pradhan, Ashish

doi:10.1016/j.measurement.2019.107079

Cited by 63 publications

(25 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Accordingly, the levels of effectiveness of the parameters with various statistical values are expressed numerically. A large number of researchers employed statistical analysis to study the various machining parameters of a wide range of materials under different cutting conditions, which reflects the success of this approach [ 3 , 60 , 61 , 62 , 63 , 64 ].…”

Section: Introductionmentioning

confidence: 99%

Optimization and Analysis of Surface Roughness, Flank Wear and 5 Different Sensorial Data via Tool Condition Monitoring System in Turning of AISI 5140

Kuntoğlu

Aslan

Sağlam

et al. 2020

Sensors

View full text Add to dashboard Cite

Optimization of tool life is required to tune the machining parameters and achieve the desired surface roughness of the machined components in a wide range of engineering applications. There are many machining input variables which can influence surface roughness and tool life during any machining process, such as cutting speed, feed rate and depth of cut. These parameters can be optimized to reduce surface roughness and increase tool life. The present study investigates the optimization of five different sensorial criteria, additional to tool wear (VB) and surface roughness (Ra), via the Tool Condition Monitoring System (TCMS) for the first time in the open literature. Based on the Taguchi L9 orthogonal design principle, the basic machining parameters cutting speed (vc), feed rate (f) and depth of cut (ap) were adopted for the turning of AISI 5140 steel. For this purpose, an optimization approach was used implementing five different sensors, namely dynamometer, vibration, AE (Acoustic Emission), temperature and motor current sensors, to a lathe. In this context, VB, Ra and sensorial data were evaluated to observe the effects of machining parameters. After that, an RSM (Response Surface Methodology)-based optimization approach was applied to the measured variables. Cutting force (97.8%) represented the most reliable sensor data, followed by the AE (95.7%), temperature (92.9%), vibration (81.3%) and current (74.6%) sensors, respectively. RSM provided the optimum cutting conditions (at vc = 150 m/min, f = 0.09 mm/rev, ap = 1 mm) to obtain the best results for VB, Ra and the sensorial data, with a high success rate (82.5%).

show abstract

Section: Introductionmentioning

confidence: 99%

Optimization and Analysis of Surface Roughness, Flank Wear and 5 Different Sensorial Data via Tool Condition Monitoring System in Turning of AISI 5140

Kuntoğlu

Aslan

Sağlam

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Reduction of heat is vastly necessary because it sways various output characteristics aspects. Therefore, the reduction of heat and augmentation of output performance is the prime motive for the cutting process [69].…”

Section: Overview Of Heat Generationmentioning

confidence: 99%

“…It influences the machinability characteristics, so heat reduction is essential with the application of cutting fluid. The cutting fluid has three primary functions like it can lubricating the cutting zone, cooling for the tool and workpiece and removal of heat, and also assist for removing the chips from the cutting zone [69]. Kumar et al noticed that the catastrophic failure of the CBN tool occurs with increasing the hardness of the workpiece and it can be improved by providing the coating on the cutting inserts [71].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review on AISI 4340 Hardened Steel: Emphasis on Industry Implemented Machining Settings, Implications, and Statistical Analysis

Bag

Panda

Sahoo

et al. 2020

IJIE

View full text Add to dashboard Cite

Turning of hardened AISI 4340 steel is regarded as one of the demanding challenges in machining sectors where precision tolerances are essential for automobile parts. The AISI 4340 steel is broadly utilized in forged steel automotive crankshafts systems, hydraulic forged and additional machine tool purposes because of their improved characteristics. Moreover, one of the keys confronts in the machining of hard 4340 steel is the comparatively deprived machining behavior that reduces the functionality of the material and further leads to component rejection at the final inspection stage. In addition, accelerated tool wear necessitates for repeated changing of cutting tool that results in higher machining and tooling costs. This comprehensive review aimed to present in-depth features on the development of machining performances using various cutting tools. This review focus is to provide a broad perceptive of the role of controllable variables during machining of hardened steel. This review analysis examines the response variables and their advantages of chip morphology and heat generation. The comprehensive overview of machining settings, key machinability indicators and statistical analysis for AISI 4340 steel. This overview will provide academic, industrial and scientific communities with benefits and shortcomings through improved conceptual understanding towards further research and development.

show abstract

“…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

“…Yazarlar, kesici takım yan yüzey aşınması üzerinde nanofluid kesme sıvıları arasında en iyi performansı suda çözülür soğutmanın sergilediğini bulmuşlardır. Yazarlar, deney sonuçlarını tahmin etmede kullandıkları 2. dereceden matematiksel modellerden elde etmiş oldukları sonuçlar % 95 güven aralığında olduğunu tespit etmişlerdir [7]. Dutta ve Narralla, Taguchi metodu kullanarak Magnezyum alaşımının işlenmesinde tornalama parametrelerinin optimizasyonu yapmışlardır.…”

Section: Gi̇ri̇ş (Introduction)unclassified

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

Gürbüz

Gönülaçar

2021

Politeknik Dergisi

View full text Add to dashboard Cite

show abstract

Performance evaluation of various cutting fluids using MQL technique in hard turning of AISI 4340 alloy steel

Cited by 63 publications

References 26 publications

Optimization and Analysis of Surface Roughness, Flank Wear and 5 Different Sensorial Data via Tool Condition Monitoring System in Turning of AISI 5140

Optimization and Analysis of Surface Roughness, Flank Wear and 5 Different Sensorial Data via Tool Condition Monitoring System in Turning of AISI 5140

A Comprehensive Review on AISI 4340 Hardened Steel: Emphasis on Industry Implemented Machining Settings, Implications, and Statistical Analysis

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

Contact Info

Product

Resources

About