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

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 its advantages on chip morphology and heat generation. The comprehensive overview of machining settings, key machinability indicators and statistical analysis for AISI 4340 steel has been presented. This overview will provide academic, industrial and scientific communities with benefits and shortcomings through improved conceptual understanding towards further research and development

Grey-Fuzzy Hybrid Optimization and Cascade Neural Network Modelling in Hard Turning of AISI D2 Steel

Nowadays hard turning is noticed to be the most dominating machining activity especially for difficult to cut metallic alloys. Attributes of dry hard turning are highly influenced by the amount of heat generation during cutting. Some major challenges are rapid tool wear, lower tool-life span, and poor surface finish but simultaneously generated heat is enough to provide thermal softening of hard work material and facilitates easier shear deformation thus easy cutting. Also, plenty of works reported the utilization of various cooling methods as well as coolants which successfully retard the intensity of cutting heat but this leads to additional cost as well as environmental and health issues. However, still, there is scope to select proper cutting tool materials, its geometry, and appropriate values of cutting parameters to get favorable machining outcomes under dry hard turning and avoid the cooling cost, environmental and health issue. Considering these challenges, current work utilizes PVD-coated (TiAlN) carbide insert in dry hard turning of AISI D2 steel. The multi-responses like tool-flank wear, chip morphology and chip reduction coefficient are considered. Further, to get the best combination of input cutting terms, grey-fuzzy hybrid optimization (Type I and Type II) is utilized considering the Gaussian membership function. Type II grey-fuzzy system attributed to 15 % less error (between GRG and GFG) compared to Type I. Hence, Type II grey-fuzzy system is utilized to get the optimal set of input terms. The optimal combination of input terms is found as t-1 (0.15 mm), s-4 (0.25 mm/rev) and is Vc-2 (100 m/min) which is comparable to the results obtained under spray impingement cooling using CVD tool in the literature. However, hard turning can be assessed under the dry condition with a PVD tool at the obtained optimal input condition for industrial uses. Further, six different types of cascade-forward-back propagation neural network modelling are accomplished. Among all models, CFBNN-4 model exhibited the best prediction results with a mean absolute error of 2.278% for flank wear (VBc) and 0.112% for the chip reduction coefficient (CRC). However, this model can be recommended for other engineering modelling problems

Machinability Investigation on Novel Incoloy 330 Super Alloy using Coconut Oil Based SiO2 Nano fluid

Over the years, the quality of the finished surface has become the foremost prevalent owing to better output performance, reliability and life span of a machined part.  Moreover, the effects of cooling and lubrication approach during the machining process play a vital role. Incoloy 330 generally used in petrochemical, chemical, power generations, thermal processing applications. This exploration focuses on the appropriate utilization of the Minimum Quantity Lubrication (MQL) based cooling approach using diverse concentrations of coconut oil based SiO2 nanofluids in the turning practice of Incoloy 330 alloy. The input variables are nanofluids concentration (Nc), feed (f) and cutting speed (Vc). The cutting insert TiAlN PVD coated cemented carbide tool is utilized to study the output responses like tool flank wear (VBc), surface roughness (Ra), material removal rate (MRR), and chip morphology. SiO2 nanofluids work effectively as tool flank wear is found to be less (VBc varies in between 0.057 mm to 0.077 mm). From ANOVA, cutting speed is found to be topmost influencing input (83.24%) for tool flank wear. Machining on the highest feed value (0.35 mm/rev) is not recommended for this work as Ra is found to be greater than 1.6 µm. With increasing cutting speed and feed rate, MRR increases. In each run, coiled continuous helical chips are obtained. Deformed chip thickness is found to be lower ( 0.3 to 0.74 mm) due to the application of SiO2 nanofluid through MQL which enhanced the heat dissipation thus eliminated the tendency of chip welding on the top surface of the tool. Chip reduction coefficient decreases with feed and cutting speed. Further, the TOPSIS optimization technique has been implemented to get an optimum set of cutting parameters for multiple responses and it is found to be Nc3 (0.3 % wt)-f1 (0.15 mm/rev)-Vc3 (160 m/min)

Investigations on surface quality characteristics with multi-response parametric optimization and correlations

This paper presents the parametric optimization on surface quality characteristics (Ra, Rz and Rt) in hard turning of EN31 steel using multilayer coated carbide insert (TiN/TiCN/Al2O3) and also finds correlations. The experiments have been conducted based on Taguchi’s L9 orthogonal array. Multiple linear regression analysis has been utilized to find the correlations. The integrated multi-response optimization approach using CQL concept in WPCA coupled with Taguchi technique has been implemented. Based on the S/N ratio, the optimal process parameters for surface roughness i.e. Ra and Rz are the depth of cut at level 3 (0.5 mm), the cutting speed at level 3 (140 m/min), and the feed at level 1 (0.04 mm/rev). The optimal process parameters for Rt are found to be the depth of cut at level 3 (0.5 mm), the cutting speed at level 2 (100 m/min), and the feed at level 1 (0.04 mm/rev). Feed and depth of cut are found to be the significant cutting parameters affecting the responses at 95% confidence limit from ANOVA study. The first order model presented high correlation coefficient between the experimental and predicted values. The optimal parametric combination for multi-response (Ra, Rz and Rt) becomes d3–v3–f1 and is greatly improved

Investigations on surface quality characteristics with multi-response parametric optimization and correlations

This paper presents the parametric optimization on surface quality characteristics (Ra, Rz and Rt) in hard turning of EN31 steel using multilayer coated carbide insert (TiN/TiCN/Al2O3) and also finds correlations. The experiments have been conducted based on Taguchi’s L9 orthogonal array. Multiple linear regression analysis has been utilized to find the correlations. The integrated multi-response optimization approach using CQL concept in WPCA coupled with Taguchi technique has been implemented. Based on the S/N ratio, the optimal process parameters for surface roughness i.e. Ra and Rz are the depth of cut at level 3 (0.5 mm), the cutting speed at level 3 (140 m/min), and the feed at level 1 (0.04 mm/rev). The optimal process parameters for Rt are found to be the depth of cut at level 3 (0.5 mm), the cutting speed at level 2 (100 m/min), and the feed at level 1 (0.04 mm/rev). Feed and depth of cut are found to be the significant cutting parameters affecting the responses at 95% confidence limit from ANOVA study. The first order model presented high correlation coefficient between the experimental and predicted values. The optimal parametric combination for multi-response (Ra, Rz and Rt) becomes d3–v3–f1 and is greatly improved

WASPAS Based Multi Response Optimization in Hard Turning of AISI 52100 Steel under ZnO Nanofluid Assisted Dual Nozzle Pulse-MQL Environment

Hard turning is an emerging machining technology that evolved as a substitute for grinding in the production of precision parts from hardened steel. It offers advantages such as reduced cycle times, lower costs, and environmental benefits over grinding. Hard turning is stated to be difficult because of the high hardness of the workpiece material, which causes higher tool wear, cutting temperature, surface roughness, and cutting force. In this work, a dual-nozzle minimum quantity lubrication (MQL) system’s performance assessment of ZnO nano-cutting fluid in the hard turning of AISI 52100 bearing steel is examined. The objective is to evaluate the ZnO nano-cutting fluid’s impacts on flank wear, surface roughness, cutting temperature, cutting power consumption, and cutting noise. The tool flank wear was traced to be very low (0.027 mm to 0.095 mm) as per the hard turning concern. Additionally, the data acquired are statistically analyzed using main effects plots, interaction plots, and analysis of variance (ANOVA). Moreover, a novel Weighted Aggregated Sum Product Assessment (WASPAS) optimization tool was implemented to select the optimal combination of input parameters. The following optimal input variables were found: depth of cut = 0.3 mm, feed = 0.05 mm/rev, cutting speed = 210 m/min, and flow rate = 50 mL/hr

Hard Turning Performance Investigation of AISI D2 Steel under a Dual Nozzle MQL Environment

In recent years, hard turning has emerged as a burgeoning cutting technology for producing high-quality finishing of cylindrical-shaped hardened steel for a variety of industrial applications. Hard turning under dry cutting was not accepted because of the generation of higher cutting temperatures which accelerated tool wear and produced an inferior surface finish. Nowadays, minimum quantity lubrication (MQL) is widely accepted in hard turning to reduce the problems encountered in dry cutting. This research aimed to augment the MQL performance in the hard turning process of AISI D2 steel by applying a novel concept, namely, a dual jet nozzle MQL system that supplies the cutting fluid into the cutting zone from two different directions. The performances of hard turning are discussed using machinability indicator parameters, such as surface roughness, tool wear, cutting temperature, power consumption, noise emission, and chip morphology. The dual nozzle MQL greatly reduced the friction between contact surfaces in the cutting zone and provided improved surface quality (Ra = 0.448 to 1.265 µm). Furthermore, tool flank wear was found to be lower, in the range of 0.041 to 0.112 mm, with abrasion and adhesion being observed to be the main mode of wear mechanisms. The power consumption was greatly influenced by the depth of cut (46.69%), followed by cutting speed (40.76%) and feed (9.70%). The chip shapes were found to be helical, ribbon, and spiral c type, while the colors were a metallic, light blue, deep blue, and light golden

Environmental sustainability during machining of hardened steel using nanofluid: A case study

The positive effects of nanofluid-assisted minimum quantity lubrication include improved performance during machining and environmental sustainability. In the present study two cutting parameters levels such as feed rate of 0.05 (mm/rev)- depth of cut of 0.1(mm)-cutting speed of 80(m/min) and feed rate of 0.15(mm/rev)-depth of cut of 0.3(mm)-cutting speed of 200(m/min) has been used in the hard turning of D2 steel. It was determined that the 0.3% ZrO2 wt% observed to be improved machinability as compared to dry and Minimum Quantity Lubrication (MQL) environment. Finally, the sustainability assessment through the Pugh Matrix Assessment (PMA) presented the potential of nanofluid-MQL for improvement of machinability of hardened D2 steel for cleaner production

A contemporary advancement of intelligent machining and sustainability aspects in hard machining area: A Critical Review

The intelligent manufacturing devotes considerable effort to towards machining process. This phenomenon is engendered by the growing demand for advanced machining process for manufacturing of precise parts by adopting optimization techniques. This article illustrates the most significant developments in the sustainability aspects as well as optimization and modelling techniques adopted to solve the problems and complexity in hard machining process. Machining realisation necessitates recent and future breakthroughs in technological innovations for Industry 4.0. A significant amount of focus is also paid to the different sustainability aspects, modelling strategies and performance analysis during hard machining process. Many avenues for future study on the needs of intelligent manufacturing are discussed in this article. The future directions for intelligent machine systems and sustainability factors are also discussed for the green, sustainable, and high dimensional accuracy manufacturing in hard machining area

Prediction models for on-line cutting tool and machined surface condition monitoring during hard turning considering vibration signal

Turning of hardened steel is an immense issue of interest concerning with machining technology and scientific research. A strategy to analyze vibration signals and its correlation on surface roughness and tool wear has not attracted much breakthrough in research so far in hard machining. Therefore, tool condition monitoring (TCM) study will be definitely worthwhile for the effective application in hard part turning. The current study examines about the online prediction of flank wear and surface roughness monitoring during dry hard turning of AISI 52100 steel (55 ± 1 HRC) utilizing MTCVD multilayer coated carbide insert (TiN/TiCN/Al2O3) considering machining parameters and vibration signals through development of prediction model (MLR and MQR) after studying the Pearson correlation coefficient and test for its accuracy. Pearson correlation coefficient for feed on flank wear is utmost pursued by acceleration amplitude of vibration (Vy) in radial direction, depth of cut and cutting speed. Similarly, acceleration amplitude of vibration followed by cutting speed and feed has strong correlation with surface roughness. MQR model predicts well for responses as percentage of error is quite less and cutting speed is obtained to be the most important parameter for vibration signal. Multiple quadratic regression (MQR) models are observed to be noteworthy, effective and adequate to predict response outputs with regards to the combined effect of machining parameters and vibration signals online. A corrective measure can safely be taken with reasonable degree of accuracy during hard turning