Modelling and Analysis of Surface Evolution on Turning of Hard-to-Cut CLARM 30NiCrMoV14 Steel Alloy

Investigations on the Surface Integrity of Ti6Al4V under Modified Dielectric(s)-Based Electric Discharge Machining Using Cryogenically Treated Electrodes

The surface integrity of machined components is considered to be an important part of the quality matrix for high-performance applications in the aviation industry. Therefore, close attention is given to the components made up of hard-to-cut materials such as Ti6Al4V, which face processability challenges. In this regard, among the non-conventional machining processes, electric discharge machining is widely preferred for cutting Ti6Al4V. In this study, the potentiality of cryogenic-treated tool electrodes (graphite and aluminum) with unmodified (kerosene) and modified (kerosene with Span 20, Span 60, and Span 80) dielectrics are comprehensively investigated. A three-phased experimentation framework is deployed based on the following process parameters, i.e., surfactant type, surfactant concentration, electrode material, and pulse ON:OFF time. Thorough statistical analyses are performed based on the full factorial design of experiments, and the results are characterized by process physics. It is found that the cutting mechanism is highly dependent on the surfactants, especially their hydrophilic–lipophilic balance in the dielectric. The desirability-based process optimization results show that the unmodified process (with kerosene) produced relatively higher roughness values of 7.5 µm and 5.8 µm for aluminum and graphite electrodes, respectively. However, the modified process (with surfactant) resulted in a lower degree of roughness on the workpiece. The graphite electrode using S-60 with a resulting Ra of 1.73 µm outperformed the aluminum electrode using S-20, yielding an Ra of 3.4 µm. The S-60 surfactant resulted in minimum roughness on the workpiece with the fewest surface defects at the 25 g/L concentration, 50:25 µSec pulse ON:OFF time, and with the cryogenically treated graphite electrode.

Helical Milling of CFRP/Ti6Al4V Stacks Using Nano Fluid Based Minimum Quantity Lubrication (NF-MQL): Investigations on Process Performance and Hole Integrity

The structural components in the aeronautical industry require CFRP/Ti6Al4V stacks to be processed together, which results in poor hole integrity due to the thermal properties of the materials and challenges related to processability. These challenges include quality variation of the machined holes because of the limitations in process properties. Therefore, a novel solution through helical milling is investigated in the study using nano fluid based minimum quantity lubrication (NF-MQL). The analysis of variance shows, for Ti6Al4V, eccentricity (PCR = 28.56%), spindle speed (Ti) (PCR = 42.84%), and tangential feed (PCR = 8.61%), and for CFRP, tangential feed (PCR = 40.16%), spindle speed (PCR = 28.75%), and eccentricity (PCR = 8.41%) are the most significant parameters for diametric error. Further on, the rise in the circularity error is observed because of prolonged tool engagement at a higher value of tangential feed. Moreover, the surface roughness of Ti was reduced with an increasing percentage of MoS₂ in the lubricant. The spindle speed (37.37%) and lubricant (45.76%) have a potential influence on the processing temperature, as evident in the analysis of variance. Similarly, spindle speed Ti (61.16%), tangential feed (23.37%), and lubrication (11.32%) controlled flank wear, which is critical to tool life. Moreover, the concentration of MoS₂ decreased edge wear from ~105 µm (0.5% concentration) to ~70 µm (1% concentration). Thorough analyses on process performance in terms of hole accuracy, surface roughness, processing temperature, and tool wear are carried out based on the physical science of the process for cleaner production. The NF-MQL has significantly improved process performance and hole integrity.

Prediction of Surface Roughness Using Machine Learning Approach in MQL Turning of AISI 304 Steel by Varying Nanoparticle Size in the Cutting Fluid

Surface roughness is considered as an important measuring parameter in the machining industry that aids in ensuring the quality of the finished product. In turning operations, the tool and workpiece contact develop friction and cause heat generation, which in turn affects the machined surface. The use of cutting fluid in the machining zone helps to minimize the heat generation. In this paper, minimum quantity lubrication is used in turning of AISI 304 steel for determining the surface roughness. The cutting fluid is enriched with alumina nanoparticles of two different average particle sizes of 30 and 40 nm. Among the input parameters chosen for investigation are cutting speed, depth of cut, feed rate, and nanoparticle concentration. The response surface approach is used in the design of the experiment (RSM). For the purpose of estimating the surface roughness and comparing the experimental value to the predicted values, three machine learning-based models, including linear regression (LR), random forest (RF), and support vector machine (SVM), are utilized in addition. For the purpose of evaluating the accuracy of the predicted values, the coefficient of determination (R2), mean absolute percentage error (MAPE), and mean square error (MSE) were all used. Random forest outperformed the other two models in both the particle sizes of 30 and 40 nm, with R-squared of 0.8176 and 0.7231, respectively. Thus, this study provides a novel approach in predicting the surface roughness by varying the particle size in the cutting fluid using machine learning, which can save time and wastage of material and energy.