MQL-Assisted Hard Turning of AISI D2 Steel with Corn Oil: Analysis of Surface Roughness, Tool Wear, and Manufacturing Costs

Precision hard turning (HT) gained more and more attention in the cutting industry in the last years due to continuous pressure of the global market for reducing costs, minimizing the environmental and health issues, and achieving a cleaner production. Therefore, dry cutting and minimal quantity lubrication (MQL) became widely used in manufacturing to meet the environmental issues with respect to harmful cutting fluids (CFs). Vegetable oils, in MQL machining, are a promising solutions to petroleum-based CFs; however, the effects and performance on surface roughness and tool wear in HT with ceramic inserts remain unclear. To address this limitation, hardened AIDI D2 steel and pure corn oil, rich in saturated and monounsaturated fatty acids, cheap and widely available, have been used to conduct dry and MQL experiments at different cutting speed and feeds. Results show that corn oil is suitable as cutting lubricant in HT, creating a strong anti-wear and anti-friction lubricating film which improves the roughness with 10–15% and tool life with 15–20%, therefore reducing costs. Best surface roughness values (Ra = 0.151 μm, Rz = 0.887 μm, Rpk = 0.261 μm) were obtained at 180 m/min and 0.1 mm/rev. The analysis of variance shows that corn oil has statistical significance on roughness, validating the results.

Galling wear between ASTM 6061 aluminum and grade D2 tool steel

In the automotive industry, galling is a huge problem either for the tool life or the quality of the stamping metal component. Galling is a severe form of scuffing associated with gross damage to the surface or failure. This work aims to carry out a series of experiments of galling tests at different loads and similar roughness with 6061 aluminum and D2 steel, common materials used in automotive components and tools respectively. A tribometer was employed to generate Galling wear, the button-on-button configuration, according to the ASTM G-196-08 standard. The results show the threshold of minimum load without galling wear. Post-test-surface analysis of the specimens was conducted by confocal microscopy to identify the damage generated during the wear tests. Additionally, a couple of friction tests were carried only to illustrate the friction coefficient behavior under galling conditions.

Wear and corrosion interaction of AISI D2 in an acidic environment

The tribological, electrochemical and tribo-electrochemical behavior of bare AISI D2 was studied. The tribological aspects were tested by pin on disc with an aluminum oxide ball as counter body, the electrochemical tests were performed in an aqueous solution of citric acid and the tribo-electrochemical evaluation was through a combination of both tests. AISI D2 steel presented abrasive wear in dry and wet conditions, such type of wear occurred due to stick-slip motion, moreover, in wet conditions the alloy corroded at corrosion current values around 10-6 A/cm2. The damage produced over the wear track was larger under wet conditions than for the dry conditions.

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. The amalgamation of input processing variables must be optimum for the effectual performance of hard to process materials turning. Generally, the Fuzzy logic hypothesis represents the uncertainties co-related with fuzziness, and deficiency in the data concerned with the problem. Further, to achieve 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 is 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. The outcomes of this research may be of immense importance to the tool manufacturers and machining industry.