A hybrid modeling approach based on computational fluid dynamics (CFD) and finite element method (FEM) is presented to simulate and study cryogenic machining (CM) of Ti–6Al–4V alloy. CFD analysis was carried out to study the characteristics of the fluid flow and heat transfer process of liquid nitrogen (LN2) jet used as a coolant in turning operation. The velocity, turbulence, gas volume fraction, and temperature of the impingement jet were investigated. Based on the analysis results, the coefficient of heat transfer (CHT) between the LN2 and cutting tool/insert was obtained and used in the FEM analysis to model the heat transfer process between the LN2 and the tool/chip/workpiece. A three-dimensional (3D) finite element (FE) model was developed to simulate a real CM operation. CM tests were carried out to validate the 3D FE model by comparing cutting forces and chip temperature. To evaluate LN2 cooling effect on tool temperature and tool wear, a two-dimensional (2D) FE model was developed for steady-state thermal analysis of cryogenic and dry machining. Based on the predicted temperatures, the tool wear was estimated, showing that LN2 cooling can significantly improve tool life.

In the current study, Ti-6Al-4V (Ti64) and Ti-6Al-7Nb (Ti67) alloys were prepared by vacuum arc melting. The produced samples were then subjected to different heat treatment regimes. The evolved microstructures and their corresponding hardness were investigated. Computerized drilling tests using TiAlN-coated high-speed steel bits were performed to assess the machinability of the prepared specimen regarding cutting force, tool wear, and thickness of the deformed layer. It was observed that Ti64 specimens that were water quenched from either α/β or β range contained martensitic phase. In Ti67, samples showed martensite only when water quenched from the β-phase range (1070 °C). Formation of martensite resulted in higher hardness and hence led to higher cutting forces and increased tool wear during the drilling process. Machined samples with higher hardness formed a thicker subsurface deformation area (white layer) and increased burr heights. Surface roughness in Ti64 workpieces was generally higher compared to Ti67 specimens. The coat of the drilling bit was partially attacked in the as-cast specimens, which was evident by elemental N on the machining chips. The machining tool deteriorated further by increasing the workpiece hardness through martensitic formation, where elements such as Cr, V, Fe, etc. that came from the tool steel were detected.

The fifth Industrial revolution (I5.0) prioritizes resilience and sustainability, integrating cognitive cyber-physical systems and advanced technologies to enhance machining processes. Numerous research studies have been conducted to optimize machining operations by identifying and reducing sources of uncertainty and estimating the optimal cutting parameters. Virtual modeling and Tool Condition Monitoring (TCM) methodologies have been developed to assess the cutting states during machining processes. With a precise estimation of cutting states, the safety margin necessary to deal with uncertainties can be reduced, resulting in improved process productivity. This paper reviews the recent advances in high-performance machining systems, with a focus on cyber-physical models developed for the cutting operation of difficult-to-cut materials using cemented carbide tools. An overview of the literature and background on the advances in offline and online process optimization approaches are presented. Process optimization objectives such as tool life utilization, dynamic stability, enhanced productivity, improved machined part quality, reduced energy consumption, and carbon emissions are independently investigated for these offline and online optimization methods. Addressing the critical objectives and constraints prevalent in industrial applications, this paper explores the challenges and opportunities inherent to developing a robust cyber–physical optimization system.

Turn-milling is a relatively new process which combines turning and milling operations, offering a number of advantages such as chip breaking and interrupted cutting, which improves tool life. In addition to providing the capability of producing eccentric forms or shapes, it increases productivity for difficult-to-machine material at lower cost. This study investigates the influence of cutting speed and feed on surface roughness and tool wear in conventional turning and turn-milling of H13 tool steel. The tests were conducted for longitudinal and face machining strategies. It was found that the range of surface roughness in turning is lower than in turn-milling. In longitudinal turning, face-turning, and face turn-milling operations, surface roughness is elevated in the higher feeds. However, the surface roughness in longitudinal turn-milling operations can be reduced by increasing the feed. Although the simultaneous rotation of the tool and workpiece in turn-milling could negatively affect the surface quality, this operation provides the advantage of an interrupted cutting mechanism that produces discontinuous chips. Also, the wear of the endmill in longitudinal and face turn-milling operations is lower than the wear of the inserts used in conventional longitudinal and face turning. Using Response Surface Methodology (RSM), mathematical models were developed for surface roughness and tool wear in each operation. The RSM models developed in this study achieved coefficients of determination (R²) above 90%, with prediction errors below 7% for surface roughness and below 3% for tool wear. The analysis of variance (ANOVA) revealed that the feed and cutting speed are the most influential parameters on the surface roughness and tool wear, respectively, with p-value < 0.05. The experimental results demonstrated that tool wear in turn-milling was reduced by up to 50% compared to conventional turning.

The fifth Industrial revolution (I5.0) prioritizes resilience and sustainability, integrating cognitive cyber-physical systems and advanced technologies to enhance machining processes. Numerous research studies have been conducted to optimize machining operations by identifying and reducing the sources of uncertainty and estimating the optimal cutting parameters. Virtual modeling and Tool Condition Monitoring (TCM) methodologies have been developed to assess the cutting states during machining processes. With a precise estimation of cutting states, the safety margin necessary to deal with the uncertainties can be reduced, resulting in improved process productivity. This paper reviews the recent advances in high-performance machining systems with a focus on the cyber-physical models developed for the cutting operation of difficult-to-cut materials using cemented carbide tools. An overview of the literature and background on the advances in the offline and online process optimization approaches have been presented. Process optimization objectives such as tool life utilization, dynamic stability, enhanced productivity, improved machined part quality, and reduced energy consumption and carbon emissions are independently investigated for the offline and online optimization methods. Addressing the critical objectives and constraints prevalent in industrial applications, the paper explores the challenges and opportunities inherent in developing a robust cyber-physical optimization system