Cutting fluids are an important part of today’s metal cutting processes, especially when machining aerospace alloys. They offer the possibility to extend tool life and improve cutting performance. However, the equipment and handling of cutting fluids also raises manufacturing costs. To reduce the negative impact of the high cost of cutting fluids, cooling systems and strategies are constantly being optimized. In most existing works, the influences of different cooling strategies on the relevant process state variables, such as tool wear, cutting forces, chip breakage, etc., are empirically investigated. Due to the limitations of experimental methods, analysis and modeling of the working mechanism has so far only been carried out at a relatively abstract level. For a better understanding of the mechanism of cutting fluids, a thermal coupled two-dimensional simulation approach for the orthogonal cutting process was developed in this work. This approach is based on the Coupled Eulerian Lagrangian (CEL) method and provides a detailed investigation of the cutting fluid’s impact on chip formation and tool temperature. For model validation, cutting tests were conducted on a broaching machine. The simulation resolved the fluid behavior in the cutting area and showed the distribution of convective cooling on the tool surface. This work demonstrates the potential of CEL-based cutting fluid simulation, but also pointed out the shortcomings of this method.

In metal cutting, a large amount of mechanical energy converts into heat, leading to a rapid temperature rise. Excessive heat accelerates tool wear, shortens tool life, and hinders chip breakage. Most existing thermal studies have focused on dry machining, with limited research on the effects of cutting fluids. This study addresses that gap by investigating the thermal behavior of cutting tools during continuous and interrupted turning with cutting fluid. Tool temperatures were first measured experimentally by embedding a thermocouple in a defined position within the tool. These experimental results were then combined with simulations to evaluate temperature changes, heat partition, and cooling efficiency under various cutting conditions. This work presents novel analytical and numerical models. Both models accurately predicted the temperature distribution, with the analytical model offering a computationally more efficient solution for industrial use. Experimental results showed that tool temperature increased with cutting speed, feed, and cutting depth, but the heat partition into the tool decreased. In continuous cutting, cooling efficiency was mainly influenced by feed rate and cutting depth, while cutting speed had minimal impact. Interrupted cutting improved cooling efficiency, as the absence of chips and workpieces during non-cutting phases allowed the cutting fluid to flow over the tool surface at higher speeds. The convective cooling coefficient was determined through inverse calibration. A comparative analysis of the analytical and numerical simulations revealed that the analytical model can underestimate the temperature distribution for complex tool structures, particularly non-orthogonal hexahedral geometries. However, the relative error remained consistent across different cutting conditions, with less error observed in interrupted cutting compared to continuous cutting. These findings highlight the potential of analytical models for optimizing thermal management in metal turning processes.

The cooling with cutting fluids is a complex process in manufacturing, since chemical, mechanical and thermal phenomena occur simultaneously. The experimental methods developed so far do not allow for a direct observation of the coolant flow during machining, which limits the understanding of the cooling mechanism. The main aim of this paper is the investigation of convective heat transfer between cutting fluid and the cutting zone as well as the heat flow distribution on the tool surface during the cutting process by means of a coupled FE-CFD simulation. The FE model calculates the heat generation under consideration of the experimentally validated punctual, transient tool temperature during the machining processes. Based on the result of the FE, the subsequent CFD simulation performs the calculation of the flow behavior and convective heat transfer. This method allows a detailed investigation of the temperature field in the cutting zone under consideration of the cutting fluid. The simulation returns spatially resolved heat transfer coefficients along the tool surface and provides first findings for an improvement of heat removal efficiency by changing the coolant supply parameters and the physical properties of the cutting fluid. The model parameters were validated by comparing the simulation results and the measured punctual tool temperature.

Cutting simulation is a crucial tool that enables engineers and operators to optimize machining processes virtually, before producing physical parts. The accuracy of these simulations relies heavily on validated models, encompassing both friction and material parameters. The prevalent technique for calibrating material models in cutting simulations is the inverse method. This state-of-the-art approach indirectly determines model parameters by comparing simulated outcomes with experimental data. However, the manual calibration process can be complex and time-consuming due to the intricacies of numerical simulation setups and the abundance of material model parameters. To address these challenges, this paper presents a novel fully-automated calibration approach utilizing multi-objective optimization algorithms. This approach integrates a modular design, simplifying the calibration process and enabling automatic calibration of any model parameters within cutting simulations. The approach has been successfully applied to calibrate the model parameters of AISI 1045 and X30CrMoN15-1 materials. Moreover, through a comparison of various optimization algorithms, this paper underscores the efficiency of the swarm optimizer in calibrating model parameters, particularly in scenarios with restricted computational resources.