Workpiece Temperature Investigation In Micromilling of Titanium Alloy

The study of the micromachining process of titanium alloys has been intensified due to a greater need to develop microcomponents applied to the medical area. However, the investigation of the behavior of process variables is still incipient, complex and challenging. Heat and temperature distribution, for example, are important aspects for any machining process. Thus, the main objective of this work is to investigate the temperature during the micro-milling of the Ti-6Al-4V alloy, by means of an experimental study combined with numerical simulation. To carry out the tests, AlCrN coated carbide end mill tool was used for machining micro slots. During machining, the workpiece temperature is measured using T-type thermocouples welded to the workpiece surface, aligned with the tool path. The temperature distribution was also obtained by numerical simulation using the commercial software Third Wave AdvantEdge, version 7.4015. The experimental results showed that the temperature on the surface of the specimen increases from the up-milling side to the down milling side. In addition, the temperature has a tendency to increase with tool wear. However, the simulation data showed that the temperature is higher in the center of the channel, the position with the highest undeformed chip thickness.

Inverse Identification of the Ductile Failure Law for Ti6Al4V Based on Orthogonal Cutting Experimental Outcomes

Despite the prevalence of machining, tools and cutting conditions are often chosen based on empirical databases, which are hard to be made, and they are only valid in the range of conditions tested to develop it. Predictive numerical models have thus emerged as a promising approach. To function correctly, they require accurate data related to appropriate material properties (e.g., constitutive models, ductile failure law). Nevertheless, material characterization is usually carried out through thermomechanical tests, under conditions far different from those encountered in machining. In addition, segmented chips observed when cutting titanium alloys make it a challenge to develop an accurate model. At low cutting speeds, chip segmentation is assumed to be due to lack of ductility of the material. In this work, orthogonal cutting tests of Ti6Al4V alloy were carried out, varying the uncut chip thickness from 0.2 to 0.4 mm and the cutting speed from 2.5 to 7.5 m/min. The temperature in the shear zone was measured through infrared measurements with high resolution. It was observed experimentally, and in the FEM, that chip segmentation causes oscillations in the workpiece temperature, chip thickness and cutting forces. Moreover, workpiece temperature and cutting force signals were observed to be in counterphase, which was predicted by the ductile failure model. Oscillation frequency was employed in order to improve the ductile failure law by using inverse simulation, reducing the prediction error of segmentation frequency from more than 100% to an average error lower than 10%.

Modelling and Simulation of Mechanical Loads and Residual Stresses in Deep Rolling at Elevated Temperature

Based on the concept of Process Signatures, the deep rolling process is analyzed, aiming at functional relationships between material modifications and internal material loads during the process. The focus of this work is to investigate the influence of the workpiece temperature on the generated residual stress components. For this purpose, extensive finite element simulations of deep rolling were conducted, taking into account the effect of neighboring tool paths on the internal material loads and residual stress. A kinematic strain hardening model was parameterized and utilized and the simulations were validated experimentally. Simulated residual stresses agree qualitatively well with measurements and show a strong influence of the workpiece temperature as expected. Process Signature Components were generated, taking into account the maximal and minimal residual stress as well as their respective positions beneath the workpiece surface.

Analytical model of workpiece temperature in axial ultrasonic vibration-assisted milling in-situ TiB2/7050Al MMCs

As a new method developed for machining difficult-to-cut materials, ultrasonic vibration-assisted machining technology has received increasing attention due to its superior properties in reducing cutting temperature in recent years. However, analytical models revealing the mechanism and predicting the cutting temperature for ultrasonic vibration-assisted machining are still needed to be developed. In this paper, an analytical model was established to predict the workpiece temperature for ultrasonic vibration-assisted milling of in-situ TiB2/Al-MMCs. The heat intensity would be directly determined by the cutting force which was significantly influenced by the ultrasonic vibration motion. Meanwhile, the moving heat source theory was applied for calculating dynamic heat flux and partition ratio. Besides, material properties, tool geometry, cutting parameters and vibration parameters were taken into account for workpiece temperature modeling. Finally, the developed analytical temperature model was validated by milling experiments with and without ultrasonic vibration on in-situ TiB2/7050Al metal matrix composites. The relative errors between model prediction results and experiments were smaller than 17%, indicating that the proposed model could provide workpiece temperature prediction reliability and accuracy. Furthermore, the established-analytical model could be used not only in ultrasonic vibration-assisted milling but also in conventional milling for the metal matrix composites.

Influence of the Cutting Strategy on the Temperature and Surface Flatness of the Workpiece in Face Milling

This article analyzes the temperature data obtained for an aluminum alloy face milled using four different cutting strategies. The workpiece temperature was measured at six points with K-type thermocouples. The heat transfer taking place in the cutting zone was also simulated numerically using the finite element method (FEM) and the finite difference method (FDM). The calculation results concerning the distribution of temperature on the workpiece surface were compared with the experimental data. The numerically simulated distribution of temperature on the workpiece surface after face milling was considered in relation to the surface flatness. The findings suggest that the flatness deviations at the workpiece ends were dependent on the depth of cut. Another reason was the cutting strategy selected for the specific thermophysical properties of the workpiece material. Measurement of the workpiece temperature is extremely important because of the thermoelastic behavior and thermal expansion of the material. The isotropic properties of the aluminum alloy make it expand in all directions during milling.

Conduction-Based Thermally Assisted Micromilling Process for Cutting Difficult-to-Machine Materials

The increasing demand for complex and wear-resistant forming tools made of difficult-to-machine materials requires efficient manufacturing processes. In terms of high-strength materials; highly suitable processes such as micromilling are limited in their potential due to the increased tool loads and the resulting tool wear. This promotes hybrid manufacturing processes that offer approaches to increase the performance. In this paper; conduction-based thermally assisted micromilling using a prototype device to homogeneously heat the entire workpiece is investigated. By varying the workpiece temperature by 20 °C < TW < 500 °C; a highly durable high-speed steel (HSS) AISI M3:2 (63 HRC) and a hot-work steel (HWS) AISI H11 (53 HRC) were machined using PVD-TiAlN coated micro-end milling tools (d = 1 mm). The influence of the workpiece temperature on central process conditions; such as tool wear and achievable surface quality; are determined. As expected; the temporary thermal softening of the materials leads to a reduction in the cutting forces and; thus; in the resulting tool wear for specific configurations of the thermal assistance. While only minor effects are detected regarding the surface topography; a significant reduction in the burr height is achieved.

A Finite Element Model for Temperature Prediction in Laser-Assisted Milling of AerMet100 Steel

Laser-assisted milling (LAM) represents an innovative process to enhance productivity in comparison with conventional milling. The workpiece temperature in LAM not only affects the cutting performance of materials, but also the machined surface quality of the part. This paper presents a 3D transient finite element (FE) model for workpiece temperature prediction in LAM. A moving Gaussian laser heat source model is implemented as a user-defined subroutine and linked to ABAQUS. The thermal model is validated by machining AerMet100 steel under different process parameters (laser power, spindle speed and feed per tooth). Good agreement between predicted and measured workpiece temperatures indicates that the FE model is feasible. In addition, the effects of laser spot size and incident angle on workpiece temperature are analyzed based on the proposed model. This work can be further applied to optimize process parameters for controlling the machined surface quality in LAM.