Volume 87, Issue 11 - Surface conditioning in machining processes Oberflächenkonditionierung in der Zerspanung / Gastherausgeber: Volker Schulze

In this study, the influence of cutting speed and feed rate on surface integrity in Boring Trepanning Association (BTA) deep hole drilling of AISI 4140+QT is investigated. Microstructure and micro-hardness in the subsurface zones of bores are analyzed, using metallographic and micromagnetic methods. It was found that when using high feed rates and cutting speeds, white etching layers (WEL) form at the surface of the bores. These layers are up to three times harder than the substrate material and have a maximum thickness of approx. tWEL≈12µm{t_{\mathrm{WEL}}}\approx 12\hspace{0.1667em}\text{\textmu m}. WEL were usually followed by a transitional layer, so that elevated hardness was observed until a depth of dsurf=35µm{d_{\mathrm{surf}}}=35\hspace{0.1667em}\text{\textmu m} below the surface. Magnetic Barkhausen noise (MBN) analysis proved to be applicable for the fast and reliable detection of WEL. The presented results contribute to gaining a deeper understanding of the complex interrelations between the design of the BTA process, the resulting microstructure in the machined component and the properties of the conditioned surface. Based on discovered correlations, a dynamic process control will be developed for BTA deep hole drilling, which will allow reliably tailoring surface integrity of components to specific demands, like an optimized fatigue performance.

Workpiece rim zone modifications during hard machining can be explained with the high thermo-mechanical loads induced by the cutting process. The formation of White Layers with a fine-grained microstructure by dynamic recrystallization (DRX) is one of those surface modifications that can negatively affect the functionality of a machined part by changing the residual stress state and facilitating crack initiation. As a consequence, the fatigue life of the machined parts is reduced. It is therefore of great interest to understand the thermo-mechanical conditions which induce White Layers formation in order to be able to control them by in-situ measurements if necessary. For this purpose, a cutting force based Soft-Sensor is developed in this study which enables the in-process estimation of White Layer thickness. Therefore, a cutting force based analytical model is used to estimate the resulting temperature fields and correlated with validated numerical chip formation simulations. In addition, the predictions of the White Layer thickness of the analytical model are then compared using light microscopy and the results of the numerical finite element model, in which a DRX model is additionally implemented.

The surface layer states of a component are of great importance for the application of hard turned parts. Without a multitude of tests and experience, the states cannot be reliably produced due to process-typical disturbance variables such as tool wear and batch inclusions. The aim of this research is to control the surface layer states during cryogenic hard turning of AISI 52100 by feedforward control and the use of a soft sensor. This paper presents the measurement techniques used in this context. On the one hand, the in-process measurement technology and challenges involved are discussed. On the other hand, the challenges in the ex-situ measurement technique for characterizing the surface layer states are shown using micro hardness measurement as an example.

In order to withstand high mechanical and tribological loads, it is important that the components not only have a high core ductility but also a hard surface. Typically, a suitable microstructure is created by heat treatment processes before the workpiece is machined. However, these processes are time and energy consuming and can lead to component distortion. It would therefore be of great advantage if no additional heat treatment process would be required to produce a hardened subsurface zone. Since turning is often already integrated as a machining process in production lines, it would be advantageous to create a hardened subsurface within this process. As there is no possibility to measure the hardness directly during the turning process, a soft sensor was developed to determine the properties of the subsurface directly during the machining process. Steels with metastable austenite are of particular interest in this context, as metastable austenite can be converted into martensite by deformation. The amount of martensite produced in the subsurface can be adjusted provided that suitable turning parameters can be found. For this purpose, a process parallel material removal simulation was used to determine the actual conditions governing the process. It was found that there is a correlation between the martensite content and the amplitude of the 3 rd harmonic of eddy current testing. Therefore, an eddy current sensor accompanying the process can be used as a basis for controlling the turning process for tailored martensite volume content adjustment.

This work investigates the relationship between acoustic emission and chip segmentation frequency of Ti-6Al-4V at the external longitudinal turning process. Therefore, several sensors like structure borne sensors, microphones and a force dynamometer have been installed in a vertical turning machine. To induce a change of the segmentation frequency, several experiments with different feed rates have been carried out. From each experiment the acoustic emissions have been recorded and the generated chips have been analyzed. Since the chips get stretched or compressed during the chip formation the change in the length is calculated to get an estimation of the segmentation frequency. The comparison of the spectral analysis of the acoustic emission signals and the chip analysis has shown that both methods show the same tendency. The segmentation frequency decreases with increasing feed.

The titanium alloy Ti-6Al-4V represents a significant metal portion of state-of-the-art aircraft structural and engine components. When critical structural components in the aerospace industry are manufactured with the objective to reach high reliability levels, surface integrity is one of the most relevant parameters used for evaluating the quality of machined surfaces. The residual stresses and the surface alteration induced by machining titanium alloys are critical due to safety and sustainability issues. In this paper, a series of end milling experiments was conducted to comprehensively characterize the surface integrity at various milling conditions. The experimental results have shown that the surface roughness value increases with the feed and the cutting velocity. However, the residual stress state in the surface layer zone is influenced by the variation of the process control variables. Here, compressive residual stresses occur both in cutting and in feed direction. In addition, a new type of sensory tool holder is presented, which should enable the indirect measurement of residual stresses during the milling process.

In this article, two data-driven modeling approaches are investigated, which allow an explicit modeling of uncertainty. For this purpose, parametric Takagi-Sugeno multi-models with bounded-error parameter estimation and nonparametric Gaussian process regression are applied and compared. These models can for instance be used for robust model-based control design. As an application, the prediction of residual stresses during hard turning depending on the machining parameters and the initial hardness is considered.