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Multi-droplet impingement is a fundamental aspect inherent to all kinds of technical spray processes which typically aim at enhancing the convective exchange of reagents or heat at the impinged surface. In this paper, the impingement of multiple droplets onto a structured surface is investigated by a comprehensive CFD model, which resolves the dynamics of the individual droplets and the film on a micro-scale level based on the Volume of Fluid (VOF) method. The considered surface topology includes cavities and is typical for protective masks used in the spray etching of Printed Circuit Boards (PCBs). The agitation of the liquid film in terms of the convective mass transfer rates across virtual horizontal evaluation planes is studied and the influence of film height and droplet impaction velocity is elaborated. Passive tracer tracking is employed to investigate the release and re-entrainment of fluid at the surface cavities. Two modes of mass exchange between the cavities and the main flow upon droplet impingement are identified, which are central inflow accompanied by lateral outflow (1) and lateral inflow with outflow at the opposing side (2). A statistical analysis of the allocation of tracer particles shows that high impaction velocities and low film heights correlate with an enhanced decay of tracer particles within the cavities. The susceptibility to re-entrainment is also reduced by high impaction velocities, whereas increased film heights are found to promote re-entrainment.

The evolution of microstructural features such as local grain size and local grain size distribution are essential in view of the final physical and mechanical properties of the nickel base alloy 718 for aircraft parts forged in a multi-step production route. Due to increasing standards and the need of the prediction of fracture mechanical properties, a multi-class grain size model for a more detailed microstructure prediction is necessary. Therefore, a multi-class model considers the real initial non-uniform grain size distribution and structure of the pre-material at the beginning of the forging process, which affects the evolution of grain sizes during thermo-mechanical treatment and leads to different results than commonly used uniform grain structures. The initial distribution is defined by grain classes according the ASTM standard. It is shown that the presence of different classes and distributions of grains are as import as the applied strain, strain rate and temperature on dynamic, meta-dynamic and static recrystallization. Additionally, dissolution processes of delta phase and grain growth kinetics are included in the model to properly indicate the recrystallized fractions and represent the resulting multi-class microstructure. A series of simulations with different initial distributions is discussed and compared with examined forged samples in terms of the resulting microstructure for typical forging parameters. Based on these results the microstructure model can be used in combination with collected process data to predict the resulting properties and for the design of new aircraft parts.

Process control in surface hardening depends greatly on the repeatability of the results. Induction heating facilities stand out in this aspect but challenges arise when it comes to the verification of the expected temperatures. In-situ temperature measurement of a workpiece may be made impossible due to it moving through an enclosed, automated induction facility that lacks built-in sensors. This paper uses transition patterns in the microstructure of the hardened region to reconstruct isothermal contour lines of the temperature field during austenitisation. It does so based on a continuous cooling transformation phase diagram and a time-temperature-austenitisation diagram of the considered steel. The presented method serves as a practical approach to validate simulations of the inductive austenitising process and supports simulations of the heat treatment of the work piece. Once these simulations have been iterated upon and validated thoroughly, they may then yield a reconstruction of the entire temperature field during the heat treatment process.

In this work, we present and test an approach based on an inverse model applicable to the control of induction heat treatments. The inverse model is comprised of a simplified analytical forward model trained with experiments to predict and control the temperature of a location in a cylindrical sample starting from any initial temperature. We solve the coupled nonlinear electromagnetic-thermal problem, which contains a temperature dependent parameter &alpha, to correct the electromagnetic field on the surface of a cylinder, and as a result effectively the modeled temperature elsewhere in the sample. A calibrated model to the measurement data applied with the process information such as the operating power level, current, frequency, and temperature provides the basic ingredients to construct an inverse model toolbox, which finally enables us to conduct experiments with more specific goals. The input set values of the power supply, i.e., the power levels in the test rig control system, are determined within an iterative framework to reach specific target temperatures in prescribed times. We verify the concept on an induction heating test rig and provide two examples to illustrate the approach. The advantages of the method lie in its simplicity, computationally cost effectiveness and independence of a prior knowledge of the internal structure of power supplies.

A simple hybrid model, integrating observation (black-box) and physical knowledge (white-box), is employed to model an induction heating process. A neural network is used to estimate the unknown physical process parameter in the physical model. Most relevant to induction hardening is the temperature evolution in a layer under the surface of a sample, in our case a cylindrical sample. We show that with a hybrid model, in which a simple ordinary differential equation describes the heating rate, the experimental data can be approximated better than using a black-box only. The hybrid model extrapolates better and it is easier to interpret. The hybrid model can be used as a prediction tool to operate and optimize induction heating processes.

Dynamic and meta-dynamic recrystallization occur during forging of alloy 718 aircraft parts and thus change the microstructure during a multistep production route. Since the prediction of the resulting grain structure in a single grain fraction is not able to describe microstructures with bimodal or even multimodal distributions, a multi-class grain size model has been deployed to describe the recrystallization mechanisms during thermomechanical treatments and predict the resulting grain size distributions more accurately. As forging parameters, such as temperature, strain rate and maximum strain influence the flow curve and consequently the recrystallization behavior, a series of double cone compression experiments has been carried out and used to verify and adapt the material parameters for the multi-class grain size model. The recrystallized fractions of the numerical and experimental results are compared and differentiated in view of the recrystallization mechanism, i.e., dynamic and meta-dynamic recrystallization. The strong dependence of the recrystallization kinetics on the initial grain size is highlighted, as well as the influence of different strain rates, which shall represent typical forging equipment.

As the optimization of highly demanding engine parts like engine disks pushes the materials used to their limits, the estimation of the material properties becomes increasingly important. The complex interactions of strengthening mechanisms in direct aged (DA) Alloy 718 forgings demand detailed modeling of each mechanism. As some strengthening mechanisms are influenced during the billet processing it is essential to consider the forging stock manufacturing in the design process of forged engine disks. Therefore, a finite element analysis of the billet processing was incorporated in an existing simulation chain of the closed die forging process of engine disks. It includes all thermo-mechanical operations after vacuum arc remelting, i.e. homogenization, upsetting, drawing and radial forging of the billet as well as prepressing, forging and heat treatment of a disk. In order to implement the local variations of microstructure caused by the billet processing a newly developed grain class model was applied. Furthermore a duplex microstructure, which is often present in the surface region of the billet, was considered using the grain class model. For a sound precipitation modeling the local temperature history of the whole simulation chain was considered in the thermo-kinetic software tool MatCalc. After parameterization of the MatCalc model, parameters for the precipitation, solid solution and grain boundary contribution to the total yield strength was calculated. For the determination of the DA-effect a semi-empirical, deterministic model was developed. The established, automated simulation chain takes all mentioned mechanisms into account and calculates the local yield strength of the forged engine disk.

The numerical investigation of common heat treated A356 as-cast parts was successfully exhibited by several authors for T4, T6 and T7 tempers. However, for non-uniform or local heat treatments (e.g. welding) the common procedure of estimating the material properties cannot be used. Therefore, a new methodology was developed in order to overcome the weak point in the kinetic calculation and to enable the prediction of the material properties in 3D. The single steps of the methods are described while mentioning interesting aspects briefly. Subsequently, a local heat treated structural A356 component case is presented while taking care about remelting and yielding. At the end possible areas of applications are given in which the new method can be applied.

Pressure casting dies are exposed to harsh service conditions consisting of cyclic thermal and mechanical loading and thus undergo thermo-mechanical fatigue. Due to cyclic plastic deformation of the material near the surface of the dies the loading conditions gradually change because of the formation of tensile residual stresses which add to the stress field from external loading. This change in the stress field influences the nucleation and the growth of cracks. Typically after a few thousand casting cycles a network of heat checks forms. In such a network crack shielding has a big influence on the evolution of the crack array. Firstly, it influences the propagation rates of the cracks and secondly it may change the propagation direction compared to the case where no neighbors are present. The crack growth rate and the length at which the cracks stop growing are also influenced by the thermo-physical and mechanical properties of the die material. It was found that the shielding effects of neighboring cracks are of equal importance. Crack deflection caused by the presence of neighboring cracks can lead to break-outs at the surface ensued by fast degradation eventually necessitating the replacement of the die. Consequently, the focus in this work is put on the investigation of the interaction of cracks in a network and their effect on the fatigue life. The problem is tackled by means of an automated strategy based on the finite element method.

Induction heating processes are of rising interest within the heat treating industry. Using inductive tempering, a lot of production time can be saved compared to a conventional tempering treatment. However, it is not completely understood how fast inductive processes influence the quenched and tempered microstructure and the corresponding mechanical properties. The aim of this work is to highlight differences between inductive and conventional tempering processes and to suggest a possible processing route which results in optimized microstructures, as well as desirable mechanical properties. Therefore, the present work evaluates the influencing factors of high heating rates to tempering temperatures on the microstructure as well as hardness and Charpy impact energy. To this end, after quenching a 50CrMo4 steel three different induction tempering processes are carried out and the resulting properties are subsequently compared to a conventional tempering process. The results indicate that notch impact energy raises with increasing heating rates to tempering when realizing the same hardness of the samples. The positive effect of high heating rate on toughness is traced back to smaller carbide sizes, as well as smaller carbide spacing and more uniform carbide distribution over the sample.