A cost effective approach to enhance surface integrity and fatigue life of precision milled forming and forging dies

Previously held under moratorium from 8 August 2019 until 19 January 2022The machining process determines the overall quality of produced forming and forging dies, including surface integrity. Previous research found that surface integrity has a significant influence on the fatigue life of the dies. This thesis aims to establish a cost-effective approach for precision milling to obtain forming and forging dies with good surface integrity and long fatigue life. It combined experimental study accompanied by Finite Element Modelling and Artificial Intelligence soft modelling to predict and enhance forming and forging die life. Four machining parameters, namely Surface Speed, Depth of cut, Feed Rate and Tool Lead Angle, each with five levels, were investigated experimentally using Design of Experiment. An ANOVA analysis was carried out to identify the key factor for every Surface Integrity (SI) parameter and the interaction of every factor. It was found that the cutting force was mostly influenced by the tool lead angle. The residual stress and microhardness were both significantly influenced by the surface speed. However, on the surface roughness it was found that the feed rate had the most influence. After the machining experiments, four-point bending fatigue tests were carried out to evaluate the fatigue life of precision milled parts at an elevated temperature in a low cycle fatigue set-up imitated for the forming and forging production. It was found that surface roughness and hardness were the most influential factors for fatigue life. A 3D-FE-Modelling framework including a new material model subroutine was developed; this led to a more comprehensive material model. A fractional factorial simulation with over 180 simulations was carried out and validated with the machining experiment. Based on the experimental and simulation results, a soft prediction model for surface integrity was established by using Artificial Neural Networks (ANN) approach. These predictions for SI were then used in a Genetic Algorithm model to optimise the SI. The confirmation tests showed that the machining strategy was successfully optimised and the average fatigue duration was increased by at least a factor of two. It was found that a surface speed of 270 m/min, a feed rate of 0.0589 mm/tooth, a depth of cut of 0.39 mm and a tool lead angle of 16.045° provided the good surface integrity and increased fatigue performance. Overall, these findings conclude that the fundamentals and methodology utilised have developed a further understanding between machining and forming/forging process, resulting in a good foundation for a framework to generate FE and soft prediction models which can be used to in optimisation of precision milling strategy for different materials.The machining process determines the overall quality of produced forming and forging dies, including surface integrity. Previous research found that surface integrity has a significant influence on the fatigue life of the dies. This thesis aims to establish a cost-effective approach for precision milling to obtain forming and forging dies with good surface integrity and long fatigue life. It combined experimental study accompanied by Finite Element Modelling and Artificial Intelligence soft modelling to predict and enhance forming and forging die life. Four machining parameters, namely Surface Speed, Depth of cut, Feed Rate and Tool Lead Angle, each with five levels, were investigated experimentally using Design of Experiment. An ANOVA analysis was carried out to identify the key factor for every Surface Integrity (SI) parameter and the interaction of every factor. It was found that the cutting force was mostly influenced by the tool lead angle. The residual stress and microhardness were both significantly influenced by the surface speed. However, on the surface roughness it was found that the feed rate had the most influence. After the machining experiments, four-point bending fatigue tests were carried out to evaluate the fatigue life of precision milled parts at an elevated temperature in a low cycle fatigue set-up imitated for the forming and forging production. It was found that surface roughness and hardness were the most influential factors for fatigue life. A 3D-FE-Modelling framework including a new material model subroutine was developed; this led to a more comprehensive material model. A fractional factorial simulation with over 180 simulations was carried out and validated with the machining experiment. Based on the experimental and simulation results, a soft prediction model for surface integrity was established by using Artificial Neural Networks (ANN) approach. These predictions for SI were then used in a Genetic Algorithm model to optimise the SI. The confirmation tests showed that the machining strategy was successfully optimised and the average fatigue duration was increased by at least a factor of two. It was found that a surface speed of 270 m/min, a feed rate of 0.0589 mm/tooth, a depth of cut of 0.39 mm and a tool lead angle of 16.045° provided the good surface integrity and increased fatigue performance. Overall, these findings conclude that the fundamentals and methodology utilised have developed a further understanding between machining and forming/forging process, resulting in a good foundation for a framework to generate FE and soft prediction models which can be used to in optimisation of precision milling strategy for different materials

Prediction of residual stress in precision milling of AISI H13 steel

Surface integrity describes the attributes of a surface and it influences the functional performance of a work piece significantly. Residual stress is one of the major characterization parameters of surface integrity. Non-favorable residual stresses on a machined surface can reduce the fatigue life and performance of the machined part. It therefore requires a prediction model for residual stress in order to establish machining strategy to obtain favorable residual stress for prolonged fatigue life. Hardened tool steels have been widely used to make molds and dies by precision milling in aerospace and automotive industries. Knowledge of the relationship between residual stress on the machined molds and machining conditions is very important for process control. In this work, a prediction model for residual stress was developed by using a model-based approach on an Artificial Neural Network. This model is expected to predict the residual stress based on cutting parameters such as cutting speed, feed rate, depth of cut and tool lead angle. Several precision milling trials were carried out using a central composite design method. The networks have been trained and validated by experimental results. The performance of a feed forward neural network model with backpropagation was assessed and compared with a radial basis function network model by criterion of least mean squared error. Furthermore, the neural network prediction model was supported by the finite element simulation of the milling process to understand the formation mechanism of the residual stress in the machined surface. It was found, that the predicted values by the neural network model matched well with the experimental results. The radial basis function network showed better results than the feed forward network and was therefore chosen to take forward in the analysis. The feed rate was in this case the most influential factor, because it contributes significantly to heat and deformation on the work piece. The model could be used to optimize machining processes to obtain machining strategy for generating favorable residual stress and increasing fatigue life performance of the machined parts

Novel Multimodal Feedback Techniques for In-Car Mid-Air Gesture Interaction

This paper presents an investigation into the effects of different feedback modalities on mid-air gesture interaction for infotainment systems in cars. Car crashes and near-crash events are most commonly caused by driver distraction. Mid-air interaction is a way of reducing driver distraction by reducing visual demand from infotainment. Despite a range of available modalities, feedback in mid-air gesture systems is generally provided through visual displays. We conducted a simulated driving study to investigate how different types of multimodal feedback can support in-air gestures. The effects of different feedback modalities on eye gaze behaviour, and the driving and gesturing tasks are considered. We found that feedback modality influenced gesturing behaviour. However, drivers corrected falsely executed gestures more often in non-visual conditions. Our findings show that non-visual feedback can reduce visual distraction significantl

A consensus map of QTLs controlling the root length of maize

Traits related to the root length of maize (Zea mays L.), reported by 15 QTL studies of nine mapping populations, were subjected to a QTL meta-analysis. Traits were grouped according to ontology, and we propose a system of abbreviations to unambiguously identify the different root types and branching orders. The nine maps were merged into a consensus map, and the number and positions of putative QTL clusters (MQTLs) were determined. A total of 161 QTLs was grouped into 24 MQTLs and 16 individual QTLs. Seven MQTLs harbored root traits, which had been reported to be collocated with QTLs for grain yield or other drought-responsive traits in the field. The most consistent collocations were observed for the number and weight of the seminal roots (five loci). Based on our analysis at least six loci are good candidates for further evaluation (bins 1.07, 2.04, 2.08, 3.06, 6.05 and 7.04). For example, the MQTL in bin 2.04 harbored ten different single QTLs; the MQTLs in bins 1.07 and 3.06 combined 11 and 7 QTLs, respectively, that were detected in more than three populations. The presented database is a first step for a comprehensive overview of the genetic architecture of root system architecture and its ecophysiological functio

A full factorial numerical investigation and validation of precision end milling process for hardened tool steel

Tool steel materials have poor machinability, as the high hardness of the material will cause high cutting forces, premature failure of the cutting tools, and is also associated with machining induced tensile stresses within the work piece. Due to high experimental costs, there is no recent research on end milling tool steel, using full factorial experimental or numerical design. A 3D FE-model of a precision end milling process with a two flute ball nose cutter were established in this paper. The FE-Model used a subroutine to model hardening realised through the Johnson-Cook model, additionally were a material removal criteria developed and implemented. Through full factorial numerical simulations the influence of cutting parameters on cutting force of H13 tool steel was studied. Depth of cut was found to be the most influential machining parameter on cutting forces followed by feed rate and surface speed. Four milling experiments were carried out to validate the simulation results. It was found that the simulation and the experiments had a good agreement on the cutting forces. The validated FEA model can be used for further studies on residual stress or temperatures and to optimise the cutting process

Microbialite formation in seawater of increased alkalinity, Satonda Crater Lake, Indonesia

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Multidisziplinäre Simulation des Wirbelschleppen Durchfluges eines Flugzeuges mit dem DLR TAU-Code

Ausgangssituation: Für die Auslegung eines Flugzeuges sind eine Vielzahl unterschiedlicher Lastfälle zu berücksichtigen. Auf der einen Seite wird das Flugzeug für den Reiseflug optimiert, um eine möglichst große Reichweite bei geringem Brennstoffverbrauch zu erzielen. Auf der anderen Seite muss sichergestellt werden, dass ein Flugzeug auch in kritischen Situationen, wie beispielsweise der Begegnung mit einer kräftigen Böe oder der Wirbelschleppe eines voreilenden Flugzeuges, beherrschbar ist und den zusätzlichen Belastungen standhält. Um die zusätzlichen aerodynamischen Lasten vorherzusagen, werden heute in der Regel vereinfachte Methoden basierend auf Streifentheorie oder Doublet-Lattice-Methoden verwendet. Dadurch sind insbesondere bei hohen Fluggeschwindigkeiten (Kompressibilitätseffekte, Nichtlinearitäten) Vorhersagefehler der einfachen Methoden zu erwarten, weshalb entsprechend hohe Sicherheitsfaktoren aufgeschlagen werden. Das führt unter Umständen zu einer deutlichen Überdimensionierung der Struktur, und damit zu einem erhöhten Flugzeuggewicht. Ziel: Um die Genauigkeit bei der Vorhersage der zusätzlichen durch Wirbelschleppen induzierten Lasten gegenüber oben angesprochenen einfachen Verfahren zu verbessern, soll im DLR RANS-Löser TAU die Möglichkeit geschaffen werden, Wirbelschleppen-Begegnungen von Flugzeugen zu simulieren. Dabei soll auch die Reaktion des Flugzeuges in Folge der Lasten durch Kopplung zur Flugmechanik Berücksichtigung finden. Lösungsweg: Verschiedene Autoren haben in Euler- bzw. RANS-Verfahren den sogenannten Störgeschwindigkeitsansatz implementiert, bei dem die durch Böen induzierten Störungen in Form von Störgeschwindigkeiten als Funktion vom Raum und der Zeit vorgegeben werden können. Von Vorteil ist, das die atmosphärischen Störungen in der Simulation im Strömungsfeld nicht numerisch aufgelöst werden müssen. Es können Standardnetze Verwendung finden, was gegenüber der Auflösung der atmosphärischen Störungen eine effiziente numerische Behandlung verspricht. Dieses Verfahren ist für Böen-Begegnungen auch im TAU-Code implementiert und erfolgreich eingesetzt worden. Inzwischen ist es für Wirbelschleppenbegegnungen erweitert worden. Die durch die Wirbelschleppe induzierten Geschwindigkeiten werden durch Überlagerung zweier gegenläufiger „Burnham-Halloc“ Wirbel modelliert. Als Beispiel für einen Wirbelschleppen-Durchflug wurde die Interaktion eines generischen Kampfflugzeuges mit einer Wirbelschleppe eines voraus fliegenden Flugzeuges erfolgreich demonstriert. Neben der Aerodynamik wird auch die Flugmechanik berücksichtigt, um die Reaktion des Flugzeuges in Folge der Wirbelschleppe und von Steuerbewegungen zu erfassen