Key Engineering Materials Vol. 968

Abstract: This work deals with an adaptable water reactor design built from different modules as a basis for research. These modules are selected according to the application and are used for sensor related cleaning and supporting tasks. For example, to produce a specific water quality or for pharmaceutical applications. Sensor related modules are used to measure various parameters such as temperature, TOC, flow parameters and others. In addition to simple membrane filter modules, UV-C disinfection and experimental modules are integrated into the setup. Modules for pumping processes, for power supply such as solar, for control tasks and the connection systems of water and electricity between modules are also outlined. This system is described on the basis of scientific examples that use this system. In more detail the modules for temperature, TOC measurement, and UV-C disinfection as well as the supply and control modules are shown.

Abstract: With the COVID 19 pandemic and the rise of polymerase chain reaction (PCR) testing in its wake, affordable large-scale testing became necessary. A thermal cycler for PCR has been developed that is affordable, fast, and accurate. The thermocycler must follow a specific temperature profile consisting of three different temperature levels. To ensure the success of any PCR, the tempering elements must accurately follow the tempering profile and be stable heat sources to maintain any given temperature. A control mechanism is required to approximate this prescribed temperature behavior. The basis for this is a Proportional-Integral-Differential (PID) controller, whose functionality is ensured by a feedback mechanism consisting of a temperature sensor. The PID controller provides precise attainment and maintenance of the temperature levels. A high performance Peltier element is used to heat and cool the PCR system. This is controlled by a programmable power supply and, in combination with a heat dissipation system, achieves heating and cooling powers in excess of 200 W. In addition, different design variants of the thermal cycler were created and their temperature behavior was simulated. These were then implemented, tested and the PID controller tuned accordingly. These tests help to find the best design for the thermal cycler with the optimal heat distribution.

Abstract: The observation, description, and ultimate prediction of causal connections between processing and resulting macroscopic properties stand at the heart of Materials Science and Engineering. To that end, the microstructure is the subject of intense examination, as it is ultimately responsible for the observed emergent behavior. As a result, many of the scientific or technical questions that we strive to answer boil down to quantitatively studying the—sometimes subtle—effects of processing on the microstructure in terms of known or hypothesized thermodynamic and kinetic phenomena. This statement is naturally also true in the case of hot isostatically pressed powder metallurgy tool steels. In the 50 years since the process' popularization, many parameters have been identified as relevant to microstructure formation during consolidation. Among these process variables, the powder solidification structure distribution is probably the last to join the list. Dendritic solidification during the atomization of relatively massive particles produces slightly elongated carbides. On the other hand, cellular solidification in smaller powder particles is responsible for smaller and more angular carbides. Characterizing powder solidification structure as a function of particle size presents two main challenges: First, the assessment relies on examining cross-sections of the powder particles, which are most likely non-diametric. And, second, the manual identification exercise is tedious and highly subjective. In this work, we show how we achieve fast and reliable powder structure solidification distributions using deep learning combined with state-of-the-art stereology reconstruction techniques.

Abstract: Interfacial thermal resistances of Al-Si alloys were evaluated by comparing the measured thermal conductivities and the simulated thermal conductivities. Al-7%Si, Al-18%Si and Al-18%Si held in the solid-liquid coexistence temperature for 90 minutes were fabricated by gravity casting. Thermal conductivities were measured with the steady state thermal conductivity measuring device. Thermal conductivities were also simulated by using optical microscope images. Comparing the measured thermal conductivities and the simulated thermal conductivities, interfacial thermal resistances in Al-Si interfaces were evaluated as about 2.5-4.8×10^-9 m²K/W.

Abstract: Optimizing the manufacturing process to increase the product quality is a major challenge most industry branches just like aluminum production have to face. To continuously improve the production quality, it is necessary to develop new methods to identify parameters which may influence the product quality. Influencing parameters can be found at various production steps. Production data is recorded by numerous sensors throughout the entire manufacturing process. The goal is to develop methods for analysing the sensor data from each step of the production process to effectively identify specific patterns that may indicate critical process parameters along the production chain. The work shows feature extraction methods to find characteristics in the sensor data that could affect the product-quality.

Abstract: An important aspect of New Product Development (NPD) is the determination of the expected product dimensional window, describing the maximum strip width, that can be produced for a given strip thickness. The estimation of the product dimensional window is used to safely execute first rolling trials. In addition, one can verify in advance whether customer geometry specifications of the final strip can be reached. For this purpose, offline simulation tools are used for hot rolling as well as cold rolling. An accurate prediction of the deformation resistance and interstand softening behaviour of the new steel grade is key in the determination of the dimensional window. Preferably, the deformation resistance model is validated with experimental data, for example from tensile tests or laboratory mini-mills. Rolling simulations are performed, using prescribed process conditions with respect to for example load distributions, temperature and rolling speed requirements. The dimensional windows of respectively the hot strip mill and the cold strip mill are merged, resulting in a final product dimensional window, indicating the maximum strip width at a final, customer specified, strip thickness.

Abstract: In recent years, methods from Data Science and Artificial Intelligence have become more and more important in various fields of economy and everyday life. Those methods are, for instance, used in context of driving assistance systems or queries in search engines. Our current works aims at developing and/or improving methods from statistics and machine learning to select relevant features concerning the product quality of aluminum ingots. During the production of aluminum, numerous process signals, such as temperature curves, are recorded. To quantify the dependency of the ingot-quality on different signals, existing statistical methods need to be adjusted and extended to the timeseries setting. The first problem tackled is the definition of a criterion numerically describing the quality of the ingots and therefore allowing to compare ingots with respect to their quality (independent of the final format of the product). A second, nontrivial challenge is to detect those process signals relevant for the ingot quality and account for possible interrelations. Our contribution sketches how timeseries information can be aggregated/discretisized and describes various candidate approaches for features selection.

Abstract: LVD Company NV and Kawasaki Heavy Industries, ltd. developed the cutting-edge, high performance CNC press brake [1] for high accuracy/precision bending of approx.10m (L) by 2.5m (W) size aircraft skins. Those skin sheets have complex shapes which include mainly machine milled thickness reduction area called pockets, thickness tapers, cutouts for the windows and doors. Due to those characters, the materials have large thickness variation between less than 2 mm and more than 11 mm in a single skin sheet. Here in this paper, the overview of this intelligence press brake equipment and its forming process are described. The materials with complex shapes described above can be bent accurately including material edges with the features of the press brake which are the synchronized material handling system, 210 numbers of variable punches, the special die suitable for the variable punches, the curvature measurement devices, and an automated bending mechanism with curvature feedback /feedforward. In addition, cardboard-like-filler jigs which are used to make thickness variations flat in a traditional bending process and shims to adjust regional press strokes can be eliminated, which reduce significant process time and product quality without worker’s superior skills. As a result, full automation of accurate bending process of aircraft skins have been achieved.

Abstract: As a producer of aluminium coils in a broad variety of applications AMAG faces challenges to control and monitor a long, multi-step production process with an immense number of parameters. Identifying impactful parameters or outliers becomes increasingly difficult when considering multiple production steps. Monitoring many coils over a big set of parameters manually is difficult, time consuming and error-prone and thus an unreasonable endeavour. To support employees in technology- and process-oriented domains, AMAG data scientists develop analytical tools for data exploration and data analysis. Based on material data containing mechanical properties in deformation tests, chemical composition, hot rolling temperature, intermediate annealing, and pre-heating duration we propose a framework of data collecting mechanisms and subsequent statistical methods to analyse and visualise data. The produced visuals can be interactively explored by material experts to gain better understanding of the complex interactions in production parameters and the effect on mechanical properties. Incorporating many coils at once, the framework offers a means to point out problems in process stability. A collaboration and a feedback loop between material scientists and data scientists is key to further develop advanced analytical methods.

Abstract: New groove pass series for long products must be developed by backward engineering starting with the necessary mechanical properties and geometry of the final shape, with a highly iterative manual effort and with numerous manual decisions. Usually, established groove sequences are adopted in this process without consideration of material requirements of the rolled materials and work roll limitations, while other groove shapes may provide better final results. In order to achieve precision in the material flow, time-consuming FE-Methods are already being used in the reverse engineering. The aim of the presented fast approach is to obtain an optimal roll contour for simple irregular groove sequences using generalized, roll-technical justified and experimentally evaluated design criteria coupled with a fast 3D approach for stress state, material flow and fast approaches for the assessment of the elastic stress state resp. work safety of the rolls. By direct coupling the material flow calculation e.g. with fast microstructure models the microstructure development and the required end properties are integrated into the optimization process. Parts of the calculation code discussed are available via the open source project PyRolL which is continuously updated by the Center of Groove Pass Design of TU Bergakademie Freiberg and free available within the terms of the BSD-3-Clause License [1]. The Python-based framework allows maximum adaptability to own needs via a flexible plugin system for inclusion of own models and routines.