Developing of Ultrasound Experimental Methods using Machine Learning Algorithms for Application of Temperature Monitoring of Nano-Bio-Composites Extrusion

In industry fiber degradation during processing of biocomposite in the extruder is a problem that requires a reliable solution to save time and money wasted on producing damaged material. In this thesis, We try to focus on a practical solution that can monitor the change in temperature that causes fiber degradation and material damage to stop it when it occurs. Ultrasound can be used to detect the temperature change inside the material during the process of material extrusion. A monitoring approach for the extruder process has been developed using ultrasound system and the techniques of machine learning algorithms. A measurement cell was built to form a dataset of ultrasound signals at different temperatures for analysis. Machine learning algorithms were applied through machine-learning algorithm’s platform to classify the dataset based on the temperature. The dataset was classified with accuracy 97% into two categories representing over and below damage temperature (190oc) ultrasound signal. This approach could be used in industry to send an alarm or a temperature control signal when material damage is detected. Biocomposite is at the core of automotive industry material research and development concentration. Melt mixing process was used to mix biocomposite material with multi-walled carbon nanotubes (MWCNTs) for the purpose of enhancing mechanical and thermal properties of biocomposite. The resulting composite nano-bio- composite was tested via different types of thermal and mechanical tests to evaluate its performance relative to biocomposite. The developed material showed enhancement in mechanical and thermal properties that considered a high potential for applications in the future

Real-time prediction and adaptive adjustment of continuous casting based on deep learning

Digitalisation of metallurgical manufacturing, especially technological continuous casting using numerical models of heat and mass transfer and subsequent solidification has been developed to achieve high manufacturing efficiency with minimum defects and hence low scrappage. It is still challenging to perform adaptive closed-loop process adjustment using high-fidelity computation in real-time. To address this challenge, surrogate models are a good option to replace the high-fidelity model, with acceptable accuracy and less computational time and cost. Based on deep learning technology, here we developed a real-time prediction (ReP) model to predict the three-dimensional (3D) temperature field distribution in continuous casting on millisecond timescale, with mean absolute error (MAE) of 4.19 K and mean absolute percent error (MAPE) of 0.49% on test data. Moreover, by combining the ReP model with machine learning technology—Bayesian optimisation, we realised the rapid decision-making intelligent adaptation of the operating parameters for continuous casting with high predictive capability. This innovative and reliable method has a great potential in the intelligent control of the metallurgical manufacturing process

Refining and Casting of Steel

Steel has become the most requested material all over the world during the rapid technological evolution of recent centuries. As our civilization grows and its technological development becomes connected with more demanding processes, it is more and more challenging to fit the required physical and mechanical properties for steel in its huge portfolio of grades for each steel producer. It is necessary to improve the refining and casting processes continuously to meet customer requirements and to lower the production costs to remain competitive. New challenges related to both the precise design of steel properties and reduction in production costs are combined with paying special attention to environmental protection. These contradictory demands are the theme of this book

Process Modeling in Pyrometallurgical Engineering

The Special Issue presents almost 40 papers on recent research in modeling of pyrometallurgical systems, including physical models, first-principles models, detailed CFD and DEM models as well as statistical models or models based on machine learning. The models cover the whole production chain from raw materials processing through the reduction and conversion unit processes to ladle treatment, casting, and rolling. The papers illustrate how models can be used for shedding light on complex and inaccessible processes characterized by high temperatures and hostile environment, in order to improve process performance, product quality, or yield and to reduce the requirements of virgin raw materials and to suppress harmful emissions

Prediction of potential evapotranspiration using temperature-based heuristic approaches

The potential or reference evapotranspiration (ET0) is considered as one of the fundamental variables for irrigation management, agricultural planning, and modeling different hydrological pr?Cesses, and therefore, its accurate prediction is highly essential. The study validates the feasibility of new temperature based heuristic models (i.e., group method of data handling neural network (GMDHNN), multivariate adaptive regression spline (MARS), and M5 model tree (M5Tree)) for estimating monthly ET0. The outcomes of the newly developed models are compared with empirical formulations including Hargreaves-Samani (HS), calibrated HS, and Stephens-Stewart (SS) models based on mean absolute error (MAE), root mean square error (RMSE), and Nash-Sutcliffe efficiency. Monthly maximum and minimum temperatures (Tmax and Tmin) observed at two stations in Turkey are utilized as inputs for model development. In the applications, three data division scenarios are utilized and the effect of periodicity component (PC) on models’ accuracies are also examined. By importing PC into the model inputs, the RMSE accuracy of GMDHNN, MARS, and M5Tree models increased by 1.4%, 8%, and 6% in one station, respectively. The GMDHNN model with periodic input provides a superior performance to the other alternatives in both stations. The recommended model reduced the average error of MARS, M5Tree, HS, CHS, and SS models with respect to RMSE by 3.7–6.4%, 10.7–3.9%, 76–75%, 10–35%, and 0.8–17% in estimating monthly ET0, respectively. The HS model provides the worst accuracy while the calibrated version significantly improves its accuracy. The GMDHNN, MARS, M5Tree, SS, and CHS models are also compared in estimating monthly mean ET0. The GMDHNN generally gave the best accuracy while the CHS provides considerably over/under-estimations. The study indicated that the only one data splitting scenario may mislead the modeler and for better validation of the heuristic methods, more data splitting scenarios should be applied

DESIGN AND PERFORMANCE OF SELF-CONSOLIDATING AND THIXOTROPIC ULTRA-HIGH-PERFORMANCE CONCRETE FOR INFRASTRUCTURE CONSTRUCTION AND REHABILITATION

The objective of this research is to develop two classes of ultra-high-performance concrete (UHPC), one with self-consolidating consistency and the other with improved thixotropy. Tailoring the rheological properties of low yield stress UHPC can improve dispersion and orientation of steel fibers used in the design of UHPC, hence enhancing the tensile and flexural properties. Similarly, improving the thixotropy of UHPC can enable unique performance for the design of thin bonded bridge deck overlay. The research investigated various thixotropy enhancing admixtures to enhance the structural build-up at rest of UHPC. A total of 16 bonded overlay slab specimens were used to investigate the effect of overlay thickness, fiber volume, and shrinkage of self-consolidating UHPC on the performance of such composite elements. Test results indicated that the key factors influencing the tensile/flexural properties of UHPC due to fiber orientation included the fiber embedment length, fiber number, and fiber-matrix bond strength. Fiber distribution of UHPC was found to depend on the rheological properties of the suspending mortar and casting method. The use of welan gum or diutan gum was more effective to enhance thixotropy compared to other specialty admixtures. Low-shrinkage UHPC led to crack-free overlay even after 30 months of outdoor exposure with temperature varying from -10 to 40°C. Such UHPC overlay slabs exhibited 85% to 135% higher flexural capacity compared to latex-modified concrete overlay slabs. The increase of overlay thickness from 25 to 50 mm led to 30% to 40% enhancement in flexural capacity of UHPC overlay slabs. Such improvement was 15% to 20% when the fiber volume increased from 2% to 3.25% --Abstract, p. i

Niobium in Microalloyed Rail Steels

Rail steels rely primarily on possessing adequate wear and rolling contact fatigue resistance. These properties, together with the toughness, can in principle be optimized by implementing thermomechanical processing assisted by controlled niobium additions. The purpose of the current work is to develop a Nb-microalloying strategy in the context of high-carbon pearlitic and cementite-free bainitic steels. The conventional methods do not leave the critical regions of a rail section in a suitably processed state. An attempt has been made for the first time, to create a pancaked austenite grain structure, with an examination of the consequences on the final product. One of the major difficulties is to ensure that niobium does not segregate during manufacturing, since niobium is a strong carbide former and rail steels traditionally contain large carbon concentrations. Niobium solubility in austenite has been assessed critically and thermodynamic calculations for microsegregation have been taken into account. The aim is to ensure that any primary niobium carbide precipitated from solute-enriched liquid during non-equilibrium solidification, can be taken into solution in austenite during reheating, to mitigate potential effects of coarse precipitates on the final mechanical properties. Rail steels containing 0.01-0.02 wt% Nb have been designed and characterised. In as-cast condition, primary niobium carbides as large as ~10 µm can be observed, which dissolve slowly during reheating. An attempt has been made to develop a model to estimate the dissolution kinetics of the carbides. Dissolved niobium in reheated austenite precipitates during hot deformation as fine niobium carbides (<50 nm) which inhibit austenite recrystallisation by pinning the austenite grain boundaries. Nb-microalloying increases the ‘no-recrystallisation temperature’ of deformed austenite during multi-pass compression tests. The topology of grain deformation has been analysed in terms of stereological calculations and dilatometric experiments have shown that transformation kinetics tend to accelerate when the austenite is deformed below the no-recrystallisation temperature, however the effect is relatively small. The microstructure and mechanical properties of the as-rolled Nb-microalloyed steels have been characterised along with their rolling-sliding wear performance and compared with their non-microalloyed counterparts. Increased austenite grain boundary area and increased dislocation activity due to pancaking, hinder bainite growth which leads to an increased retained austenite volume fraction. This in turn, leads to slightly improved ductility, improved toughness and improved wear resistance in Nb-microalloyed bainitic alloys. Microstructural refinement in Nbmicroalloyed pearlitic alloys does not have any significant effect on tensile and toughness properties, but wear resistance improves significantly. A Bayesian neural network model has been developed to estimate the wear of rails. Predicted trends have been found consistent with metallurgical experience and the perceived noise levels are consistent with reasonable repeatability of the wear testing method used. The model can be applied widely to estimate wear because of its capacity to indicate uncertainty, including both the perceived level of noise in the output, and an uncertainty associated with fitting the function in the local region of input space