37 research outputs found
Studio delle trasformazioni dell’austenite negli acciai multifase innovativi
No full textLa famiglia degli acciai multifase include gli acciai Dual-Phase (DP), TRIP, a fasi complesse (CP) e martensitici (MS). Le proprietà di questi materiali sono date principalmente dalla combinazione delle componenti microstrutturali con diversi gradi di durezza. Le strutture degli acciai multifase sono ottenute attraverso una specifica combinazione dei parametri di laminazione e della strategia di raffreddamento. Le trasformazioni dell’ austenite negli acciai multifase sono uniche. La combinazione di alta resistenza e buona formabilità è data dal particolare ciclo termico che prevede il raffreddamento da zona intercritica (da una temperatura compresa tra A1 e A3): l’ austenite ha quindi un tenore di carbonio diverso dall’ acciaio di base che è funzione della temperatura intercritica, inoltre la ferrite non viene generata da una fase di nucleazione e accrescimento, ma solo dalla fase di accrescimento. La predizione delle curve CCT per questi acciai riveste particolare importanza in quanto prevedendo la microstruttura finale si riesce a predirne le proprietà meccaniche
Strain Hardening Behaviour Prediction Model For Automotive High Strength Multiphase Steels
No full textA neural networks-based model relating properties of the as cast-semi and rolling parameters with rolled product properties for plate rolled pipeline steels
No full textSegregation is an important phenomenon which heavily affects the final mechanical properties of steel products. The presence of several complex physical phenomena resulting in final segregation pattern in as-cast products makes the quantitative prediction of macro-segregation for industrially relevant casting processes extremely difficult. In the present work, a reliable prediction of important rolled product quality (in terms of mechanical and Charpy impact properties) which are linked to segregation is achieved for plate rolled pipeline steels by exploiting data related to the as-cast structure and caster operational data (including casting machine condition) through the application of neural networks. In particular, a hierarchical approach is proposed for the prediction of the Charpy Impact Value, in order to reflect the physical link between this quantity and the Ultimate Tensile Strength. The neural predictor has been developed by exploiting real industrial data and its performance can improve through time by enlarging the database that is used for its training
Diagnosis of the instability of the cooling behaviour of flat steel products through parametric characterisation, neural networks and statistics
No full textThis paper presents a mathematical model developed by means of an analytical function whose shape depends on the values of a few parameters for the run-out table cooling which is used in hot strip mills. The system relies on a first-order differential equation for describing the temperature loss along the run-out table. Neural networks have been applied in order to find correlations between the model parameters and the steel and process variables. Then, traditional statistical techniques have been applied in order to evaluate the stability of the cooling behaviour. Numerical results obtained on an industrial database are presented and discussed
A Finite Element Method for the Prediction of Thermal, Metallurgical, and Mechanical Behaviour of Rebars in the TempCore Process
No full textImproving the estimation of mean flow stress within hot rolling of steel by means of different artificial intelligence techniques
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Prediction of Hot-Deformation Resistance during Processing of Microalloyed Steels in Plate Rolling Process
No full textMean Flow Stress (MFS) of micro-alloyed high strength steels during plate rolling has been thorough studied. It has been found out by both thermomechanical tests and measurements taken in the industrial plate mill. For this purpose, log data obtained from the plate rolling mills have been converted to mean flow stress using the Sims approach. The agreement between thermomechanical tests and mill data have been tested in order to confirm that thermomechanical testing can provide an easy, convenient and very effective simulation of industrial hot rolling process.
The results are analyzed and compared to the predictions of some mathematical models developed in literature. Subsequently, the best performing formula, namely the Poliak's equation, has been optimized by means of Genetic Algorithms and the standard Gauss-Newton method. This latter has allowed a finer tuning of the models parameters in order to fit at best the available data. The Poliak's formula optimized by Genetic Algorithms is shown to accurately predict the mean flow stress and therefore it provides a useful tool for the determination of the milling settings before the incoming strip enters the mill
Prediction of continuous cooling transformation diagrams for dual-phase steels from the intercritical region
No full textThe purpose of the present work is the implementation and validation of a model able to predict the microstructure changes and the mechanical properties in the modern high-strength dualphase steels after the continuous annealing process line (CAPL) and galvanizing (Galv) process. Experimental continuous cooling transformation (CCT) diagrams for 13 differently alloying
dual-phase steels were measured by dilatometry from the intercritical range and were used to tune the parameters of the microstructural prediction module of the model. Mechanical properties and microstructural features were measured for more than 400 dual-phase steels simulating the CAPL and Galv industrial process, and the results were used to construct the
mechanical model that predicts mechanical properties from microstructural features, chemistry, and process parameters. The model was validated and proved its efficiency in reproducing the
transformation kinetic and mechanical properties of dual-phase steels produced by typical industrial process. Although it is limited to the dual-phase grades and chemical compositions explored, this model will constitute a useful tool for the steel industry
Role of somatomedin-B-like domains on ENPP1 inhibition of insulin signaling
No full textThe exact mechanism by which ectonucleotide pyrophosphatase phosphodiesterase 1 (ENPP1) inhibits insulin signaling is not known. ENPP1 contains two somatomedin-B-like domains (i.e. SMB 1 and 2) involved in ENPP1 dimerization in animal cells. The aim of the present study was to investigate if these domains modulate ENPP1 inhibitory activity on insulin signaling in human insulin target cells (HepG2). ENPP1 (ENPP1-3' myc), ENPP1 deleted of SMB 1 (ENPP1-Delta I-3'myc) or of SMB 2 (ENPP1-Delta II-3'myc) domain were cloned in frame with myc tag in mammalian expression vector pRK5. Plasmids were transiently transfected in human liver HepG2 cells. ENPP1 inhibitory activity on insulin signaling, dimerization and protein-protein interaction with insulin receptor (IR), reported to mediate the modulation of ENPP1 inhibitory activity, were studied. As compared to untransfected cells, a progressive increase of ENPP1 inhibitory activity on insulin-induced IR beta-subunit autophosphorylation and on Akt-S-473 phosphorylation was observed in ENPP1-3' myc, ENPP1-Delta I-3'myc and ENPP1-Delta II-3'myc cells. Under non reducing conditions a 260 kDa homodimer, indicating ENPP1 dimerization, was observed. The ratio of non reduced (260 kDa) to reduced (130 kDa) ENPP1 was significantly decreased by two thirds in ENPP1-Delta II-3'myc vs. ENPP1-3'myc but not in ENPP1-Delta I-3'myc. A similar ENPP1/IR interaction was detectable by co-immunoprecipitation in ENPP1-3'myc, ENPP1-Delta I-3'myc and ENPP1-Delta II-3'myc cells. In conclusion, SMB 1 and SMB 2 are negative modulators of ENPP1 inhibitory activity on insulin signaling. For SMB 2 such effect might be mediated by a positive role on protein dimerization. (C) 2012 Elsevier B.V. All rights reserved
