Automated track recognition and event reconstruction in nuclear emulsion

The major advantages of nuclear emulsion for detecting charged particles are its submicron position resolution and sensitivity to minimum ionizing particles. These must be balanced, however, against the difficult manual microscope measurement by skilled observers required for the analysis. We have developed an automated system to acquire and analyze the microscope images from emulsion chambers. Each emulsion plate is analyzed independently, allowing coincidence techniques to be used in order to reject background and estimate error rates. The system has been used to analyze a sample of high-multiplicity Pb-Pb interactions (charged particle multiplicities ∼1100) produced by the 158 GeV/c per nucleon 208Pb beam at CERN. Automatically measured events agree with our best manual measurements on 97% of all the tracks. We describe the image analysis and track reconstruction techniques, and discuss the measurement and reconstruction uncertainties

Optimization of oil yield from Hevea brasiliensis seeds through ultrasonic-assisted solvent extraction via response surface methodology

The demand for oil has been increasing vastly over time, and the source of this has slowly been diminishing. The use of non-food feedstock is seen as a promising alternative source for the production of bio-based fuel. In this study, rubber (Hevea brasiliensis) seeds were utilized as biomass in bio-oil production considering that these are non-edible and considered wastes in rubber tree plantations. In the oil extraction process, the rubber seed kernels were oven dried at 100 °C for 24 h, powdered and then dried further at 105 °C for 4 h. After characterization, optimization study was done using Design Expert 7.0 software through central composite design of the response surface methodology. Ultrasonication technology was employed in the oil extraction process which significantly reduced the reaction time needed for extraction to 15 min compared the conventional extraction method of at least 8 h. An optimum rubber seed oil (RSO) yield of 30.3 ± 0.3% was obtained using 15 g biomass, 5:1 n-hexane to biomass (mL gâ1) ratio, 50 μm resonance amplitude and 60 ± 5 °C temperature at 15 min reaction time. The oil yield at optimum condition was found to have 0.89 g mLâ1 density at room temperature, 26.7 cSt kinematic viscosity at 40 °C and high heating value of 39.2 MJ kgâ1. The Fourier Transform Infrared Radiation spectroscopy analysis of the RSO, at optimum condition, showed the presence of carboxylic acid and ester carbonyl functional groups which are good indicators as a potential source of biodiesel. Keywords: Hevea brasiliensis, Oil extraction, Optimization, Response surface methodology, Rubber seed oil, Ultrasonic-assisted solvent extractio

Dynamic Ferrite Formation and Evolution above the Ae₃ Temperature during Plate Rolling Simulation of an API X80 Steel

Thermo-mechanically controlled rolling is a technique used to produce steel strips and plates. One of the steels widely used in the production of heavy plates for application in oil and gas pipelines is API X80. The hot rolling process of this family of steels consists of applying deformation passes at high temperatures, mainly above Ae3, inside the austenite phase field. It has been shown that during deformation, the phenomenon of dynamic transformation (DT) of austenite into ferrite leads to lower hot deformation resistance within the stable austenite region. In this investigation, hot torsion simulations of an industrial rolling process under continuous cooling conditions were used to monitor the formation of ferrite by DT. Stress–strain flow curves and equivalent mean flow stresses followed by sample characterization via optical and electron microscopy showed the inevitable formation of ferrite above the Ae3. The employed 10-pass deformation schedule was divided into 5 roughing and 5 finishing passes, thereby promoting an increased volume fraction of ferrite and decreased critical strain for the onset of DT and dynamic recrystallization (DRX). A microstructural analysis confirmed the formation of ferrite from the first roughing strain until the last finishing pass. The volume fraction of DT ferrite increased due to strain accumulation, an increased number of deformation passes and as the temperature approached the Ae3, leading to a characteristic torsion texture at the end of the simulation

Optimization of Thermomechanical Processing under Double-Pass Hot Compression Tests of a High Nb and N-Bearing Austenitic Stainless-Steel Biomaterial Using Artificial Neural Networks

Physical simulation is a useful tool for examining the events that occur during the multiple stages of thermomechanical processing, since it requires no industrial equipment. Instead, it involves hot deformation testing in the laboratory, similar to industrial-scale processes, such as controlled hot rolling and forging, but under different conditions of friction and heat transfer. Our purpose in this work was to develop an artificial neural network (ANN) to optimize the thermomechanical behavior of stainless-steel biomaterial in a double-pass hot compression test, adapted to the Arrhenius–Avrami constitutive model. The method consists of calculating the static softening fraction (Xs) and mean recrystallized grain size (ds), implementing an ANN based on data obtained from hot compression tests, using a vacuum chamber in a DIL 805A/D quenching dilatometer at temperatures of 1000, 1050, 1100 and 1200 °C, in passes (ε1 = ε2) of 0.15 and 0.30, a strain rate of 1.0 s−1 and time between passes (tp) of 1, 10, 100, 400, 800 and 1000 s. The constitutive analysis and the experimental and ANN-simulated results were in good agreement, indicating that ASTM F-1586 austenitic stainless steel used as a biomaterial undergoes up to Xs = 40% of softening due solely to static recovery (SRV) in less than 1.0 s interval between passes (tp), followed by metadynamic recrystallization (MDRX) at strains greater than 0.30. At T > 1050 °C, the behavior of the softening curves Xs vs. tp showed the formation of plateaus for long times between passes (tp), delaying the softening kinetics and modifying the profile of the curves produced by the moderate stacking fault energy, γsfe = 69 mJ/m2 and the strain-induced interaction between recrystallization and precipitation (Z-phase). Thus, the use of this ANN allows one to optimize the ideal thermomechanical parameters for distribution and refinement of grains with better mechanical properties