Thermal Machine Research Articles

Drawing fracture behavior and mechanism analysis of ER50-6G welding wire steel

In this paper, we investigated the drawing fracture behavior of ER50-6G welding wire steel and analyzed the factors affecting the fracture, such as continuous casting slag and inclusion, billet structure segregation, Ti inclusion, surface defect inheritance, metallographic structure type, fracture morphology, and so on. OLYMPUS GX53 optical microscope was used to observe and analyze the macrostructure of the cross-section and longitudinal section of the continuous casting billet and the original quality state of the wire rod surface. The continuous cooling transformation curve (CCT curve) of the steel was measured using a DIL805A thermal dilatometer and a Gleeble-3500 thermal simulation test machine. The microstructures of the non-fracture area and drawing fracture of wire rod samples were observed through a ZEISS-ULTRA scanning electron microscope. The results show that the austenite starting transformation temperature (Ac1) and the austenite finishing transformation temperature (Ac3) of ER50-6G welding wire steel are 732 °C and 865 °C, respectively. Under the actual cooling conditions of online production, the strip structure is composed of ferrite (F), pearlite (P), and a small amount of bainite (B). There is a small amount of slag and inclusion in the continuous casting billet; in addition, Al2O3 inclusions and TiC (or Ti(C,N)) inclusions can be observed in the wire rod. The scratch depth of the wire rod surface is 24∼62 μm and the width is 31∼50 μm. With the increase in drawing cumulative deformation, the Al2O3 and TiC (or Ti(C,N)) inclusions that are not easy to deform lead to drawing fracture and brittle fracture. In addition, a small amount of bainite in the wire rod microstructure causes the fracture of the wire with a fine diameter, whereas, the limited-depth scratches on the steel surface do not have an obvious effect on the drawing fracture.

Control Strategy Based on Artificial Intelligence for a Double-Stage Absorption Heat Transformer

Thermal energy recovery systems have different candidates to mitigate CO2 emissions as recommended by the UN in its list of SDGs. One of these promising systems is thermal absorption transformers, which generally use lithium-water bromide as the working fluid. A Double Stage Heat Transformer (DSHT) is a thermal machine that allows the recovery of thermal energy at a higher temperature than it is supplied through the effect of steam absorption in a concentrated solution of lithium bromide. There are very precise thermodynamic models which allow us to calculate all the possible operating conditions of the DSHT. To perform the control of these systems, the use of Artificial Intelligence (AI) is proposed with two computational techniques—Fuzzy Logic (FL) and Artificial Neural Network (ANN)—to calculate in real-time the set of variables that maximize the product’s Gross Temperature Lift (GTL) and Coefficient of Performance (COP) in a DSHT. The values for Coefficient of Determination (R2), Mean Square Error Root (MRSE), and Mean Error Bias (MBE) for the two types of computational techniques were analyzed and compared with the purpose of identifying which of them may be more accurate to calculate the operating conditions (temperatures, pressures, concentration and flows) with the highest COP for an interval of the value of the temperature absorption entered by the user. The result of the analysis of the evaluated techniques concluded that the control strategy of a DSHT in real-time will be based on the precise calculation of the refrigerant flow in the second evaporator with a Neural Network of 30 neurons, 300 weights and 40 bias, as it is more accurate than the Fuzzy Logic technique. The goodness-of-fit for two computational techniques was evaluated as having an R2 higher than 0.98 for the provided data. Future AI controllers must be based on evaporator flow values with evaporator power at 3.9−04 kg/KJ.

Fluctuations and stability of a fast-driven Otto cycle

We investigate the stochastic dynamics of a thermal machine realized by a fast-driven Otto cycle. By employing a stochastic approach, we find that system coherences strongly affect fluctuations depending on the thermodynamic current. Specifically, we observe an increment in the system instabilities when considering the heat exchanged with the cold bath. On the contrary, the cycle precision improves when the system couples with the hot bath, where thermodynamic fluctuations reduce below the classical Thermodynamic Uncertainty Relation bound. Violation of the classical bound holds even when a dephasing source couples with the system. We also find that coherence suppression not only restores the cycle cooling but also enhances the convergence of fluctuation relations by increasing the entropy production of the reversed process. An additional analysis unveiled that the stochastic sampling required to ensure good statistics increases for the cooling cycle while downsizes for the other protocols. Despite the simplicity of our model, our results provide further insight into thermodynamic relations at the stochastic level.

Microstructure Evolution and Mechanical Response of a Direct Quenched and Partitioned Steel at Different Finishing Rolling Temperatures.

The effects of finishing rolling temperature on the microstructure and mechanical properties of a direct quenched and partitioned (DQ&P) steel were investigated by a thermal simulation machine, a field emission scanning electron microscope (FE-SEM), electron backscattering diffraction (EBSD), and a transmission electron microscope (TEM). The results show that the original austenite grain size was refined by 31% as the finishing rolling temperature decreased from 920 °C to 840 °C, leading to the formation of the finest martensite lath at 840 °C. At the same time, the lower finishing rolling temperature resulted in a higher dislocation density, and consequently improved the stability of the retained austenite. Moreover, compared to the conventional Q&P process, the comprehensive mechanical properties of a steel with similar chemical composition can be enhanced by DQ&P processing. With the decrease of finishing rolling temperature from 920 °C to 840 °C, the strength and total elongation increases. The yield strength, tensile strength, and total elongation reach the maximum values of 1121 MPa, 1134 MPa, and 11.7%, respectively, at 840 °C.

The use of mobile thermal imaging and machine learning technology for the detection of early surgical site infections

BackgroundSurgical Site Infections (SSI) yield subtle, early signs that are not readily identifiable. This study sought to develop a machine learning algorithm that could identify early SSIs based on thermal images. MethodsImages were taken of surgical incisions on 193 patients who underwent a variety of surgical procedures. Two neural network models were generated to detect SSIs, one using RGB images, and one incorporating thermal images. Accuracy and Jaccard Index were the primary metrics by which models were evaluated. ResultsOnly 5 patients in our cohort developed SSIs (2.8%). Models were instead generated to demarcate the wound site. The models had 89–92% accuracy in predicting pixel class. The Jaccard indices for the RGB and RGB ​+ ​Thermal models were 66% and 64%, respectively. ConclusionsAlthough the low infection rate precluded the ability of our models to identify surgical site infections, we were able to generate two models to successfully segment wounds. This proof-of-concept study demonstrates that computer vision has the potential to support future surgical applications.

Arrhenius constitutive model and dynamic recrystallization behavior of 18CrNiMo7-6 steel

In order to analyze the thermal deformation and dynamic recrystallization behavior of 18CrNiMo7-6 steel, the isothermal compression tests are carried out on Gleeble-3500 thermal simulator machine at deformation temperatures 973.15 K–1273.15 K with strain rates 0.001 s−1–1 s−1. Based on the true stress σ0.3, the Arrhenius constitutive models are constructed by using characterization parameters and physical base parameters, and the determination coefficients between the predicted values of the two models and the measured values are 0.98378 and 0.97613, respectively. The constitutive models of characterization parameters and physical base parameters considering strain effect are also constructed, and a comparative analysis is made. The results show that the predicted values of the characterization parameters constitutive model is closer to the measured values in the low strain region. After reaching the peak strain, the predicted values of the two constitutive models are close to the measured values, and both have high prediction accuracy. The dynamic recrystallization models are established to characterize the recrystallization behavior. The recrystallization activation energy of 18CrNiMo7-6 steel is 169.6793 kJ mol−1, the ratio of critical strain to peak strain is about 0.47074, and the ratio of critical stress to peak stress is about 0.94059. The activation energy of 50% dynamic recrystallization is 29.9844 kJ mol−1. The dynamic recrystallized grain size increases with increasing deformation temperature or decreasing strain rate, and the activation energy of grain growth is −49.9237 kJ mol−1.

Inspection of Enamel Removal Using Infrared Thermal Imaging and Machine Learning Techniques.

Within aerospace and automotive manufacturing, the majority of quality assurance is through inspection or tests at various steps during manufacturing and assembly. Such tests do not tend to capture or make use of process data for in-process inspection and certification at the point of manufacture. Inspection of the product during manufacturing can potentially detect defects, thus allowing consistent product quality and reducing scrappage. However, a review of the literature has revealed a lack of any significant research in the area of inspection during the manufacturing of terminations. This work utilises infrared thermal imaging and machine learning techniques for inspection of the enamel removal process on Litz wire, typically used for aerospace and automotive applications. Infrared thermal imaging was utilised to inspect bundles of Litz wire containing those with and without enamel. The temperature profiles of the wires with or without enamel were recorded and then machine learning techniques were utilised for automated inspection of enamel removal. The feasibility of various classifier models for identifying the remaining enamel on a set of enamelled copper wires was evaluated. A comparison of the performance of classifier models in terms of classification accuracy is presented. The best model for enamel classification accuracy was the Gaussian Mixture Model with expectation maximisation; it achieved a training accuracy of 85% and enamel classification accuracy of 100% with the fastest evaluation time of 1.05 s. The support vector classification model achieved both the training and enamel classification accuracy of more than 82%; however, it suffered the drawback of a higher evaluation time of 134 s.

Influence of Deformation Rate on Nucleation and Growth of Recrystallized Grains during Hot Deformation of Nickel-Based Powder Superalloys

High temperature uniaxial compression deformation test was carried out on nickel-based powder superalloy FGH96 alloy by thermal processing simulation test machine, and the relationship between grain boundary characteristics and nucleation mechanism of recrystallized grains, growth characteristics of recrystallized cores and related dynamic softening mechanisms was studied. According to the correlation and evolution law of grains, combined with the change trend of grain orientation during the deformation process, the recrystallization behavior characteristics of the grains with special orientation generated during the deformation process were analyzed. When the deformation temperature is 1070 °C, the evolution rules of the substructure grain boundaries with an orientation difference of less than 10° are different under different deformation rate conditions, which indicates that the dynamic softening mechanism of FGH96 alloy is slightly different with the increase of deformation rate. There are different. With the increase of the deformation rate, the nucleation mechanism induced by Σ3 twinning gradually replaced the nucleation mechanism of continuous subgrain rotation and coarsening, and the grain growth rate gradually slowed down.

Grain Size Evolution of Nickel-Based Superalloys after Hot Deformation

In this work, the uniaxial compression deformation test of nickel-based powder superalloys was carried out in a vacuum isothermal adiabatic environment using the Thermocmaster-Z thermal processing simulation test machine. The evolution of the microstructure in the two stages of dynamic softening and strain hardening in the true stress-true strain curve in the process, including the interaction of grain size, local misorientation and their relative mobility. During most of the plastic deformation time, the flow stress is gradually increased, that is, the strain hardening dominates, while in the remaining plastic deformation process parameters, dynamic softening dominates. Under different plastic deformation process parameters, The average grain size fluctuates to a certain extent under the condition of partial strain values, but the final evolution law is gradually decreasing, while the final trend of the average local misorientation is increasing.

Hyperaccurate thermoelectric currents

Thermodynamic currents, such as energy, heat, and entropy production, can fluctuate significantly at the nanoscale. However, some fluctuate less than others. Hyperaccurate currents are defined as those which fluctuate the least, in the sense that they maximize the signal-to-noise ratio (precision). In this Letter we analytically determine what are the hyperaccurate currents in quantum thermoelectrics, modeled by coherent transport in the Landauer-B\"uttiker formalism. Our results yield a tight and general bound on precision, which replace the classical thermodynamic uncertainty relations, that can be violated in quantum thermoelectrics. They also allow us to address the question of how close to hyperaccurate is a given current. We illustrate our findings for smooth boxcar functions, and for a double quantum dot operating as a thermal machine. In the latter, we use our results to establish the parameter ranges for which the output power of an autonomous engine can become hyperaccurate arbitrarily far from equilibrium.

Influence of Thermal and Mechanical Load Cycling on Fracture Resistance of Premolars Filled with Calcium Silicate Sealer

The aim of this study was to evaluate the aging effect of thermomechanical cyclic load on fracture resistance of lower premolars obturated with AH Plus and BioRoot RCS root canal sealers. Forty-eight single-rooted premolars teeth were instrumented with REVO-S files up to SU/0.06 taper. The teeth were randomly assigned into 2 main groups (n = 24) according to the selected two root canal sealers (AH Plus or BioRoot RCS). All teeth were obturated using matching gutta-percha. Each main group were further divided into 3 subgroups (A, B and C) (n = 8). Group A acted as the negative control group (non-Thermomechanical aging). Whereas Group B and C were subjected first to thermal variations in a thermal cycling machine (7500 and 15,000 thermal cycles), then two different dynamic loading periods namely 3 × 105 and 6 × 105 in a masticatory simulator with a nominal load of 5 kg at 1.2 Hz which simulate approximately 1 ½ and 3 years of clinical function respectively. The roots were decoronated and fracture resistance were measured using a universal testing machine. After thermal-mechanical aging, BioRoot RCS showed significantly higher fracture resistance (p < 0.05) than AH Plus. As the thermal-mechanical cycles increased both AH Plus and BioRoot RCS exhibited a significant decrease in fracture resistance (p < 0.05). It could be concluded that thermomechanical aging had a significant impact on the outcome of the fracture resistance of AH Plus and BioRoot RCS.

Simulation on High Strain Rate Thermal Deformation Constitutive Equation of AZ91 Magnesium Alloy

The hot compression simulation experiment of AZ91 magnesium alloy was carried out by a Gleeble-1500D thermal simulation machine under the conditions of T within the range of 300~400°C, within the range of 10-2~101 s−1, and a compression capacity being 70%. In order to establish the constitutive equation more accurately, this paper uses the piecewise function model to match the σ-ε curve as the necessity of making the simulated curve have a good match with the true stress-strain curve. The linear function and nonlinear function are used to match the σ-ε curve when the material reaches the stage of softening. The fitting results show that the piecewise function matches the true stress-strain curve well under different experimental circumstances of T and even at a high strain rate. This indicates that the piecewise function has a better capacity in the real situation of the relationship between hot-working deformation and the stress-strain curve of magnesium alloys.

Thermal Degradation Studies and Machine Learning Modelling of Nano-Enhanced Sugar Alcohol-Based Phase Change Materials for Medium Temperature Applications

Thermogravimetric analysis (TGA) was utilised to compare the thermal stability of pure phase change material (D-mannitol) to that of nano-enhanced PCM (NEPCM) (i.e., PCM containing 0.5% and 1% multiwalled carbon nanotubes (MWCNT)). Using model-free kinetics techniques, the kinetics of pure PCM and NEPCM degradation were analysed. Three different kinetic models such as Kissinger-Akahira-Sunose (KAS), the Flynn-Wall-Ozawa (FWO), and the Starink were applied to assess the activation energies of the pure and nano-enhanced PCM samples. Activation energies for pure PCM using the Ozawa, KAS, and Starink methods ranged from 71.10–77.77, 79.36–66.87, and 66.53–72.52 kJ/mol, respectively. NEPCM’s (1% MWCNT) activation energies ranged from 76.59–59.11, 71.52–52.28, and 72.15–53.07 kJ/mol. Models of machine learning were utilised to predict the degradation of NEPCM samples; these included linear regression, support vector regression, random forests, gaussian process regression, and artificial neural network models. The mass loss of the sample functioned as the output parameter, while the addition of nanoparticles weight fraction, the heating rate, and the temperature functioned as the input parameters. Experiment-based TGA data can be accurately predicted using the created machine learning models.

Hybrid quantum thermal machines with dynamical couplings

Quantum thermal machines can perform useful tasks, such as delivering power, cooling, or heating. In this work, we consider hybrid thermal machines, that can execute more than one task simultaneously. We characterize and find optimal working conditions for a three-terminal quantum thermal machine, where the working medium is a quantum harmonic oscillator, coupled to three heat baths, with two of the couplings driven periodically in time. We show that it is possible to operate the thermal machine efficiently, in both pure and hybrid modes, and to switch between different operational modes simply by changing the driving frequency. Moreover, the proposed setup can also be used as a high-performance transistor, in terms of output-to-input signal and differential gain. Owing to its versatility and tunability, our model may be of interest for engineering thermodynamic tasks and for thermal management in quantum technologies.

Hot tensile deformation behavior and microstructure evolution of 7075 aluminum alloy sheet

The hot tensile deformation behavior of 7075 aluminum alloy sheet was studied using Gleeble-3800 thermal simulation machine at deformation temperatures of 400–475 °C and strain rates of 0.001∼1s−1. Microstructure evolution and fracture morphology were observed by optical microscopy (OM), scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The hyperbolic sine model is used to establish the constitutive equation of the relationship between peak stress and Z parameter. The results show that the hyperbolic sine Arrhenius constitutive equation can well characterize the flow behavior of the studied alloy, and the hot deformation activation energy is 132.52 kJ/mol. The average relative error between the predicted and experimental values is only 4.58%, and r2 is as high as 0.988, indicating that there is good consistency between the experimental value and the predicted value. When the strain rate keeps unchanged, the elongation of the studied alloy shows an upward trend in the lower deformation temperature range (400–425 °C). When the deformation temperature continued to rise to 475 °C, the elongation of the studied alloy decreased sharply. Based on the analysis of fracture morphology, a large number of dimples can be observed at 450 °C/0.001s−1, which is a typical ductile fracture. With increasing of deformation temperatures, the fracture mechanism gradually changes from ductile fracture to brittle fracture. Under the condition of 425 °C/0.01s−1, there are a lot of dislocations in the alloy, and the dislocation walls are formed. With the increase of deformation temperatures or the decrease of strain rates, the dislocation density gradually decreases and dynamic recrystallization occurs. The softening mechanism of the studied alloy changes from dynamic recovery to dynamic recrystallization.

Detection of Mthfr (C667t) and Mthfd (G1958a) Polymorphisms Among Sudanese Women with The Recurrent Miscarriages

Background Recurrent pregnancy loss affects 1 to 5% of women trying to conceive, I t has a significant impact at individual and social level. Methodology case-control study conducted at the research laboratory of the national center of neurological sciences, Khartoum, Sudan. All patients attending Ibrahim Malik teaching hospital and diagnosed with recurrent spontaneous abortion were included as case and healthy women at reproductive age were included as controls. The DNA was isolated by standard phenol chloroform extraction method, The PCR was done by using commercial thermal cycler machine with specific protocol. PCR products were sent for sequencing to Macro gene Europe Laboratory. Results 494 bp of MTHFR(C677T) and 174 bp of MTHFD (rs2236225) (G19581A) alleles were detected after PCR. The PCR result of MTHFR(C677T) shows; 40 (90%) forMTHFR (C677T) allele was positive in the cases and only12 (24 %) were positive for the control group. For MTHFD (G19581A) allele 42 (95%) were positive in the case group and 14 (28%) were positive in the control. When compared between case and control there was highly significant differences. The sequencing results for MTHFR C677; single Base Exchange was found C>T in the case groups, when used the mutation taster software the mutation was predicted. For MTHFD (G1958A) in the case group tow single Base Exchange were found T>A and G>A, the mutation taster software was predicted T>A mutation. Conclusion According to this result the MTHF polymorphisms might become one of the main causes of unexplained diagnosis women with recurrent spontaneous abortion

Thermal Deformation Behavior and Microstructure Evolution of GH4151 Alloy

Herein, an as‐cast GH4151 alloy is subjected to thermal compression at different temperatures and strain rates by a thermal simulation testing machine. The constitutive equation, dynamic recrystallization (DRX) model, and hot processing map are obtained from the stress–strain curves. The results show that the optimal heat processing conditions are 1100–1120 °C, 0.001–0.01 s−1 and 1100–1135 °C, 0.001–0.1 s−1. Electron backscatter diffraction (EBSD) and scanning electron microscopy (SEM) show that the recrystallization mechanism of GH4151 alloy is mainly discontinuous DRX (DDRX) and is supplemented by continuous DRX (CDRX). Deformation dislocations are eliminated by the phagocytosis mechanism of dislocations during recrystallization, and the subgrains and low‐angle grain boundaries are transformed into high‐angle grain boundaries, which finally form equiaxed grains. In addition, due to the pinning effect of γ′ phase on grain boundaries, the redissolution of γ′ phase during DRX greatly affects grain growth.

Prediction of variable-groove weld penetration using texture features of infrared thermal images and machine learning methods

When workpieces with varying grooves are welded, changes in the groove angle of the base metal can affect the butt weld penetration, thus affecting the final weld forming quality. Therefore, it is particularly important to be able to diagnose variable-groove weld penetration in real time. In this study, a novel variable-groove weld-penetration diagnosis technique using the texture features of infrared thermal images and machine learning methods is proposed. First, an improved local binary pattern algorithm based on the window standard deviation is introduced for texture analysis of welding-temperature field images. Second, the feasibility of using the images' texture features for predicting variable-groove weld penetration is demonstrated. Finally, a predictive machine-learning-based model for predicting variable-groove weld penetration is developed, and the effects of the size and location of the monitored area on the model's performance are quantified. The experimental results suggest that the proposed predictive model is feasible and offers a new and effective method for real-time diagnosis of variable-groove weld penetration. The research results of this paper enrich the application of optical inspection in the field of welding processing.

Hot compressive deformation and microstructural evolution of 60NiTi alloy

Hot deformation behavior and microstructure of 60NiTi alloy during hot compressive deformation were investigated using Gleeble−3500 thermal simulation test machine in the temperature range of 500−650 °C and strain rate range of 0.001−1 s−1. The constitutive equation of hot compression was constructed and the hot processing maps were plotted. When the compression temperature rose or the strain rate declined, the peak stress reduced. The hot deformation activation energy was calculated as 327.89 kJ/mol, and the proper hot working parameters of compression temperature of 600−650 °C and strain rate of 0.005−0.05 s−1 were obtained. The recrystallization degree increased with increasing compression temperature. When the strain rate increased, both dislocation density and number of twins were improved, and the Ni3Ti phase tended to aggregate. Ni3Ti precipitation phase was beneficial to inducing dynamic recrystallization of the 60NiTi alloy. The dynamic recovery, dynamic recrystallization and twinning took place during hot compressive deformation of the 60NiTi alloy.

Modeling the thermal machine tool error during cooling lubricant usage

The thermo-elastic machine tool error, which is one of the main error sources in a machine tool, can be modeled and corrected using the machine control. In the past, the models to do so have been parametrized using precise measurement equipment like lasertracers. However, these lasertracers cannot be used to measure the thermo-elastic error during cooling lubricant usage, which is necessary for most industrial applications. Therefore, this paper presents a method on how to measure the thermo-elastic machine behavior using Integral Deformation Sensors to compute the tool center point displacement during coolant usage based on a white box modeling approach. These computed displacements are then used to parametrize a correction model that uses low order delays elements.