scholarly journals Simulation of Laser Heating of Aluminum and Model Validation via Two-Color Pyrometer and Shape Assessment

Materials ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 1506 ◽  
Author(s):  
Fabrizia Caiazzo ◽  
Vittorio Alfieri
Keyword(s):  
Fusion Zone ◽  
Laser Heating ◽  
Thermal Evolution ◽  
Transverse Cross Section ◽  
Average Error ◽  
Real Time Control ◽  
Gaussian Profile ◽  
Aluminum Alloy 2024 ◽  
Actual Response ◽  
Color Pyrometer

The modeling of laser-based processes is increasingly addressed in a competitive environment for two main reasons: Preventing a trial-and-error approach to set the optimum processing conditions and non-destructive real-time control. In this frame, a thermal model for laser heating in the form of non-penetrative bead-on-plate welds of aluminum alloy 2024 is proposed in this paper. A super-Gaussian profile is considered for the transverse optical intensity and a number of laws for temperature-dependent material properties have been included aiming to improve the reliability of the model. The output of the simulation in terms of both thermal evolution of the parent metal and geometry of the fusion zone is validated in comparison with the actual response: namely, a two-color pyrometer is used to infer the thermal history on the exposed surface around the scanning path, whereas the shape and size of the fusion zone are assessed in the transverse cross-section. With an average error of 3% and 4%, the model is capable of predicting the peak temperature and the depth of the fusion zone upon laser heating, respectively. The model is intended to offer a comprehensive description of phenomena in laser heating in preparation for a further model for repairing via additive manufacturing.

scholarly journals Mars’ thermal evolution from machine-learning-based 1D surrogate modelling

2020 ◽  
Author(s):  
Siddhant Agarwal ◽  
Nicola Tosi ◽  
Doris Breuer ◽  
Sebastiano Padovan ◽  
Pan Kessel
Keyword(s):  
Machine Learning ◽  
Rayleigh Number ◽  
Mantle Convection ◽  
Initial Conditions ◽  
Thermal Evolution ◽  
Boussinesq Approximation ◽  
Average Error ◽  
Diffusion Creep ◽  
Test Set ◽  
Low Dimensional

<p>The parameters and initial conditions governing mantle convection in terrestrial planets like Mars are poorly known meaning that one often needs to randomly vary several parameters to test which ones satisfy observational constraints. However, running forward models in 2D or 3D is computationally intensive to the point that it might prohibit a thorough scan of the entire parameter space. We propose using Machine Learning to find a low-dimensional mapping from input parameters to outputs. We use about 10,000 thermal evolution simulations with Mars-like parameters run on a 2D quarter cylindrical grid to train a fully-connected Neural Network (NN). We use the code GAIA (Hüttig et al., 2013) to solve the conservation equations of mantle convection for a fluid with Newtonian rheology and infinite Prandtl number under the Extended Boussinesq Approximation. The viscosity is calculated according to the Arrhenius law of diffusion creep (Hirth & Kohlstedt, 2003). The model also considers the effects of partial melting on the energy balance, including mantle depletion of heat producing-elements (Padovan et., 2017), as well as major phase transitions in the olivine system. </p><p>To generate the dataset, we randomly vary 5 different parameters with respect to each other: thermal Rayleigh number, internal heating Rayleigh number, activation energy, activation volume and a depletion factor for heat-producing elements in the mantle. In order to train in time, we take the simplest possible approach, i.e., we treat time as another variable in our input vector. 80% of the dataset is used to train our NN, 10% is used to test different architectures and to avoid over-fitting, and the remaining 10% is used as test set to evaluate the error of the predictions. For given values of the five parameters, our NN can predict the resulting horizontally-averaged temperature profile at any time in the evolution, spanning 4.5 Ga with an average error under 0.3% on the test set. Tests indicate that with as few as 5% of the training samples (= simulations x time steps), one can achieve a test-error below 0.5%, suggesting that for this setup, one can potentially learn the mapping from fewer simulations. </p><p>Finally, we ran a fourth batch of GAIA simulations and compared them to the output of our NN. In almost all cases, the instantaneous predictions of the 1D temperature profiles from the NN match those of the computationally expensive simulations extremely well, with an error below 0.5%.</p>

Grey-Box Modeling for HCCI Engine Control

2013 ◽  
Author(s):  
M. Bidarvatan ◽  
M. Shahbakhti
Keyword(s):  
Real Time ◽  
Operating Conditions ◽  
Box Model ◽  
Average Error ◽  
Engine Control ◽  
Gas Temperature ◽  
Real Time Control ◽  
Time Model ◽  
Hcci Engine ◽  
Hcci Engines

High fidelity models that balance accuracy and computation load are essential for real-time model-based control of Homogeneous Charge Compression Ignition (HCCI) engines. Grey-box modeling offers an effective technique to obtain desirable HCCI control models. In this paper, a physical HCCI engine model is combined with two feed-forward artificial neural networks models to form a serial architecture grey-box model. The resulting model can predict three major HCCI engine control outputs including combustion phasing, Indicated Mean Effective Pressure (IMEP), and exhaust gas temperature (Texh). The grey-box model is trained and validated with the steady-state and transient experimental data for a large range of HCCI operating conditions. The results indicate the grey-box model significantly improves the predictions from the physical model. For 234 HCCI conditions tested, the grey-box model predicts combustion phasing, IMEP, and Texh with an average error less than 1 crank angle degree, 0.2 bar, and 6 °C respectively. The grey-box model is computationally efficient and it can be used for real-time control application of HCCI engines.

Thermal evolution of W/Si and W/C multilayers under pulsed laser heating and classical annealing

1990 ◽  
Vol 46 (1-4) ◽  
pp. 398-404 ◽  
Author(s):  
V. Dupuis ◽  
M.F. Ravet ◽  
C. Tête ◽  
M. Piecuch
Keyword(s):  
Laser Heating ◽  
Thermal Evolution ◽  
Pulsed Laser ◽  
Pulsed Laser Heating

Gray-Box Modeling for Performance Control of an HCCI Engine With Blended Fuels

2014 ◽  
Vol 136 (10) ◽  
Author(s):  
M. Bidarvatan ◽  
M. Shahbakhti
Keyword(s):  
Real Time ◽  
Operating Conditions ◽  
Box Model ◽  
Average Error ◽  
Gas Temperature ◽  
Real Time Control ◽  
Neural Network Models ◽  
Time Model ◽  
Hcci Engine ◽  
Hcci Engines

High fidelity models that balance accuracy and computation load are essential for real-time model-based control of homogeneous charge compression ignition (HCCI) engines. Gray-box modeling offers an effective technique to obtain desirable HCCI control models. In this paper, a physical HCCI engine model is combined with two feed-forward artificial neural network models to form a serial architecture gray-box model. The resulting model can predict three major HCCI engine control outputs, including combustion phasing, indicated mean effective pressure (IMEP), and exhaust gas temperature (Texh). The gray-box model is trained and validated with the steady-state and transient experimental data for a large range of HCCI operating conditions. The results indicate that the gray-box model significantly improves the predictions from the physical model. For 234 HCCI conditions tested, the gray-box model predicts combustion phasing, IMEP, and Texh with an average error of less than 1 crank angle degree, 0.2 bar, and 6 °C, respectively. The gray-box model is computationally efficient and it can be used for real-time control application of HCCI engines.

scholarly journals Computer simulations of thermal phenomena in surface heating process using the real distribution of Yb:YAG laser power

2018 ◽  
Vol 157 ◽  
pp. 02021
Author(s):  
Marcin Kubiak ◽  
Vladimír Dekýš ◽  
Tomasz Domański ◽  
Pavol Novák ◽  
Zbigniew Saternus
Keyword(s):  
Temperature Field ◽  
Fusion Zone ◽  
Computer Simulations ◽  
Cross Sections ◽  
Power Distribution ◽  
Laser Heating ◽  
Laser Power ◽  
Numerical Algorithms ◽  
Heating Process ◽  
Thermal Phenomena

This work concerns mathematical and numerical modelling of temperature field during Yb:YAG laser heating of sheets made of S355 steel with the motion of liquid steel in the fusion zone taken into account. Laser power distribution and the caustics are determined on the basis of the geostatistical kriging method. Temperature field and melted material velocity field in the fusion zone are obtained from the numerical solution of continuum mechanics equations using projection method and finite volume method. Numerical algorithms are implemented into computer solver using ObjectPascal programming language. Computer simulations of Yb:YAG laser heating process are performed for different process parameters. Characteristic zones of experimentally obtained cross sections of heated elements are compared to numerically predicted fusion zone and heat affected zone.

Interfacial structures of dissimilar materials joined by capacitor-discharge welding

1994 ◽  
Vol 52 ◽  
pp. 534-535
Author(s):  
C. P. Doğan ◽  
R. D. Wilson ◽  
J. A. Hawk
Keyword(s):  
Rapid Solidification ◽  
Fusion Zone ◽  
Dissimilar Materials ◽  
Solidification Process ◽  
Weld Interface ◽  
Capacitor Discharge ◽  
Fine Grained ◽  
Iron Aluminum ◽  
Conductive Ceramics ◽  
Capacitor Discharge Welding

Capacitor Discharge Welding is a rapid solidification technique for joining conductive materials that results in a narrow fusion zone and almost no heat affected zone. As a result, the microstructures and properties of the bulk materials are essentially continuous across the weld interface. During the joining process, one of the materials to be joined acts as the anode and the other acts as the cathode. The anode and cathode are brought together with a concomitant discharge of a capacitor bank, creating an arc which melts the materials at the joining surfaces and welds them together (Fig. 1). As the electrodes impact, the arc is extinguished, and the molten interface cools at rates that can exceed 106 K/s. This process results in reduced porosity in the fusion zone, a fine-grained weldment, and a reduced tendency for hot cracking.At the U.S. Bureau of Mines, we are currently examining the possibilities of using capacitor discharge welding to join dissimilar metals, metals to intermetallics, and metals to conductive ceramics. In this particular study, we will examine the microstructural characteristics of iron-aluminum welds in detail, focussing our attention primarily on interfaces produced during the rapid solidification process.

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

A Real Time Control Architecture for Continuously Managing Patients in a Care Unit

1995 ◽  
Vol 34 (05) ◽  
pp. 475-488
Author(s):  
B. Seroussi ◽  
J. F. Boisvieux ◽  
V. Morice
Keyword(s):  
Real Time ◽  
Linear Time ◽  
Treatment Plan ◽  
Control Architecture ◽  
Real Time Control ◽  
Time Representation ◽  
Time Control ◽  
Continuous Support ◽  
Definition Of ◽  
Computerized System

Abstract:The monitoring and treatment of patients in a care unit is a complex task in which even the most experienced clinicians can make errors. A hemato-oncology department in which patients undergo chemotherapy asked for a computerized system able to provide intelligent and continuous support in this task. One issue in building such a system is the definition of a control architecture able to manage, in real time, a treatment plan containing prescriptions and protocols in which temporal constraints are expressed in various ways, that is, which supervises the treatment, including controlling the timely execution of prescriptions and suggesting modifications to the plan according to the patient’s evolving condition. The system to solve these issues, called SEPIA, has to manage the dynamic, processes involved in patient care. Its role is to generate, in real time, commands for the patient’s care (execution of tests, administration of drugs) from a plan, and to monitor the patient’s state so that it may propose actions updating the plan. The necessity of an explicit time representation is shown. We propose using a linear time structure towards the past, with precise and absolute dates, open towards the future, and with imprecise and relative dates. Temporal relative scales are introduced to facilitate knowledge representation and access.

Sign in / Sign up

Export Citation Format

Share Document