Laser powder bed fusion (LPBF)) has great application prospects in aerospace and other fields, but low process stability and difficult quality assurance in the process of Laser Powder Bed Fusion are the key problems t...
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Laser powder bed fusion (LPBF)) has great application prospects in aerospace and other fields, but low process stability and difficult quality assurance in the process of Laser Powder Bed Fusion are the key problems that restrict its extensive development at present. One of the important ways to solve this problem is to realize online monitoring of molten pool and closed-loop quality control. In this paper, the photodiode sensor is used to collect the radiation signal of the molten pool in real time, and the characteristics of the radiation intensity signal of the molten pool in time domain and frequency domain are extracted. The most appropriate modeling features are selected by comparing and analyzing different features, and XGBoost classical integration algorithm is used to model, and the relationship between the radiation intensity signal of the molten pool and process parameters is established, which provides a new method for abnormal process identification and quality control.
The model-based safety analysis (MBSA) method is used to analyze the safety of a digital control system given the difficulty in using the traditional safety analysis method for aeroengine digital control system. Based...
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As the application of machine vision system in industry becomes more and more widespread, product information is more and more presented in the form of image data. process capability analysis of image data has become ...
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The article discusses an approach that combines expert systems and dynamic models in an intellectualized operator information support system (IOISS) for nuclear power plant units. The main goal of this approach is to ...
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This paper documents the workflow and supporting technologies that a large system dynamics model, the biomass scenario model, employs to streamline the data preparation, simulation, quality control, and analysis proce...
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This paper documents the workflow and supporting technologies that a large system dynamics model, the biomass scenario model, employs to streamline the data preparation, simulation, quality control, and analysisprocess at the National Renewable Energy Laboratory. The workflow centers on automation of routine aspects of the flow of data between data stores, simulations, and visualizations. It enforces quality checks on data, reproducibility of computations, and traceability of results, while maintaining complete archives of modeling and analysis artifacts. The resulting frictionless simulation/analysis environment supports large-scale sensitivity analysis, interactive creation of ensembles of simulations, and rapid visualization-based exploration of simulation results.
Achieving optimization and control of industrial processes relies on accurate models. This paper proposes a combined data model for modeling complex industrial processes, utilizing a graph convolutional network and an...
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In the production process of the process industry, precise adjustment of working conditions presents a challenge due to the complexity of processes and unknown disturbances. Central control operators need to adjust se...
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modeling the combustion characteristics in a multi-fuel compression ignition engine, under varying operating conditions is a challenging problem. Physics-based models can be developed but tend to be quite extensive or...
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ISBN:
(纸本)9798350382662;9798350382655
modeling the combustion characteristics in a multi-fuel compression ignition engine, under varying operating conditions is a challenging problem. Physics-based models can be developed but tend to be quite extensive or limited to certain operating conditions. To achieve reliable combustion under these conditions, the number of actuators required can be high, further increasing the complexity of the model and in turn the difficulty of control design based on it. To simplify the control, feedforward (FF) control is developed by inverting steady state models, built based on data collected at various operating points. Use of data driven models for capturing these steady state characteristics has gained a lot of attraction in the recent years due to the available computational resources and ease of model development. For developing the FF control, data-driven models are inverted by numerically searching for desired control inputs. The time taken for this inversion grows with the complexity of the model and the complexity of the model increases with the number of operating conditions and actuators. In this paper, use of scalable Gaussian process (GP) methods for building computationally efficient models to reduce the time taken for generating FF maps is proposed. The performance of these models and control design is validated using computational fluid dynamics (CFD) and experimental data.
Increasing energy demand in today's world emphasizes the importance of optimal scheduling for distributed energy resources to minimize energy costs and greenhouse gas (GHG) emissions. The efficiency of this decisi...
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Increasing energy demand in today's world emphasizes the importance of optimal scheduling for distributed energy resources to minimize energy costs and greenhouse gas (GHG) emissions. The efficiency of this decision -making process relies on accurate modeling. In this paper, reinforcement learning (RL), an artificial intelligence -based approach, is proposed to optimize the energy management system (EMS) of an energy hub (EH). This EH contains renewable energy resources (RER), a combined heat and power (CHP), and a gas furnace. In order to meet electrical and thermal energy demand, available options such as day -ahead and real-time purchases from the main grid, RERs, and natural gas consumption are managed, with the preference of RERs to minimize GHG emissions and energy costs. With the adaptable RL method, a non-linear model of the CHP operation is constructed, considering the operational costs of the CHP. Furthermore, the natural gas tariff is varied according to the consumption level of the microgrid. Finally, this paper presents an RL-based method for EMS optimization of an EH with day -ahead and real-time scheduling, applied to a 24 -hour case study with linear and nonlinear modeling of the problem and sensitivity analysis of the parameters. Corresponding simulation results show the efficiency of the presented approach.
In order to solve the problems of multi-work and uncertainty in the grinding process, a process monitoring method called Multimodal Complete Information Principal Component analysis was proposed in this paper. In this...
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ISBN:
(纸本)9798350334722
In order to solve the problems of multi-work and uncertainty in the grinding process, a process monitoring method called Multimodal Complete Information Principal Component analysis was proposed in this paper. In this method, SDAE-K-Means++ is adopted to carry out modal partitioning of grinding production processdata, and online mode recognition is realized by calculating the similarity between mode center and online data. At the same time, considering the uncertainty characteristics of field data due to noise and sensor drift, CIPCA was combined with modal center estimation for monitoring. This method combined interval dataprocessing with interval monitoring method to reduce the impact of uncertain information. By applying to the actual grinding process of a mine to verify the effectiveness of the proposed method.
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