7 results about "Optimal composition" patented technology

The disclosure relates to a simulation method for selecting an optimal composition ratio of an oxide semiconductor and an electronic device including the oxide semiconductor. The oxide semiconductor includes: at least two elements selected from the group consisting of indium (In), gallium (Ga), zinc (Zn), tin (Sn), silver (Ag), aluminum (Al), cadmium (Cd), magnesium (Mg), antimony (Sb), silicon (Si), titanium (Ti), and zirconium (Zr); oxygen (O); and unavoidable impurities. The simulation method includes: setting a simulation target composition ratio set including various composition ratios of elements constituting an oxide semiconductor; checking whether the oxide semiconductor satisfies Equation 1, Equation 2, and Equation 3 for each of the various Equation ratios included in the simulation target Equation ratio set; and selecting a composition ratio satisfying formula 1, formula 2, and formula 3 as an optimal composition ratio. Formula 1, Formula 2, and Formula 3 may be the same as described in the specification.

The invention discloses an MICP reaction control method and application, and belongs to the crossing field of molecular biomineralization and civil construction, the MICP reaction control method provided by the invention comprises the following steps: S1, testing and obtaining an optimal composition ratio of a cementing fluid; s2, testing and obtaining the optimal concentration of the cementing fluid according to the optimal composition ratio of the cementing fluid; and S3, carrying out an MICP reaction to generate an MICP product, and analyzing the pure MICP product. By adopting the control method provided by the invention, the MICP reaction can be effectively controlled, the MICP reaction control method provided by the invention can be applied to a plurality of technical fields, has the advantages of easiness in control and low time cost, and reduces the technical barrier of application of the MICP in the civil engineering field.

The present invention relates to the technical field of die-casting aluminum alloys, and specifically relates to a design method, a storage medium, and a device for an Al-Si-Mg-Cu heat-treatment-free die-casting aluminum alloy. The method includes establishing a thermodynamic database of the Al-Si-Mg-Cu quaternary system at the Al-rich end; obtaining the solidification microstructure information of the Al-Si-Mg-Cu alloy; training a Gaussian regression model using data with corresponding input features and output features, and completing the training when R<supgt;2< / supgt> is greater than or equal to 0.9; screening out the optimal composition points within the preset alloy composition content range. The storage medium stores computer program instructions; the device includes at least one processor, at least one memory, and computer program instructions stored in the memory; when the computer program instructions are executed by the processor, the method is implemented. The present invention can quickly screen out the optimal composition points within the preset alloy composition content range, and accelerate the design of new high-performance heat-treatment-free die-casting aluminum alloys.

The embodiment of the invention relates to the field of aluminum-based material design, and discloses an aluminum-based material design method and device based on multi-performance integrated prediction.The method comprises the steps that according to multi-performance index requirements, optimal components and technological parameters of all working procedures are determined through a multi-performance integrated predication model; the multi-performance integrated prediction model is obtained by training based on a machine learning database at least containing components, process parameters, mechanical properties and defect information, the input comprises the components and the process parameters of each process, the output comprises multi-performance indexes, and the multi-performance indexes at least comprise the mechanical properties and the defect information; performing multi-performance index experimental verification; and under the condition that the verification result does not meet the multi-performance index requirement, adjusting the current optimal component and the process parameters of each process, and performing multi-performance index experimental verification again. According to the method, the problem of intelligently optimizing the design parameters of the aluminum-based material is solved, the aluminum-based material components and process parameters meeting the requirements of multiple performance indexes can be determined, and the quality of the aluminum-based material is remarkably improved.

The invention discloses a method for determining components of a titanium alloy. The method comprises the following steps that the adjusting ranges and the step lengths of Ti, Al, V, Mo and Si are obtained; obtaining a plurality of groups of components according to the adjusting ranges and step lengths of Ti, Al, V, Mo and Si; simulating and generating a plurality of phase structures based on each group of components, and calculating mole fractions of key phases in the phase structures; and inputting the mole fractions corresponding to each group of components into a model to obtain a plurality of performances corresponding to each group of components, so as to determine an optimal component according to the plurality of performances. According to the scheme provided by the invention, a brand-new correlation model among components, tissues and performance is constructed, the influence of the components on the phase tissues is quickly predicted through thermodynamic calculation, and a prediction model of the tissues on the performance is constructed in combination with a machine learning algorithm, so that quick feedback from the components to the tissues to the performance is realized; and the existing titanium alloy components are effectively improved. According to the method, the research and development period and the test cost can be greatly shortened, and the scientificity and the accuracy of material design can be improved.

The application discloses a data labeling method and device, equipment and a readable storage medium, which can be applied to the field of artificial intelligence technology, and automatically labels the composition frame score of each candidate frame based on a pre-trained aesthetic large model, performs first labeling to obtain a high-score composition image, performs second labeling based on the sample labeling score of the high-score composition image fed back by a client, obtains multiple optimal composition frames and corresponding labeling score results, the first labeling is realized based on automatic labeling, and the second optimal selection is realized based on a small amount of manual labeling, that is, it is not necessary to score each candidate frame manually, the pre-trained aesthetic large model and a small amount of manual intervention are used to obtain a labeling training set for retraining the aesthetic large model, the aesthetic large model is fine-tuned based on the labeling training set, the artificial cost of constructing the aesthetic large model is reduced, and the accuracy of automatic labeling of the aesthetic large model is improved.

The invention provides an AI prediction driven magnesium alloy dissolution behavior control method and system, and relates to the technical field of new material design, and the method comprises the steps: carrying out the parameter collection of a use water area environment of a sonar buoy, and obtaining an environment data set; by taking the environment data set as a conditional constraint and an alloy component parameter threshold value as an optimization space, alloy component parameter optimization is carried out based on the component fitness evaluation function until convergence, and an optimal component parameter is output; and preparing the sonar buoy according to the optimal component parameters. By means of the method, the technical problem that a traditional magnesium-based alloy production method cannot accurately set magnesium-based alloy component parameters according to the sonar buoy using environment can be solved, and scientificity and accuracy of magnesium-based alloy component parameter setting can be improved through environment data driving and alloy component optimization design; the controllable dissolution rate and stable corrosion performance of the sonar buoy in a specific environment are realized, so that the performance of the buoy is effectively improved, and the service life of the buoy is effectively prolonged.