An elastic matrix determination method and a vibration analysis method for a laminated iron core, with which it is possible to optimally determine an elastic modulus of a laminated iron core. When a vibration analysis of a laminated iron core obtained by laminating steel sheets is performed by using a configuration expression indicating a relationship between stress and strain in a matrix display by using an elastic matrix, a shear modulus in two surfaces including a laminating direction of the laminated iron core included in the elastic matrix in the configuration expression is determined in consideration of slip between laminated steel sheets.

An elastic matrix determination method and a vibration analysis method for a laminated iron core, with which it is possible to optimally determine an elastic modulus of a laminated iron core. When a vibration analysis of a laminated iron core obtained by laminating steel sheets is performed by using a configuration expression indicating a relationship between stress and strain in a matrix display by using an elastic matrix, a shear modulus in two surfaces including a laminating direction of the laminated iron core included in the elastic matrix in the configuration expression is determined in consideration of slip between laminated steel sheets.

The present invention relates to a sampler for taking samples from a molten metal bath, particularly a molten steel bath, the sampler comprising: a carrier tube having an immersion end; a sample chamber assembly arranged on the immersion end of the carrier tube, the sample chamber assembly comprising a cover plate and a housing, wherein the housing comprises an immersion end having an opening; an inflow conduit having a first end for receiving molten metal and a second end, opposite the first end, wherein the second end is in communication with the opening, wherein the opening is configured to receive the molten metal from the inflow conduit; a measuring head, wherein the sample chamber and the second end of the inflow conduit are at least partly arranged in the measuring head; and a de-oxidant material arranged along a central axis of the inflow conduit, wherein at least part of the de-oxidant material is arranged near the second end of the inflow conduit inside the measuring head, and wherein the inflow conduit comprises first coupling means, arranged on the second end of the inflow conduit, wherein the de-oxidant material comprises second coupling means, to interact with the first coupling means on the inflow conduit to anchor the de-oxidant material in a position along the central axis of the inflow conduit. The invention also relates to a sampler for taking samples from a molten metal bath, particularly a molten steel bath, the sampler comprising:
a carrier tube having an immersion end;
a sample chamber assembly arranged on the immersion end of the carrier tube, the sample chamber assembly comprising a cover plate and a housing, wherein the housing comprises an immersion end having an opening;
an inflow conduit having a first end for receiving molten metal and a second end, opposite the first end, wherein the second end is in communication with the opening, wherein the opening is configured to receive the molten metal from the inflow conduit;
a measuring head, wherein the sample chamber and the second end of the inflow conduit are at least partly arranged in the measuring head; and
a metal bushing, wherein the metal bushing coupling the inflow conduit to the sample chamber.

The present invention relates to a sampler for taking samples from a molten metal bath, particularly a molten steel bath, the sampler comprising: a carrier tube having an immersion end; a sample chamber assembly arranged on the immersion end of the carrier tube, the sample chamber assembly comprising a cover plate and a housing, wherein the housing comprises an immersion end having an opening; an inflow conduit having a first end for receiving molten metal and a second end, opposite the first end, wherein the second end is in communication with the opening, wherein the opening is configured to receive the molten metal from the inflow conduit; a measuring head, wherein the sample chamber and the second end of the inflow conduit are at least partly arranged in the measuring head; and a de-oxidant material arranged along a central axis of the inflow conduit, wherein at least part of the de-oxidant material is arranged near the second end of the inflow conduit inside the measuring head, and wherein the inflow conduit comprises first coupling means, arranged on the second end of the inflow conduit, wherein the de-oxidant material comprises second coupling means, to interact with the first coupling means on the inflow conduit to anchor the de-oxidant material in a position along the central axis of the inflow conduit. The invention also relates to a sampler for taking samples from a molten metal bath, particularly a molten steel bath, the sampler comprising:
a carrier tube having an immersion end;
a sample chamber assembly arranged on the immersion end of the carrier tube, the sample chamber assembly comprising a cover plate and a housing, wherein the housing comprises an immersion end having an opening;
an inflow conduit having a first end for receiving molten metal and a second end, opposite the first end, wherein the second end is in communication with the opening, wherein the opening is configured to receive the molten metal from the inflow conduit;
a measuring head, wherein the sample chamber and the second end of the inflow conduit are at least partly arranged in the measuring head; and
a metal bushing, wherein the metal bushing coupling the inflow conduit to the sample chamber.

The invention belongs to the technical field of quantitative statistical distribution analysis for micro-structures of metal materials, and relates to a method for automatic quantitative statistical distribution characterization of dendrite structures in a full view field of metal materials. According to the method based on deep learning in the present invention, dendrite structure feature maps are marked and trained to obtain a corresponding object detection model, so as to carry out automatic identification and marking of dendrite structure centers in a full view field; and in combination with an image processing method, feature parameters in the full view field such as morphology, position, number and spacing of all dendrite structures within a large range are obtained quickly, thereby achieving quantitative statistical distribution characterization of dendrite structures in the metal material. The method is accurate, automatic and efficient, involves a large amount of quantitative statistical distribution information, and is statistically more representative as compared with the traditional measurement of feature sizes of dendrite structures in a single view field.

The invention belongs to the technical field of quantitative statistical distribution analysis for micro-structures of metal materials, and relates to a method for automatic quantitative statistical distribution characterization of dendrite structures in a full view field of metal materials. According to the method based on deep learning in the present invention, dendrite structure feature maps are marked and trained to obtain a corresponding object detection model, so as to carry out automatic identification and marking of dendrite structure centers in a full view field; and in combination with an image processing method, feature parameters in the full view field such as morphology, position, number and spacing of all dendrite structures within a large range are obtained quickly, thereby achieving quantitative statistical distribution characterization of dendrite structures in the metal material. The method is accurate, automatic and efficient, involves a large amount of quantitative statistical distribution information, and is statistically more representative as compared with the traditional measurement of feature sizes of dendrite structures in a single view field.

According to one embodiment, a grain size estimation device includes an acquisition unit that acquires a captured image of a surface segment of an object inducing metal; and an estimation unit that estimates a grain size of the surface segment of the object indicated in the acquired image, based on a predictive model generated by machine learning using images of metal surfaces and grain sizes in the metal surfaces as training data.

According to one embodiment, a grain size estimation device includes an acquisition unit that acquires a captured image of a surface segment of an object inducing metal; and an estimation unit that estimates a grain size of the surface segment of the object indicated in the acquired image, based on a predictive model generated by machine learning using images of metal surfaces and grain sizes in the metal surfaces as training data.

A photographing condition determining method includes: photographing a part of a metal structure of a metal material subjected to predetermined sample preparation under a predetermined photographing condition; assigning, to pixels corresponding to one or a plurality of predetermined phases of the metal structure, labels of respective phases for a photographed image; calculating one or more feature values for a pixel to which a label of one of the assigned phases; classifying the phases of the metal structure of the image by inputting a calculated feature value to a model, which has been learned in advance using feature values assigned with labels of respective phases as input and labels of the respective phases as output, and acquiring a label of a phase of a pixel corresponding to the input feature value; and determining a photographing condition when other parts of the metal structure are photographed based on a classification result.

A photographing condition determining method includes: photographing a part of a metal structure of a metal material subjected to predetermined sample preparation under a predetermined photographing condition; assigning, to pixels corresponding to one or a plurality of predetermined phases of the metal structure, labels of respective phases for a photographed image; calculating one or more feature values for a pixel to which a label of one of the assigned phases; classifying the phases of the metal structure of the image by inputting a calculated feature value to a model, which has been learned in advance using feature values assigned with labels of respective phases as input and labels of the respective phases as output, and acquiring a label of a phase of a pixel corresponding to the input feature value; and determining a photographing condition when other parts of the metal structure are photographed based on a classification result.