WELDING QUALITY CLASSIFICATION APPARATUS
First Claim
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1. A welding quality classification apparatus in which a data point indicating feature information whose components include a plurality of features, the plurality of features being obtained based on at least one of physical quantities including welding current, welding voltage, welding force of welding electrode, and displacement of welding electrode when a welded joint to be classified whose welding quality is unknown is welded, is mapped to a point in a mapping space which has a dimensional number higher than the number of the features constituting the feature information, and determination is made as to which of regions of two welding qualities, which are formed by separating the mapping space, contains the mapped point to classify the welding quality of the welded joint to be classified to be a welding quality corresponding to the region where the mapped point is located, the welding quality classification apparatus comprising:
- an acquisition section for acquiring the features;
a determination section for determining a discriminant function indicating a decision boundary which separates the mapping space; and
a classification section for classifying the welding quality of the welded joint to be classified based on an output value of the discriminant function when the feature information of the welded joint to be classified is inputted into the discriminant function determined by the determination section;
whereinthe acquisition section comprises a detection portion for measuring at least one of physical values including welding current, welding voltage, welding force of welding electrode, and displacement of welding electrode when the welded joint to be classified is welded, and a feature extraction portion for extracting the features based on physical values measured by the measurement portion,each of the two welding qualities is a predetermined and mutually different welding quality,the determination section determines the discriminant function by using the feature information of a training dataset which is known to have either one of the two welding qualities,the discriminant function is a function that consists of a kernel function k(x, x′
) which outputs a mapped point of a training dataset whose feature information is inputted when the feature information of the training dataset having either one or the other welding quality of the two welding qualities is inputted, and a weight of each feature constituting the feature information, which is attached to the kernel function k(x, x′
), andthe kernel function k(x, x′
) is a kernel function in which a matrix K whose elements are given as k(x, x′
) is positive semi-definite, x is the feature information of a training dataset having one of the welding qualities, and x′
is the feature information of a training dataset having the other of the welding qualities, whereinthe determination section;
determines the weight of each feature constituting the feature information for a predetermined regularization parameter so as to minimize the value of an error function, which consists of a sum of;
classification error which is defined by the difference between the output value of the discriminant function when the feature information of the training dataset having one of the welding qualities is inputted into the kernel function k(x, x′
) and the value corresponding to the one of the welding qualities, and the difference between the output value of the discriminant function when the feature information of the training dataset having the other of the welding qualities is inputted into the kernel function k(x, x′
) and the value corresponding to the other of the welding qualities, decreases as the absolute value of either one of the two differences decreases, and increases as the absolute value increases; and
a regularization term multiplied by the regularization parameter, wherein the regularization term has a positive correlation with the dimensional number of the discriminant function, and varies according to the weight of each feature constituting the feature information, andwhen the weight of each feature constituting the feature information which has been determined to minimize the value of the error function is temporarily adopted as the weight of each feature constituting the discriminant function,if the number of misclassification, which is the sum of the number of training dataset having one of the welding qualities, for which the absolute value of the difference between the output value of the discriminant function when the feature information of a training dataset having one of the welding qualities is inputted into the kernel function k(x, x′
) and the value corresponding to the one of the welding qualities is smaller than the absolute value of the difference between the output value of the discriminant function when the feature information of a training dataset having one of the welding qualities is inputted into the kernel function k(x, x′
) and the value corresponding to the one of the welding qualities, and the number of training dataset having the other of the welding qualities for which the absolute value of the difference between the output value of the discriminant function when the feature information of a training dataset having the other of the welding qualities is inputted into the kernel function k(x, x′
) and the value corresponding to the one of the welding qualities is smaller than the absolute value of the difference between the output value of the discriminant function when the feature information of a training dataset having the other of the welding qualities is inputted into the kernel function k(x, x′
) and the value corresponding to the other of the welding qualities, is not less than a predetermined value;
adjusts the regularization term parameter to determine the weight of each feature constituting the feature information again so as to minimize the value of the error function, andif the number of misclassification is less than the predetermined value;
ascertains that the weight of each feature constituting the feature information which has been determined so as to minimize the value of the error function is adopted as the weight of each feature constituting the discriminant function to determine the discriminant function.
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Abstract
The welding quality classification apparatus relating to the present invention is an apparatus, wherein a data point indicating feature information of a welded joint to be classified whose welding quality is unknown is mapped to a point in a mapping space which has a dimensional number higher than the number of the features constituting the feature information, and the welding quality of a welded joint to be classified is classified based on which of regions of two welding qualities, which are formed by separating the mapping space with a decision boundary, contains the mapped point, and wherein a discriminant function is determined by adopting a weight which minimizes the sum of the classification error corresponding to classification accuracy of a training dataset and a regularization term having a positive correlation with the dimensional number of the discriminant function as weight for each feature constituting the discriminant function indicating the decision boundary.
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Citations
2 Claims
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1. A welding quality classification apparatus in which a data point indicating feature information whose components include a plurality of features, the plurality of features being obtained based on at least one of physical quantities including welding current, welding voltage, welding force of welding electrode, and displacement of welding electrode when a welded joint to be classified whose welding quality is unknown is welded, is mapped to a point in a mapping space which has a dimensional number higher than the number of the features constituting the feature information, and determination is made as to which of regions of two welding qualities, which are formed by separating the mapping space, contains the mapped point to classify the welding quality of the welded joint to be classified to be a welding quality corresponding to the region where the mapped point is located, the welding quality classification apparatus comprising:
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an acquisition section for acquiring the features;
a determination section for determining a discriminant function indicating a decision boundary which separates the mapping space; and
a classification section for classifying the welding quality of the welded joint to be classified based on an output value of the discriminant function when the feature information of the welded joint to be classified is inputted into the discriminant function determined by the determination section;
whereinthe acquisition section comprises a detection portion for measuring at least one of physical values including welding current, welding voltage, welding force of welding electrode, and displacement of welding electrode when the welded joint to be classified is welded, and a feature extraction portion for extracting the features based on physical values measured by the measurement portion, each of the two welding qualities is a predetermined and mutually different welding quality, the determination section determines the discriminant function by using the feature information of a training dataset which is known to have either one of the two welding qualities, the discriminant function is a function that consists of a kernel function k(x, x′
) which outputs a mapped point of a training dataset whose feature information is inputted when the feature information of the training dataset having either one or the other welding quality of the two welding qualities is inputted, and a weight of each feature constituting the feature information, which is attached to the kernel function k(x, x′
), andthe kernel function k(x, x′
) is a kernel function in which a matrix K whose elements are given as k(x, x′
) is positive semi-definite, x is the feature information of a training dataset having one of the welding qualities, and x′
is the feature information of a training dataset having the other of the welding qualities, whereinthe determination section; determines the weight of each feature constituting the feature information for a predetermined regularization parameter so as to minimize the value of an error function, which consists of a sum of;
classification error which is defined by the difference between the output value of the discriminant function when the feature information of the training dataset having one of the welding qualities is inputted into the kernel function k(x, x′
) and the value corresponding to the one of the welding qualities, and the difference between the output value of the discriminant function when the feature information of the training dataset having the other of the welding qualities is inputted into the kernel function k(x, x′
) and the value corresponding to the other of the welding qualities, decreases as the absolute value of either one of the two differences decreases, and increases as the absolute value increases; and
a regularization term multiplied by the regularization parameter, wherein the regularization term has a positive correlation with the dimensional number of the discriminant function, and varies according to the weight of each feature constituting the feature information, andwhen the weight of each feature constituting the feature information which has been determined to minimize the value of the error function is temporarily adopted as the weight of each feature constituting the discriminant function, if the number of misclassification, which is the sum of the number of training dataset having one of the welding qualities, for which the absolute value of the difference between the output value of the discriminant function when the feature information of a training dataset having one of the welding qualities is inputted into the kernel function k(x, x′
) and the value corresponding to the one of the welding qualities is smaller than the absolute value of the difference between the output value of the discriminant function when the feature information of a training dataset having one of the welding qualities is inputted into the kernel function k(x, x′
) and the value corresponding to the one of the welding qualities, and the number of training dataset having the other of the welding qualities for which the absolute value of the difference between the output value of the discriminant function when the feature information of a training dataset having the other of the welding qualities is inputted into the kernel function k(x, x′
) and the value corresponding to the one of the welding qualities is smaller than the absolute value of the difference between the output value of the discriminant function when the feature information of a training dataset having the other of the welding qualities is inputted into the kernel function k(x, x′
) and the value corresponding to the other of the welding qualities, is not less than a predetermined value;
adjusts the regularization term parameter to determine the weight of each feature constituting the feature information again so as to minimize the value of the error function, andif the number of misclassification is less than the predetermined value;
ascertains that the weight of each feature constituting the feature information which has been determined so as to minimize the value of the error function is adopted as the weight of each feature constituting the discriminant function to determine the discriminant function. - View Dependent Claims (2)
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Current AssigneeNippon Steel & Sumitomo Metal Corporation (Nippon Steel Corporation)
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Original AssigneeNippon Steel & Sumitomo Metal Corporation (Nippon Steel Corporation)
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InventorsUchihara, Masato, Anayama, Kazunori, Suzuma, Toshiyuki, Nishibata, Hitomi, Fujimoto, Hiroki, Fukui, Kiyoyuki
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Application NumberUS13/878,917Publication NumberTime in Patent OfficeDaysField of SearchUS Class Current219/130.01CPC Class CodesB23K 11/115 by means of two electrodes ...B23K 31/125 Weld quality monitoringB23K 9/095 Monitoring or automatic con...
