Welding system, welding method, welding support device, program, learning device, and method of generating trained model
This welding system comprises: a welding device, various different types of a plurality of sensors which detect an event according to welding performed by a welding device; and an estimation unit which uses a trained model that is pre-generated by machine-learning by taking, as input data, a plurality of pieces of data for learning obtained by detecting events according to welding by means of the same types of sensors as the plurality of sensors, and, as training data, labels representing whether the welding is normal or abnormal, thereby estimating an abnormality of the welding performed by the welding device from a plurality of pieces of detection data generated by the plurality of sensors.
1 . A welding system comprising:
a welding device;
a plurality of sensors of different types, which detect an event according to welding performed by the welding device;
a learning device programmed to pre-generate a trained model by machine-learning by performing the steps of taking, as input data, a plurality of pieces of data for learning obtained by detecting events according to the welding by the plurality of sensors, and taking, as training data, labels representing whether each of the pieces of data for learning indicate the welding is normal or abnormal; and
an estimation unit programmed to estimate, using the trained model, an abnormality of the welding performed by the welding device from a plurality of pieces of detection data generated by the plurality of sensors.
2 . The welding system according to claim 1 ,
wherein the plurality of pieces of detection data include two or more of an image obtained by capturing a weld zone during welding, welding sound, a voltage of a welding power source, and a current of a welding power source, and include at least one of the image and the welding sound.
3 . A welding system comprising:
a welding device;
a plurality of sensors in different types, which detect an event according to welding performed by the welding device; and
an estimation unit that estimates an abnormality of the welding performed by the welding device from a plurality of pieces of detection data generated by the plurality of sensors, using a trained model that is pre-generated by machine-learning by taking, as input data, a plurality of pieces of data for learning obtained by detecting events according to the welding by the plurality of sensors, and, as training data, labels representing whether the welding is normal or abnormal;
wherein one of the plurality of pieces of detection data is welding sound, a voltage of a welding power source, or a current of a welding power source,
a conversion unit is further provided configured to generate a spectrogram that represents the welding sound, the voltage of the welding power source, or the current of the welding power source in terms of three dimensions: time, frequency, and strength, and
the estimation unit inputs the spectrogram to the trained model.
4 . The welding system according to claim 1 ,
wherein the trained model generates, as training data, a feature value of at least one of the plurality of pieces of data for learning, and
the estimation unit further estimates a feature value of at least one of the plurality of pieces of detection data using the trained model.
5 . The welding system according to claim 4 ,
wherein one of the plurality of pieces of detection data is an image obtained by capturing a weld zone during welding, and
the estimation unit estimates a feature point in the image as the feature value.
6 . The welding system according to claim 4 ,
wherein one of the plurality of pieces of detection data is welding sound, a voltage of a welding power source, or a current of a welding power source, and
the estimation unit estimates, as the feature value, an interval of abnormal quality of the welding sound, the voltage of the welding power source, or the current of the welding power source.
7 . The welding system according to claim 4 , further comprising:
a similarity calculation unit that calculates a similarity between the feature value extracted from the one piece of data for learning and the feature value estimated by the estimation unit; and
a reliability determination unit that determines a reliability of an abnormality of welding estimated by the estimation unit based on the similarity.
8 . The welding system according to claim 1 ,
wherein the plurality of pieces of data for learning and the plurality of pieces of detection data are time series data.
9 . The welding system according to claim 8 ,
wherein the trained model includes a recurrent neural network.
10 . A welding system comprising:
a welding device;
a plurality of sensors in different types, which detect an event according to welding performed by the welding device; and
an estimation unit that estimates an abnormality of the welding performed by the welding device from a plurality of pieces of detection data generated by the plurality of sensors, using a trained model that is pre-generated by machine-learning by taking, as input data, a plurality of pieces of data for learning obtained by detecting events according to the welding by the plurality of sensors, and, as training data, labels representing whether the welding is normal or abnormal;
wherein the plurality of pieces of data for learning and the plurality of pieces of detection data are time series data; and
wherein the estimation unit
estimates a feature value of at least one of the plurality of pieces of detection data using a second trained model that is pre-generated by machine-learning by taking, as input data, at least one of the plurality of pieces of data for learning, and as training data, a feature value in the at least one piece of data for learning, and
inputs time series data of index based on the feature value to the trained model.
11 . The welding system according to claim 10 ,
wherein the time series data of index represents a shape of a molten pool in an image obtained by capturing a weld zone during welding.