A method and a system for monitoring a laser machining process includes the steps of: inputting at least one process signal data set of the laser machining process into an autoencoder formed by a deep neural network; generating a reconstructed process signal data set by means of the autoencoder; determining a reconstruction error based on the at least one process signal data set and the at least one reconstructed process signal data set; and detecting an anomaly of the laser machining process based on the determined reconstruction error. A laser machining method includes the method and a laser machining system includes the system.

An ultrasonic bonding apparatus includes an ultrasonic bonding machine having an ultrasonic tool for applying an ultrasonic wave to a bonding target member mounted on a fixed object fixed to a jig, while pressing a bonding member against the bonding target member; and a bonding inspection apparatus for inspecting a bonding quality of the bonding target member and the bonding member. The bonding inspection apparatus includes: a bonded-state measuring device for detecting a vibration in the jig or a housing of the ultrasonic bonding machine equipped with the jig, to thereby output a detection signal; and a bonded-state determination device for determining, in a bonding process for the bonding target member and the bonding member, a bonded state between the bonding target member and the bonding member on the basis of the detection signal outputted by the bonded-state measuring device.

System and methods for using analytics and algorithms to predict weld quality are provided and include a computer having a processor and memory configured to receive weld parameter data generated during a welding process by a welder to join at least two parts with a weld, input the received weld parameter data to a data analytics model to generate at least one predicted weld quality parameter, compare the predicted weld quality parameter with a weld quality parameter threshold, and generate output indicating at least one of: the at least one predicted weld quality parameter and a result of the comparison between the at least one predicted weld quality parameter and the weld quality parameter threshold

A welding quality inspection apparatus for inspecting the welding quality of a plurality of welding parts formed on the material through the upper electrode and the lower electrode of the welding gun includes a position detection unit for detecting a position of the upper electrode, a control unit generating a position table based on a signal detected by the position detection unit during a total welding time of welding the plurality of welding parts of the material, generating first position data for a first welding time and second position data for a second welding time of a spot welding time of each of the welding parts based on the position table, checking whether the welding parts are defective by using the first position data, the second position data, and reference data, and generating result data based on whether the welding parts are detective, and an output unit for outputting the result data.

A welding quality detection system and a welding quality detection method are provided. A detection device applies a force to at least one weld point of a first welded object or a second welded object that are welded together. A displacement detector detects a displacement signal that varies with the force or time between the first welded object and the second welded object based on the force. A detection module receives or records the displacement signal and determines whether a gap exists between the first welded object and the second welded object based on a slope of the displacement signal, so as to detect the welding quality of the weld point quickly and precisely.

A welding quality detection system and an ultrasonic welding device, and a welding quality detection method are disclosed. The welding quality detection system includes a detection module, a control module and a display module. The detection module is configured for collecting a transverse friction force and a longitudinal welding pressure of a welded workpiece. The control module is electrically connected with the detection module, and the control module is configured for receiving, analyzing and processing a transverse friction force signal and a longitudinal welding pressure signal of the detection module. The display module is electrically connected with the control module, and the display module is configured for displaying analysis results of the control module. According to the application, a welding quality of the welded workpiece can be effectively detected, and a welding defect can be found in real time.

The present disclosure relates to a method and system for predicting the critical floating time of a reinforcing phase. According to the method, a particle concentration processing model, a half-life processing model, an agglomeration kinetics model, and a floating time processing model are combined to obtain the critical floating time of a reinforcing phase particle according to an initial particle size of the reinforcing phase particle, a density of the reinforcing phase particle, a mass fraction of the reinforcing phase of a composite soldering material, and a density of the composite soldering material. The method and system can accurately predict the critical floating time of the reinforcing phase particle.

This inspection device includes: a laser irradiation unit that irradiates a wafer having a back surface and a front surface with a laser beam from the back surface side of the wafer; an imaging unit that outputs light having permeability to the wafer and detects the light propagating through the wafer; and a control part configured to perform a first process of controlling the laser irradiation unit so that a modified region is formed inside the wafer by irradiating the wafer with the laser beam and a second process of deriving a position of the modified region on the basis of a signal output from the imaging unit that detects the light and deriving a thickness of the wafer on the basis of the derived position of the modified region and a set recipe.

An on-line quantitative evaluation method for the stability of a welding process includes the steps of monitoring and acquiring the arc voltage U and the welding current I during the welding process, and drawing a phase diagram of each U-I cycle; converting the phase diagram of each U-I cycle into a binary image K; obtaining an area J.sub.N through which a dynamic working curve passes in the binary image K; obtaining a welding process stability evaluation index P according to the formula (1), where J.sub.N is the area of a U-I curve, N is the number of cycles passed, L is the total number of samples in N cycles, and P is the repetition rate of the i-th U-I cycle and other cycles (i=1 . . . N); and evaluating the stability of the welding process according to the obtained welding stability evaluation index P.

A system and method of electric arc welding that includes a welding apparatus having an electric arc welder torch with sensors to determine the absolute position of the torch tip and the relative position of the torch tip to the weld joint during automatic welding. Combining absolute and relative positional data can be used to adjust the path of the robot during automated or robotic welding in response to variations in the weld joint.