Embodiments of welding work cells are disclosed. One embodiment includes a workpiece positioning system, a welding power source, and a welding job sequencer. The workpiece positioning system powers an elevating motion and a rotational motion of a workpiece mounted between a headstock and a tailstock to re-position the workpiece for a next weld to be performed. The welding power source generates welding output power based on a set of welding parameters of the power source. The welding job sequencer commands the workpiece positioning system to re-position the workpiece from a current position to a next position in accordance with a next step of a welding sequence of a welding schedule. The welding job sequencer also commands the welding power source to adjust a current set of welding parameters to a next set of welding parameters in accordance with the next step of the welding sequence of the welding schedule.

Various systems and methods are provided that allow a weld sequencer to use a statistical analysis of generated reports to automatically determine weld parameter limits for the welds defined by various functions in a sequence file. For example, the weld sequencer can take reports generated for a specific type of part and statistically analyze the weld data included in the reports according to a set of analysis parameters provided by a user. The weld sequencer can use the statistical analysis to identify and remove outlier data and define a set of weld parameter limits based on the remaining data. The weld parameter limits can define a low limit and/or a high limit for one or more weld parameters associated with a function. The weld sequencer can then update the sequence file to include the weld parameter limits.

A method for fastening material webs, such as roofing sheets made of plastic on a surface with fastening points (head disks including a hot-melt adhesive layer) arranged thereon using a self-propelled fastening unit (20) comprising the following steps: (A) detecting a route marking by means of a first detector (22) on the fastening unit (20) and moving the fastening unit along the route marking; (B) detecting and calculating the position of a head disk (14) by means of a second detector (24); (C) approaching and remaining at an operating position during the subsequent fastening process; (D) positioning an induction heater (30) and heating up the head disk (14) for a period of time Th; (E) removing the induction heater (30) and pressing the material web against the head disk by a cooling device (32); (F) taking off the cooling device after a predetermined time Tk has passed; (G) continuing with (A), until an end of the route marking is reached.

Methods, systems, and non-transitory computer-readable storage media having programs are described for monitoring an ultrasonic bonding operation via acoustic and/or vibration measurements and analyzing the measurements in order to predict and/or characterize the quality of a weld resulting from the bonding operation. The measurements are non-destructively acquired and the characterization is expressed as a bond quality index value.

A manufacturing monitoring assistance device includes: a model creation unit creating a computation model when a product as a sample is normal, based on a three-dimensional form acquired from the product; a simulation unit creating a corrective computation model when the product is abnormal, by adding a sample of an abnormal portion in the product to the created computation model, and performing a simulation on each of the computation model and the corrective computation model; and a monitoring method determination unit determining a method for monitoring a manufacturing process for the product, based on an abnormality index being a difference between an output from a sensor as a result of the simulation performed on the computation model and an output from a sensor as a result of the simulation performed on the corrective computation model, and causing an output device to display the determined method and the abnormality index.

Various systems and methods are provided that allow a weld sequencer to use a statistical analysis of generated reports to automatically determine weld parameter limits for the welds defined by various functions in a sequence file. For example, the weld sequencer can take reports generated for a specific type of part and statistically analyze the weld data included in the reports according to a set of analysis parameters provided by a user. The weld sequencer can use the statistical analysis to identify and remove outlier data and define a set of weld parameter limits based on the remaining data. The weld parameter limits can define a low limit and/or a high limit for one or more weld parameters associated with a function. The weld sequencer can then update the sequence file to include the weld parameter limits.

A teaching apparatus includes circuitry. The circuitry is configured to obtain result information corresponding to a position of a worked region on a workpiece. The circuitry is configured to generate first teaching information based on the result information. The first teaching information specifies a motion of an examination robot configured to examine the workpiece that has undergone work.

An AI enhanced self-correcting and closed loop SMT manufacturing system for fabricating PCBAs. The system includes a screen printer for depositing solder paste on solder pads on a RGB, an SRI sub-system for inspecting the solder paste deposited on the PCB to identify defects, a pick-and-place machine for placing circuit components on the solder paste, an AOI sub-system for inspecting the PCB after the circuit components are placed on the PCB, and a reflow soldering oven for bonding component leads both electrically and mechanically to the pads on the PCB. An AI/ML analysis engine is responsive to process data and variables from each of the screen printer, the SPI sub-system, the pick-and-place machine, the AOI sub-system and the reflow soldering oven and provides downstream feedback signals to each of the screen printer, the SPI sub-system, the pick-and-place machine, the AOI sub-system and the reflow soldering oven for self-correction purposes.

A method for fastening material webs, such as roofing sheets made of plastic on a surface with fastening points (head disks including a hot-melt adhesive layer) arranged thereon using a self-propelled fastening unit (20) comprising the following steps: (A) detecting a route marking by means of a first detector (22) on the fastening unit (20) and moving the fastening unit along the route marking; (B) detecting and calculating the position of a head disk (14) by means of a second detector (24); (C) approaching and remaining at an operating position during the subsequent fastening process; (D) positioning an induction heater (30) and heating up the head disk (14) for a period of time Th; (E) removing the induction heater (30) and pressing the material web against the head disk by a cooling device (32); (F) taking off the cooling device after a predetermined time Tk has passed; (G) continuing with (A), until an end of the route marking is reached.

A spot welding system comprises a robot which changes a relative position of a spot welding gun and a workpiece. A control device drives an electrode drive motor so that a movable electrode of the spot welding gun abuts on the workpiece, and is formed so as to perform a position detection control which detects a position of the workpiece based on a position of the movable electrode when a state value of the electrode drive motor deviates from a predetermined range. An operation program includes a workpiece detection parameter for performing the position detection control. The workpiece detection parameter is set at each of welding points in the operation program.