Illinois Tool Works Inc.

Leland D Falde

Stephen P Ivkovich

Vincent N Romano

Adam Kirk Pliska

Craig Jeffords

Todd Michael McEllis

Praveen Dandu

Jason Bonnell

Tret West

Ben Buhr

Systems and methods for distributed weld monitoring using jobs and job sessions are described. In some examples, a distributed monitoring system comprises a central monitoring station in communication with a user device and a local monitoring station. A user may use the user device to enter weld monitoring data that is subsequently received by the central monitoring station and stored in a central data repository. The central data repository may associate the weld monitoring data with welding data received from a welding device, as well as with a job session that is, in turn, associated with a job.

Distributed weld monitoring system with job tracking

Systems and methods are disclosed relating to welding sequence guidance using three-dimensional (3D) models. In some examples, a welding sequence program may use 3D models, rather than two-dimensional (2D) images, to guide operators through welding sequences. Since only one 3D model must be saved for each sequence, rather than potentially hundreds of 2D images, substantial memory space may be saved. Additionally, the same 3D model may be used for several welding sequences. Further, the 3D model may be animated to help the operator understand changes in perspective between steps of the welding sequence.

Welding sequence guidance using three dimensional models

Some examples relate to a welding-type monitoring system configured to track parts, production, and/or operations within a welding-type production cell. In some examples, the monitoring system comprises a computing system, one or more tags attached to one or more first items, and/or one or more tag readers attached to one or more second items. In some examples, the one or more tags store data relating to the first items, and the one or more tag readers are configured to read the data using a close proximity communication protocol when in range of the one or more tags. After the tag reader reads the data from the tag, the computing system may determine whether one or more workflow events are associated with the data. If so, the computing system may execute an event script corresponding to the workflow event.

Automated monitoring systems for welding-type production cells

Systems and methods for weld monitoring systems with unknown downtime disabling are described. In some examples, a local monitoring station may perform activity tracking as part of a larger weld monitoring system. A welding device may send welding data to the local monitoring system, which may be used to determine a current activity. A user may also manually input an activity to use as the current activity. If the local monitoring station is unable to determine a current activity from the welding data or user input, then the local monitoring station determines that an unknown downtime has occurred. If the local monitoring station cannot determine a reason for the unknown downtime, the welding device may be disabled until the user provides a reason for the unknown downtime.

Weld monitoring systems with unknown downtime disabling

Systems and methods to design part weld processes are disclosed. An example system for monitoring welding of components of a workpiece with multiple arc welds in a fixture includes: one or more weld sensors configured to collect data associated with the multiple arc welds; and a computing device configured to: access a weld program; provide weld instructions defined in the weld program and associated with a sequence of welds for the workpiece, the weld instructions comprising a visual slide including an image of the workpiece and a visual indication of a first location of a first weld; based on the weld program, display the visual slide at a first zoom level; based on the weld program, display the visual slide at a second zoom level; and monitor first weld data from the one or more sensors during a first weld to determine a status of the first weld.

Systems and methods to design part weld processes

Systems and methods to design part weld processes using media libraries are disclosed. An example system to generate weld instructions for display to a weld operator during a welding sequence, the system including: a processor; and a machine readable storage device comprising machine readable instructions which, when executed by the processor, cause the processor to: provide an interface to define a weld program comprising a sequence of weld instructions for display to a weld operator during a weld sequence; in response to an input specifying a location of an object in a media library, defining an element in the weld program based on an identifier of the object in the media library; and generating the weld program by including the identifier of the object in the media library in association with the element.

Systems and methods to design part weld processes using media libraries

Systems and methods for welding are described. The welding system can include, for example, a welding power source, a welding torch, and a computer. The computer and the welding torch can be operatively coupled to the power source. A first weld is performed and its signature is saved by the computer. It is considered a high quality weld and is selected as a weld reference. A second weld is performed and its signature is saved by the computer. The computer then computes a single weld confidence result for the second weld based on a comparison between the signature data of the second weld and the signature data of the reference weld. A weld fault condition is triggered based on the single weld confidence result which causes the welding system to stop or to modify the welding operation, and/or which causes the welding system to send out communications relating to the triggering of the weld fault condition.

Systems and methods for providing weld quality confidence

Systems and methods for part quality confidence are described. In some examples a part quality confidence system may receive inputs (e.g., via sensor measurements, operator input(s), etc.) related to one or more stages of a part assembly process. The inputs may be representative of certain feature characteristics of the part and/or one or more of the assembly stages. A computational engine of the system may determine one or more quality characteristics of the part based on the feature characteristics, and assign a quality metric to the part (and/or part assembly process) based on the quality characteristics. In some examples, a quality rating may be assigned to the part based on the quality metric, so as to provide an even simpler abstraction.

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Jackson, MI, United States of America

Leland D Falde