Systems and methods are described for governing and monitoring operations of tools. The systems include a registration and control computer, one or more mobile devices, and one or more tools. The tools include electronic locking provisions which upon activation selectively enable tool operation.

A body and at least one control unit that stores and/or controls data for drilling processes is disclosed. At least one machining tool is located on the body extends outward from the body, and provides part shaping, at least one image capturing device that is controlled by the control unit and connected with the control unit for capturing images is also present.

Provided is a control method for a machining tool that processes a workpiece while causing a tool, which rotates by means of a main shaft motor, and the workpiece, which is secured to a table, to move relative to one another, wherein on the basis of the torque command value, the current command value, or the actual current value of the main shaft motor, calculated is the actual cutting force of the tool that is received from the workpiece at a location which is predetermined during the processing of the workpiece, and the actual cutting force that has been calculated is displayed on a display unit.

Portable, hand-held, battery operated, hydraulic tools are provided with a tool frame, a force sensor, and a location detector. A piston actuated by a hydraulic system within the tool frame applies force to the working head to perform a task, such as to apply a crimp to an electrical connector. The tool determines the maximum force applied to the crimp and records that maximum force along with the geographic location of the tool when the crimp was formed. The maximum force provides an indication of the quality of the crimp and the recorded location allows a potentially defective crimp to be located. Images of crimps can be uploaded to a web service and linked to cycle information for retrieval and review at a later date.

Provided are an abnormality determination system and program that make it possible to accurately detect abnormalities at a machine tool. An abnormality determination system that comprises a detection threshold determination unit that determines a detection threshold for detecting abnormalities at the machine tool on the basis of a motor inference information and an inference precision, a motor measurement unit that acquires motor measurement information about a motor, and a state determination unit that determines the state of the machine tool on the basis of the motor measurement information and the detection threshold.

A part analysis tool is used to analyze aspects of a composite part. The part analysis tool includes a verification control unit that compares numerical control data used to control operation of a forming system that is used to form the composite part to computer-aided design (CAD) data that includes an authoritative part definition for the composite part. The verification control unit determines whether the numerical control data is within one or more conformance thresholds related to the CAD data. The part analysis tool may also include an inspection control unit that compares inspection data of one or more plies of the composite part to the CAD data. The inspection control unit determines whether the inspection data is within the conformance threshold(s) related to the CAD data.

This control system takes into account the thermomechanical aspects of materials to quickly and easily determine optimal cutting conditions and to automatically control machining to preserve the integrity of the workpiece. This system includes an acquisition module configured to acquire values of a set of input parameters relating to cutting conditions and material properties of the piece, and a microprocessor configured for determining at least one operating cutting parameter representative of a cutting signal from the machining apparatus using a set of output parameters of an integrity model previously constructed during a learning phase. The integrity model connects the set of input parameters to the set of output parameters comprising specific cutting coefficients representative of the material integrity of the piece, and establishes at least one fatigue threshold of the at least one cutting operating parameter. The fatigue threshold allows control of the progress of cutting operations.

An example welding system includes a power supply configured to output welding power; a weld parameter feedback sensor configured to produce a weld feedback parameter corresponding to an actual weld condition during welding; and a welding program comprising: a first weld having first weld parameters; a second weld having second weld parameters, wherein the second weld follows the first weld; a controller configured to, in response to selection of the weld program for performing a welding job on a part: control the power supply to provide the welding power to a first weld operation based the first weld parameters; monitor first feedback from the power supply; control the power supply to provide the welding power to a second weld operation based the second weld parameters; monitor second feedback from the power supply; and determine whether at least one of the welding job or the part is acceptable.

A diagnosis device includes a first acquisition unit, a second acquisition unit, and an associating unit. The first acquisition unit is configured to acquire, from a machine tool, context information including at least a monitoring specifying number identifying a machining process. The second acquisition unit is configured to acquire detection information output from a detection unit installed for the machine tool. The associating unit is configured to associate the monitoring specifying number included in the context information acquired by the first acquisition unit and the detection information acquired by the second acquisition unit.

A welding system including an arc monitoring, training, and control system is disclosed. The welding system includes a power supply, controller, and associated memory. When a weld is performed, the weld command and weld feedback parameters can be stored in the memory, along with associated alarm limit values. During subsequent welds, the input weld commands and actual feedback values can be compared to the established limits, and a fault signal provided to an operator or supervisor when the value exceeds the established limits. The fault signals can be used for training operators, as well as providing monitoring signals, and can be stored with weld data in a database for later analysis. In addition, collected weld data can be used to determine when to clean, repair, or replace consumables, including, for example, contact tips, wire drive liners, and drive rolls, and to monitor usage of wire and gas.