US-12627107-B2 - System and methods for determining crimp applications and reporting power tool usage

Abstract

Systems and methods for reporting usage of a power tool. The power tool comprises a pair of jaws configured to crimp a workpiece, a piston cylinder configured to actuate at least one of the pair of jaws, and a sensor configured to sense operating characteristics associated with a crimping application. An electronic processor connected to the sensor. The electronic processor is configured to receive, from the sensor, one or more characteristic signals, determine, based on the one or more characteristic signals, a first operating characteristic of the power tool, and determine, based on the one or more characteristic signals, a second operating characteristic of the power tool. The electronic processor is configured to determine the crimping application of the power tool based on the first operating characteristic and the second operating characteristic and generate a report indicating the crimping application performed by the power tool.

Inventors

• Carl B. Westerby
• Jonathan E. Abbott
• Corey J. Dickert

Assignees

• MILWAUKEE ELECTRIC TOOL CORPORATION

Dates

Publication Date

20260512

Application Date

20220811

Claims (20)

1. 1 . A power tool comprising: a pair of jaws configured to crimp a workpiece; a piston cylinder configured to actuate at least one of the pair of jaws; one or more sensors configured to provide characteristic signals associated with a crimping application; and an electronic processor connected to the one or more sensors, the electronic processor configured to: receive, from the one or more sensors, one or more characteristic signals, determine, based on the one or more characteristic signals, a first operating characteristic of the power tool, determine, based on the one or more characteristic signals, a second operating characteristic of the power tool, compare the first operating characteristic and the second operating characteristic to a plurality of application profiles, determine the crimping application of the power tool based on the comparison, wherein, to determine the crimping application, the electronic processor is configured to determine a crimping application type including a material type of the workpiece that is crimped and a crimp size, and generate a report indicating the crimping application type performed by the power tool.
2. 2 . The power tool of claim 1 , wherein both the first operating characteristic and the second operating characteristic are selected from the group consisting of hydraulic work, contact distance, a maximum time derivative of pressure, an average time derivative of pressure, a minimum time derivative of pressure, a negative time derivative of pressure, a touch off time, a total operating time, an average time derivative of pressure, and an average second time derivative of pressure.
3. 3 . The power tool of claim 2 , wherein the first operating characteristic is hydraulic work, and the second operating characteristic is contact distance.
4. 4 . The power tool of claim 1 , wherein the electronic processor is configured to implement a random forest machine learning algorithm to determine the crimping application of the power tool.
5. 5 . The power tool of claim 1 , wherein the report includes the crimping application of the power tool, a time the crimping application was performed, and a location the crimping application was performed.
6. 6 . The power tool of claim 1 , wherein the power tool further includes a motor configured to actuate the piston cylinder, wherein the one or more sensors includes a voltage sensor configured to sense a voltage of the motor, wherein the one or more sensors includes a current sensor configured to sense a current of the motor, and wherein the electronic processor is further configured to: receive, from the voltage sensor, one or more voltage signals, receive, from the current sensor, one or more current signals, determine, based on the one or more voltage signals and the one or more current signals, the first operating characteristic of the power tool, and determine, based on the one or more voltage signals and the one or more current signals, the second operating characteristic of the power tool.
7. 7 . The power tool of claim 1 , wherein the one or more sensors include a pressure sensor configured to sense a pressure of the piston cylinder, and wherein the electronic processor is further configured to: receive, from the pressure sensor, one or more pressure signals, determine, based on the one or more pressure signals, the first operating characteristic of the power tool, and determine, based on the one or more pressure signals, the second operating characteristic of the power tool.
8. 8 . The power tool of claim 1 , wherein the plurality of application profiles comprises one or more pressure profiles, voltage profiles, or current profiles.
9. 9 . A method for reporting usage of a power tool, the method comprising: receiving, from one or more sensors, one or more characteristic signals, the one or more characteristic signals being associated with a crimping application by the power tool, where the power tool comprises a pair of jaws configured to crimp a workpiece and a piston cylinder configured to actuate at least one of the pair of jaws, determining, based on the one or more characteristic signals, a first operating characteristic of the power tool, determining, based on the one or more characteristic signals, a second operating characteristic of the power tool, comparing the first operating characteristic and the second operating characteristic to a plurality of application profiles, determining the crimping application of the power tool based on the comparison, wherein determining the crimping application includes determining a crimping application type including a material type of the workpiece that is crimped and a crimp size, and generating a report indicating the crimping application type performed by the power tool.
10. 10 . The method of claim 9 , wherein both the first operating characteristic and the second operating characteristic are selected from the group consisting of hydraulic work, contact distance, a maximum time derivative of pressure, an average time derivative of pressure, a minimum time derivative of pressure, a negative time derivative of pressure, a touch off time, a total operating time, an average time derivative of pressure, and an average second time derivative of pressure.
11. 11 . The method of claim 9 , wherein the first operating characteristic is hydraulic work, and the second operating characteristic is contact distance.
12. 12 . The method of claim 9 , further comprising using a random forest machine learning algorithm to determine the crimping application of the power tool.
13. 13 . The method of claim 9 , wherein the report includes the crimping application of the power tool, a time the crimping application was performed, and a location the crimping application was performed.
14. 14 . The method of claim 9 , wherein the one or more sensors includes a voltage sensor configured to sense a voltage of a motor of the power tool, wherein the one or more sensors includes a current sensor configured to sense a current of the motor, and wherein the method further includes: receiving, from the voltage sensor, one or more voltage signals, receiving, from the current sensor, one or more current signals, determining, based on the one or more voltage signals and the one or more current signals, the first operating characteristic of the power tool, and determining, based on the one or more voltage signals and the one or more current signals, the second operating characteristic of the power tool.
15. 15 . The method of claim 9 , wherein the one or more sensors includes a pressure sensor configured to sense a pressure of the piston cylinder of the power tool, and wherein the method further includes: receiving, from the pressure sensor, one or more pressure signals, determining, based on the one or more pressure signals, the first operating characteristic of the power tool, and determining, based on the one or more pressure signals, the second operating characteristic of the power tool.
16. 16 . A power tool comprising: a piston cylinder configured to actuate a pair of jaws during a crimping application; one or more sensors configured to sense power tool characteristics associated with the crimping application; and an electronic processor connected to the one or more sensors, the electronic processor configured to: receive, from the one or more sensors, one or more characteristic signals, determine, based on the one or more characteristic signals, a plurality of operating characteristics, compare the plurality of operating characteristics to a plurality of application profiles, determine the crimping application of the power tool based on the comparison, wherein, to determine the crimping application, the electronic processor is configured to determine a crimping application type including a material type of a workpiece that is crimped and a crimp size, and generate a report indicating the crimping application type performed by the power tool.
17. 17 . The power tool of claim 16 , wherein the report includes the crimping application of the power tool, a time the crimping application was performed, and a location the crimping application was performed.
18. 18 . The power tool of claim 16 , wherein the power tool further includes a motor configured to actuate the piston cylinder, wherein the one or more sensors includes a voltage sensor configured to sense a voltage of the motor, wherein the one or more sensors includes a current sensor configured to sense a current of the motor, and wherein the electronic processor is further configured to: receive, from the voltage sensor, one or more voltage signals, receive, from the current sensor, one or more current signals, and determine, based on the one or more voltage signals and the one or more current signals, the plurality of operating characteristics.
19. 19 . The power tool of claim 16 , wherein the one or more sensors includes a pressure sensor configured to sense a pressure of the piston cylinder, and wherein the electronic processor is further configured to: receive, from the pressure sensor, one or more pressure signals, and determine, based on the one or more pressure signals, the plurality of operating characteristics.
20. 20 . The power tool of claim 16 , wherein the electronic processor is configured to implement a random forest machine learning algorithm to determine the crimping application of the power tool.

Description

RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Patent Application No. 63/231,797, filed Aug. 11, 2021, the entire content of which is hereby incorporated by reference. FIELD Embodiments described herein relate to power tools. SUMMARY Systems described herein include a power tool including a pair of jaws configured to crimp a workpiece, a piston cylinder configured to actuate at least one of the pair of jaws, and one or more sensors configured to provide characteristic signals associated with a crimping application. The power tool includes an electronic processor connected to the one or more sensors. The electronic processor is configured to receive, from the one or more sensors, one or more characteristic signals, determine, based on the one or more characteristic signals, a first operating characteristics of the power tool, and determine, based on the one or more characteristic signals, a second operating characteristic of the power tool. The electronic processor is configured to determine the crimping application of the power tool based on the first operating characteristic and the second operating characteristic, and generate a report indicating the crimping application performed by the power tool. In some embodiments, both the first operating characteristic and the second operating characteristic are one selected from the group consisting of hydraulic work, contact distance, a maximum time derivative of pressure, an average time derivative of pressure, a minimum time derivative of pressure, a negative time derivative of pressure, a touch off time, a total operating time, an average time derivative of pressure, and an average second time derivative of pressure. In some embodiments, the first operating characteristic is hydraulic work, and the second operating characteristic is contact distance. In some embodiments, the electronic processor uses a random forest machine learning algorithm to determine the crimping application of the power tool. In some embodiments, the report includes the crimping application of the power tool, a time the crimping application was performed, and a location the crimping application was performed. In some embodiments, the power tool further includes a motor configured to actuate the piston cylinder, the one or more sensors includes a voltage sensor configured to sense a voltage of the motor, and the one or more sensors includes a current sensor configured to sense a current of the motor. In some embodiments, the electronic processor is configured to receive, from the voltage sensor, one or more voltage signals, receive from the current sensor, one or more current signals, determine, based on the one or more voltage signals and the one or more current signals, the first operating characteristic of the power tool, and determine, based on the one or more voltage signals and the one or more current signals, the second operating characteristic of the power tool. In some embodiments, the one or more sensors includes a pressure sensor configured to sense a pressure of the piston cylinder. In some embodiments, the electronic processor is configured to receive, from the pressure sensor, one or more pressure signals, determine, based on the one or more pressure signals, the first operating characteristic of the power tool, and determine, based on the one or more pressure signals, the second operating characteristic of the power tool. Methods described herein comprise receiving, from one or more sensors, one or more characteristic signals, the one or more characteristic signals being associated with a crimping application, determining, based on the one or more characteristic signals, a first operating characteristic of the power too, and determining, based on the one or more characteristic signals, a second operating characteristic of the power tool. The method includes determining the crimping application of the power tool based on the first operating characteristic and the second operating characteristic, and generating a report indicating the crimping application performed by the power tool. In some embodiments, both the first operating characteristic and the second operating characteristic are one selected from the group consisting of hydraulic work, contact distance, a maximum time derivative of pressure, an average time derivative of pressure, a minimum time derivative of pressure, a negative time derivative of pressure, a touch off time, a total operating time, an average time derivative of pressure, and an average second time derivative of pressure. In some embodiments, the first operating characteristic is hydraulic work, and the second operating characteristic is contact distance. In some embodiments, the method includes using a random forest machine learning algorithm to determine the crimping application of the power tool. In some embodiments, the report includes the crimping application of the power tool, a time the crimping application was per