# US 12,224,545 B2 · Systems and methods for evaluating crimp applications > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,224,545 B2 - Document ID: US12224545B2 - Status: Granted - Kind: B2 - Application number: 17/845,127 - Filed: 2022-06-21 - Published: 2022-12-22 - Granted: 2025-02-11 - Earliest claimed: 2021-06-21 - Assignees: Milwaukee Electric Tool Corporation - Applicants: Milwaukee Electric Tool Corporation - Inventors: Timothy J. Radtke; Carl B. Westerby; Jonathan E. Abbott; Corey J. Dickert - Web page: https://patentgrep.com/patent/US12224545B2 ## CPC - B25F 5/00: Details or components of portable power-driven tools not particularly related to the operations performed and not otherwise provided for - H01R 43/0428: {Power-driven hand crimping tools} - B25B 27/10: inserting fittings into hoses - H01R 43/0486: {with force measuring means} - B25F: COMBINATION OR MULTI-PURPOSE TOOLS NOT OTHERWISE PROVIDED FOR; DETAILS OR COMPONENTS OF PORTABLE POWER-DRIVEN TOOLS NOT PARTICULARLY RELATED TO THE OPERATIONS PERFORMED AND NOT OTHERWISE PROVIDED FOR {(machines or arrangements of machines for performing specified combinations of different metal-working operations not covered by a single other subclass B23P23/00 and subgroups)} - H01R: ELECTRICALLY-CONDUCTIVE CONNECTIONS; STRUCTURAL ASSOCIATIONS OF A PLURALITY OF MUTUALLY-INSULATED ELECTRICAL CONNECTING ELEMENTS; COUPLING DEVICES; CURRENT COLLECTORS - B25B: TOOLS OR BENCH DEVICES NOT OTHERWISE PROVIDED FOR, FOR FASTENING, CONNECTING, DISENGAGING, OR HOLDING ## Abstract Systems and methods for evaluating a crimping application. A power tool includes 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 pressure sensor configured to provide pressure signals associated with a crimping application. The power tool also includes an electronic processor connected to the pressure sensor. The electronic processor is configured to monitor, while performing the crimping application, a pressure applied by the piston cylinder, construct a pressure curve indicative of a change in the pressure applied during the crimping application, process the pressure curve into a vector indicative of one or more features, evaluate the crimping application based on the vector, and provide an output indicative of the evaluation. ## Claims ### Claim 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; a pressure sensor configured to provide pressure signals associated with a crimping application; and an electronic processor connected to the pressure sensor, the electronic processor configured to: monitor, while performing the crimping application, a pressure applied by the piston cylinder, construct a pressure curve indicative of a change in the pressure applied during the crimping application, process the pressure curve into a vector indicative of one or more features, evaluate the crimping application based on the vector, and provide an output indicative of the evaluation. ### Claim 2 (depends on 1) The power tool of claim 1, wherein the one or more features includes at least one selected from the group consisting of a cumulative time during the crimping application spent below a first pressure threshold, a cumulative time during the crimping application spent above a second pressure threshold, a total crimping application time, a hydraulic work performed during the crimping application, and average derivatives of the pressure curve over a plurality of intervals. ### Claim 3 (depends on 1) The power tool of claim 1, wherein the electronic processor is configured to evaluate the crimping application using a random forest decision tree. ### Claim 4 (depends on 1) The power tool of claim 1, wherein the electronic processor is configured to evaluate the crimping application using an artificial neural network. ### Claim 5 (depends on 4) The power tool of claim 4, wherein a first layer of the artificial neural network includes at least triple a number of nodes as a number of inputs to the artificial neural network. ### Claim 6 (depends on 1) The power tool of claim 1, wherein the electronic processor is configured to: classify the crimping application as one of a passing application and a failing application; and identify a type of the crimping application. ### Claim 7 (depends on 1) The power tool of claim 1, wherein the electronic processor is configured to normalize the vector using a Z-transform function. ### Claim 8 A method for evaluating crimping applications, the method comprising: monitoring, while performing a crimping application, a pressure applied during the crimping application; constructing a pressure curve indicative of a change in the pressure applied during the crimping application; processing the pressure curve into a vector indicative of one or more features; evaluating the crimping application based on the vector; and providing an output indicative of the evaluation. ### Claim 9 (depends on 8) The method of claim 8, wherein the one or more features includes at least one selected from the group consisting of a cumulative time during the crimping application spent below a first pressure threshold, a cumulative time during the crimping application spent above a second pressure threshold, a total crimping application time, a hydraulic work performed during the crimping application, and average derivatives of the pressure curve over a plurality of intervals. ### Claim 10 (depends on 8) The method of claim 8, wherein evaluating the crimping application based on the vector includes applying a random forest decision tree on the vector. ### Claim 11 (depends on 8) The method of claim 8, wherein evaluating the crimping application based on the vector includes applying an artificial neural network on the vector. ### Claim 12 (depends on 11) The method of claim 11, wherein a first layer of the artificial neural network includes at least triple a number of nodes as a number of inputs to the artificial neural network. ### Claim 13 (depends on 8) The method of claim 8, further comprising classifying the crimping application as one of a passing application and a failing application. ### Claim 14 (depends on 8) The method of claim 8, further comprising normalizing the vector using a Z-transform function. ### Claim 15 A power tool comprising: a pair of jaws configured to crimp a workpiece; a piston cylinder configured to be actuated to operate the pair of jaws to perform 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: monitor, while performing the crimping application, a power tool characteristic associated with the crimping application, construct a derivative curve indicative of a change in the power tool characteristic during the crimping application, process the derivative curve into a vector indicative of one or more features, evaluate the crimping application based on the vector, and provide an output indicative of the evaluation. ### Claim 16 (depends on 15) The power tool of claim 15, wherein the one or more features includes at least one selected from the group consisting of a cumulative time during the crimping application spent below a first pressure threshold, a cumulative time during the crimping application spent above a second pressure threshold, a total crimping application time, a hydraulic work performed during the crimping application, and average derivatives of the derivative curve over a plurality of intervals. ### Claim 17 (depends on 15) The power tool of claim 15, wherein the electronic processor is configured to evaluate the crimping application using an artificial neural network. ### Claim 18 (depends on 17) The power tool of claim 17, wherein a first layer of the artificial neural network includes at least triple a number of nodes as a number of inputs to the artificial neural network. ### Claim 19 (depends on 15) The power tool of claim 15, wherein the electronic processor is configured to: classify the crimping application as one of a passing application and a failing application, and identify a type of the crimping application. ### Claim 20 (depends on 15) The power tool of claim 15, wherein the output indicative of the evaluation includes a type of the crimping application, a time the crimping application was performed, and a location the crimping application was performed.