Systems and methods for evaluating crimp applications
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.
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.
2. 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.
3. The power tool of claim 1 , wherein the electronic processor is configured to evaluate the crimping application using a random forest decision tree.
4. The power tool of claim 1 , wherein the electronic processor is configured to evaluate the crimping application using an artificial neural network.
5. 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.
6. 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.
7. The power tool of claim 1 , wherein the electronic processor is configured to normalize the vector using a Z-transform function.
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.
9. 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.
10. The method of claim 8 , wherein evaluating the crimping application based on the vector includes applying a random forest decision tree on the vector.
11. The method of claim 8 , wherein evaluating the crimping application based on the vector includes applying an artificial neural network on the vector.
12. 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.
13. The method of claim 8 , further comprising classifying the crimping application as one of a passing application and a failing application.
14. The method of claim 8 , further comprising normalizing the vector using a Z-transform function.
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.
16. 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.
17. The power tool of claim 15 , wherein the electronic processor is configured to evaluate the crimping application using an artificial neural network.
18. 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.
19. 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.
20. 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.