Power tool including a machine learning block for controlling a seating of a fastener
A power tool is provided including a housing a motor supported by the housing, a sensor supported by the housing, and an electronic controller. The sensor is configured to generate sensor data indicative of an operational parameter of the power tool. The electronic controller includes an electronic processor, and a memory. The memory includes a machine learning control program for execution by the electronic processor. The electronic processor is configured to receive the sensor data, and process the sensor data, using the machine learning control program. The electronic processor is further configured to generate, using the machine learning control program, an output based on the sensor data, the output indicating a seating value associated with a fastening operation of the power tool. The electronic processor is further configured to control the motor based on the generated output.
1. A power tool comprising:
a housing;
a motor supported by the housing;
a sensor supported by the housing and configured to generate sensor data indicative of an operational parameter of the power tool;
an electronic controller, the electronic controller including an electronic processor and a memory, the memory including a machine learning control program for execution by the electronic processor, the electronic controller configured to:
receive the sensor data,
process the sensor data, using the machine learning control program,
generate, using the machine learning control program, an output based on the sensor data, the output indicating a seating value associated with a fastening operation of the power tool, wherein the seating value indicates a level of seating of the fastener, wherein the level represents a percentage that the fastener is seated within a workpiece, and
control the motor based on the generated output.
2. The power tool of claim 1 , wherein the machine learning control program is generated on an external system device through training based on exemplary sensor data and associated outputs, and is received by the power tool from the external system device.
3. The power tool of claim 2 , wherein the machine learning control program is one of a static machine learning control program and a trainable machine learning control program.
4. The power tool of claim 1 , wherein the electronic controller is configured to reduce a speed of the motor based on the seating value indicating that the fastener has started seating.
5. The power tool of claim 1 , wherein the electronic controller is configured to stop the motor of the power tool based on the seating value indicating that the fastener is fully seated.
6. A method of operating a power tool to control fastener fastening, the method comprising:
generating, by a sensor of the power tool, sensor data indicative of an operational parameter of the power tool;
receiving, by an electronic controller of the power tool, the sensor data, the electronic controller including an electronic processor and a memory, wherein the memory includes a machine learning control program for execution by the electronic processor;
processing the sensor data using a machine learning control program of the electronic controller;
generating, using the machine learning control program, an output based on the sensor data, wherein the output indicates a fastening value associated with a fastening operation of the power tool, wherein the fastening value indicates a level of seating of the fastener, wherein the level represents a depth that the fastener is seated within a workpiece; and
controlling, by the electronic controller, a motor of the power tool based on the output.
7. The method of claim 6 , wherein the machine learning control program is generated on an external system device based on exemplary sensor data and associated outputs, and is received by the power tool from the external system device.
8. The method of claim 7 , wherein the machine learning control program is one of a static machine learning control program and a trained machine learning control program.
9. The method of claim 7 , further comprising reducing a speed of the motor based on the fastening value indicating that the fastener is approaching a target fastening torque.
10. The method of claim 7 , further comprising stopping the motor based on the fastening value indicating that the fastener is torqued to a target fastening torque.
11. The method of claim 7 , wherein the operational parameters include one or more of a number of rotations, a measured torque, a characteristic speed, a voltage of the power tool, a current of the power tool, a power of the power tool, a selected operating mode, a fluid temperature, and tool movement information.
12. The method of claim 11 , wherein the power tool includes a gyroscope configured to provide data indicative of a tool movement.
13. A power tool comprising:
a housing;
a motor supported by the housing;
a sensor supported by the housing and configured to generate sensor data indicative of an operational parameter of the power tool;
an electronic controller, the electronic controller including an electronic processor and a memory, the memory including a machine learning control program for execution by the electronic processor, the electronic controller configured to:
receive the sensor data,
process the sensor data using the machine learning control program,
generate, using the machine learning control program, an output based on the sensor data and a confidence level associated with the output, the output associated with one or more operational functions of the power tool, and
control a speed of the motor based on the generated output and the confidence level associated with the generated output,
wherein the machine learning control program is configured to process the sensor data using a neural network.
14. The power tool of claim 13 , wherein the one or more operational functions include a torquing operation.
15. The power tool of claim 13 , wherein the electronic controller is configured to reduce the speed of the motor based on the sensor data indicating that a fastener is approaching a specified torque value.
16. The power tool of claim 13 , wherein the neural network is configured to generate multiple outputs corresponding to a desired speed of the motor.
17. The power tool of claim 13 , wherein the machine learning control program is generated on an external system device through training based on exemplary sensor data and associated outputs, and is received by the power tool from the external system device.
18. The power tool of claim 13 , wherein the machine learning control program is one of a static machine learning control program, and a trained machine learning control program.
19. The power tool of claim 1 , wherein the sensor data includes torque data associated with the fastener.
20. The power tool of claim 1 , wherein the level is a value of a range of values, wherein a lowest value in the range of values is associated with 0 percent indicating that the fastener has not started seating and a highest value in the range of values is associated with 100 percent indicating that the fastener is fully seated.