IP Library › Granted Patent US 12,402,565
Granted Patent B2
US 12,402,565 · App. 17/532,166 · Granted Sep 2, 2025

Dynamically operated concave threshing bar

Inventor: Brian G. Robertson (Frisco, TX)
A01F12/28A01D41/127A01F12/181A01F12/26
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Quick Facts
Patent No.
US 12,402,565
App. No.
17/532,166
Granted
Sep 2, 2025
Kind
B2
Abstract

A dynamically operated concave threshing bar system, method, and apparatus wherein one or more threshing bars within a concave can dynamically move to various positions in real-time based on one or more conditions such as the type crop being harvested and on a determination by a combine harvester's computerized system, artificial intelligence (AI) system, or upon the operators input, among others. The concave can include a concave frame having a pair of arcuate side members, a threshing bar, and an actuator coupled to the threshing bar, wherein the actuator can be configured to move the threshing bar along the arcuate side members of the concave frame.

Claims (9)

1. A method of threshing crop in a combine harvester, comprising:

receiving one or more commands to operate one or more actuators in connection with a plurality of threshing bars, wherein the threshing bars are disposed along a pair of arcuate side members of a concave frame;

adjusting, via the actuators, a position of the threshing bars; and

moving, via the actuators, the threshing bars along the length or arc of the arcuate side members of the concave frame to a first position.

2. The method of claim 1 , further comprising receiving the one or more commands at a controller.

3. The method of claim 2 , further comprising operating the controller wirelessly or via a wired connection.

4. The method of claim 2 , further comprising operating the controller via a central computing system.

5. The method of claim 2 , further comprising operating the controller based on data from an artificial intelligence system, neural network, or a machine learning algorithm.

6. The method of claim 5 , wherein the artificial intelligence system, neural network, or a machine learning algorithm receives one or more of the following conditions: crop conditions, harvest conditions, soil conditions, crop characteristics, crop type, crop variety, crop yield, crop moisture, crop test weight, crop protein, crop starch, crop oil, crop volume, crop grade, bulk density, stalk moisture, leaf moisture, combine productivity, engine revolutions per minute (RPM), engine usage, horsepower consumption, fuel consumption, concave clearance, concave pressure, rotor speed, rotor type, pinch point, threshing angle, rotor elements, gear ratio, gear position, power band, sieve loss, shoe loss, separator loss, cleaning loss, separator clearance, grain loss, grain divider position, loss sensitivity, threshing efficiency, separation efficiency, ground speed, elevator speed, elevator throughput, separator pressure, fan speed, feed accelerator speed, cross auger position, drum speed, drum position, paddle speed, paddle position, drum position, auger speed, feeder house position, feederhouse throughput, gathering chain speed, header speed, header width, header cut height, header angle, header throughput, header loss, cutting efficiency, cutter bar position, cutterbar length, deck plate position, stripper plate gap, reel speed, combine angle, pre-sieve position, sieve angle, sieve position, chaffer position, grain distribution, grain throughput, bushels per hour, tailings, clean grain, clean grain foreign material, grain damage, whole grain, grain damage position, broken grain, skinned grain, broken grain and foreign material, broken cobs, stress cracks, fines, dockage, straw quality, grain tank sample, grain size, foreign material, threshed grain, unthreshed grain, grain return, storability, threshing effectiveness, separation effectiveness, operational or performance parameters, operational or performance benchmarks, operational or performance goals, operational or performance priorities, operational or performance sensitivity, weather data, soil data, historical data, fleet information, or global positioning system (GPS) information or historical data from previous harvests or fleet information used to predict variances in yield rates at a particular GPS location.

Continuity (13)
Continuation 17099601 · Nov 16, 2020
Continuation In Part 29754179 · Oct 7, 2020
Continuation In Part 17008430 · Aug 31, 2020
Continuation In Part 16860845 · Apr 28, 2020
Continuation In Part 16826194 · Mar 21, 2020
Continuation In Part 29696475 · Jun 27, 2019
Continuation In Part 29680208 · Feb 14, 2019
Continuation In Part 29670114 · Nov 13, 2018
Continuation 16115331 · Aug 28, 2018
Continuation In Part 15856381 · Dec 28, 2017
Continuation In Part 15856402 · Dec 28, 2017
Provisional Application 62821570 · Mar 21, 2019
Related Publication 20220071097A1 · Mar 10, 2022
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