A method handles a workpiece with a removal tool. The method includes: generating at least one parameter for the removal tool; determining at least one characteristic variable from the at least one parameter and at least one item of workpiece information; creating a removal prediction taking into account the characteristic variable and at least one removal result from a removal result store; assessing the removal prediction; removing the workpiece in the event of a positive removal prediction; detecting the removal result; and storing the removal result in the removal result store.

A method handles a workpiece with a removal tool. The method includes: generating at least one parameter for the removal tool; determining at least one characteristic variable from the at least one parameter and at least one item of workpiece information; creating a removal prediction taking into account the characteristic variable and at least one removal result from a removal result store; assessing the removal prediction; removing the workpiece in the event of a positive removal prediction; detecting the removal result; and storing the removal result in the removal result store.

Systems and methods for learning software assisted, fixtureless object pickup and placement include a machine vision system to scan a part for pickup to measure target points at said part, retrieves weighted desired target points for the part from a controller, and performs an iterative analysis to determine a best fit solution for moving the part within a predetermined range of the desired target points. The best fit solution being determined by fitting vectors between each of the desired target points and the measured target points to develop a solution set. The best fit solution having the overall shortest length when said vectors are summed while prioritizing relatively higher weighted desired target points.

A material handling system comprising one or more sub-systems and one or more sensing panels is provided. Each sensing panel may determine whether a mode control token is within a vicinity of the respective sensing panel. When the mode control token is within the vicinity of the respective sensing panel, the sensing panel may generate a signal, and when the mode is outside of the vicinity of the respective sensing panel, the generation of the signal is halted. The material handling system may further include a processor that enables at least one of the one or more sub-systems of the material handling system in response to the generated signal and disables each of the one or more sub-systems of the material handling system in response to a halting of the generation of the signal.

A material handling system comprising one or more sub-systems and one or more sensing panels is provided. Each sensing panel may determine whether a mode control token is within a vicinity of the respective sensing panel. When the mode control token is within the vicinity of the respective sensing panel, the sensing panel may generate a signal, and when the mode is outside of the vicinity of the respective sensing panel, the generation of the signal is halted. The material handling system may further include a processor that enables at least one of the one or more sub-systems of the material handling system in response to the generated signal and disables each of the one or more sub-systems of the material handling system in response to a halting of the generation of the signal.

A material handling system comprising one or more sub-systems and one or more sensing panels is provided. Each sensing panel may determine whether a mode control token is within a vicinity of the respective sensing panel. When the mode control token is within the vicinity of the respective sensing panel, the sensing panel may generate a signal, and when the mode is outside of the vicinity of the respective sensing panel, the generation of the signal is halted. The material handling system may further include a processor that enables at least one of the one or more sub-systems of the material handling system in response to the generated signal and disables each of the one or more sub-systems of the material handling system in response to a halting of the generation of the signal.

Systems and methods for learning software assisted, fixtureless object pickup and placement include a machine vision system to scan a part for pickup to measure target points at said part, retrieves weighted desired target points for the part from a controller, and performs an iterative analysis to determine a best fit solution for moving the part within a predetermined range of the desired target points. The best fit solution being determined by fitting vectors between each of the desired target points and the measured target points to develop a solution set. The best fit solution having the overall shortest length when said vectors are summed while prioritizing relatively higher weighted desired target points.

A system for machining semi-rigid curved panels includes a fixturing device within an enclosure cell, the cell having a sealable entrance and a sealable exit. A holding table, moveable through the sealable entrance between the inside and outside of the cell, is configured to receive unfinished semi-rigid curved panels. Another holding table, fixed outside of the cell, is configured to receive processed semi-rigid curved panels. A first robot inside the cell transports the semi-rigid curved panels from the first holding table and onto the fixturing device, later transporting the curved panels from the fixturing device onto the second holding table through the sealable exit. A second robot processes the semi-rigid curved panels on the fixturing device. A controller is configured to sequentially actuate first and second robots for transporting and processing the semi-rigid curved panels, and to manage a vacuum clamping system for workpiece retention on the fixturing device.

Systems and methods for learning software assisted, fixtureless object pickup and placement include a controller which derives measured target points from image(s) of a part in a work area received from a machine vision system, performs an iterative analysis to determine a best fit solution for moving the part adjacent to another part for forming the assembly such that each of the measured target points is within a predetermined range of an associated one of desired target points for said part, where the other part is located at support object(s). The controller converts the measured target points to a common reference frame and command movement of the part, by way of the robot, according to said best fit solution as converted such that the part is moved and secured adjacent to the other part in a fixtureless manner.

A sorting method automatically sorts workpieces produced in a flatbed machine tool according to a scheduled sorting process by a sorting device. The method includes: providing at least one piece of process information created as a result of manufacturing of the workpieces; analyzing the at least one piece of process information, such that it is determined whether there is a sorting disruption; and upon determining that there is the sorting disruption, automatically modifying the sorting process.