For parts that arrive loose, in random poses, in several specifications or hard to arrange with a fixed fixture: a vision system recognizes position, direction, pose and the graspable area first, and a robot then picks and feeds the part automatically.

Ordinary automatic feeding relies on a fixed pick position and direction, normally guaranteed by a tray, a chute or a vibratory bowl. Flexible feeding starts from the fact that the part has no fixed position or direction, so each cycle has to judge which part to pick, where, and in what pose.

Parts whose arrangement is not fixed, whose direction is not fixed, that come in more than one specification, or whose pose has to be judged. Where parts can be arranged in order and located accurately, mechanical locating is usually simpler and more reliable.

It can, provided vision can recognize the part, the robot can reach it and the gripper can hold it. How loose the parts are, how much they occlude each other and where they can be gripped are confirmed during the part test.

Part of the time. It depends on how distinct the part features are, whether the lighting can be controlled and how much occlusion there is. Symmetrical parts, parts with similar directions and parts without feature surfaces are noticeably harder for pose judgement.

Where the differences are concentrated in size and appearance, vision classification and program switching usually cover them; where the differences fall on the gripping method, a gripper change or zoned feeding is usually needed. The exact boundary follows the part test.

The target specification can be distinguished by features, but it needs enough feature difference and stable imaging. The more mixed the parts are, the more the configuration depends on the classification logic and the zoning of the feeding end.

2D gives the plane position, the angle and outline features; 3D adds height, depth, spatial pose and stacking information. Which one to use is assessed from the part form, the stacking, the occlusion and the picking requirement.

No. The executing end can also be a gantry or a dedicated mechanism. But picking from a loose state normally needs a motion mechanism that can reach any graspable position in the plane or in space.

No. For parts lying flat with little occlusion, 2D is often enough; 3D is considered where height, spatial pose or deeper stacking information is needed.

No. The feeding end can be a vibratory bowl, a flexible vibratory plate, a conveyor, a tray, a box or a loose platform, depending on the incoming state, the part condition and the space available.

Because imaging and gripping depend on material, surface, reflection, color, size, weight, stacking and the graspable area, none of which can be inferred from a part name. The test is the main basis for judging feasibility.

They can, but reflection is the main difficulty and is normally handled through the lighting scheme and the camera angle. Machined surfaces and blank surfaces image very differently, so the real part has to be confirmed.

They can, but black material absorbs light and gives low contrast, so a dedicated lighting and exposure scheme is needed. Whether recognition is stable still follows the test on the real part.

Stacking brings occlusion and interference, which is harder than parts lying in a single layer. The common answers are to improve the stack condition with the feeding mechanism, or to use a vision scheme that carries height information.

Recognition is only the first step. A successful pick also depends on how well the gripper matches the part surface, the approach pose, the gripping position and whether the part is stable. These are verified together during the test.

Vision usually outputs the target coordinate, pose and pick point, which is converted into the robot coordinate system by hand-eye calibration and sent to the robot. The trigger method and the communication protocol are settled per project.

The loading result triggers the machine's ready signal, or a PLC or master controller sequences the feeding, the robot and the machine. The interface method is confirmed against the actual equipment.

Yes. Vision handles the recognition and picking of loose blanks; multi-face machining and second clamping on the machining center are handled by the machining center automation.

Yes. Vision handles the recognition and picking of loose blanks; chuck loading and unloading on the lathe is handled by lathe automation.

It usually involves the vision parameters, the robot program, the gripper and the feeding mechanism. Which steps can run automatically and which need a person depends on how much recipe management the system has, and is designed per project.

It is made up of image capture and recognition, approach, grip, carry, place, return and waiting for confirmation, and the effective pick rate in a loose bin has to be taken into account. No fixed number is given here; the items are measured during the part test.

Five groups: part information, incoming and stacking, picking and placing, production and machines, and system and interfaces. The detailed list is in the data section of this page.