Industrial robots work quickly and precisely to complete tasks like painting, welding, assembly, and product inspection. They work dependably and without getting bored when performing repetitive tasks, unlike humans, which results in high production at a low cost. Industrial robots are very useful to manufacturers across numerous industries thanks to their robot spare parts characteristics.
Some industrial robots do routine tasks in a consistent manner, such as in standard “pick and place” applications. Programmed routines that specify the direction, velocity, acceleration, deceleration, and distance of a series of coordinated movements are what lead to these actions.
To carry out sophisticated duties like weld inspection and optimization in the automotive industry, other robots use machine vision systems. These typically entail complex movements and behaviours, for which the robot may even be required to self-identify.
High-resolution cameras are paired with potent image processing software in machine vision systems. They are easy to handle and manage and maintain their functionality even in tough manufacturing environments. Even in unfavourable environmental conditions, machine vision systems provide seamless production without user intervention or oversight.
There are many uses for machine vision in industrial automation:
Robot Vision in 2D
Line-scan or area-scan cameras are used by 2D vision systems to take photographs that have width and length but no depth. They may measure an object’s visual properties and provide information about its position, rotational orientation, and type to robotic handling systems by analysing these images.
2D vision systems are used by the automobile sector to remove large gearboxes from cages, remove cylinder heads from wire mesh containers, recognise axle castings, and locate slide bearing shells.
3D Position Detection Automated
Using specialised cameras and lasers, 3D vision systems can determine the position and shape of an item in three dimensions. They establish a component’s starting point, overall length, and rotation and provide this information to industrial robots for quick and effective handling. The automatic, reliable handling of objects of various sizes is made possible by 3D vision systems.
The manufacture of crankshaft castings for the automotive industry is a typical application for 3D vision systems, where they direct robots to place castings in readiness for the following step of assembly.
Any manufacturing process needs to assemble parts properly. Ineffectively put together components result in defective, dangerous products. Robots are programmed to remove defective items from the manufacturing line via machine vision systems equipped with quick, fixed focus cameras and LED illumination. These systems continuously inspect parts during assembly to confirm the presence of distinguishing features.
Screws, pins, fuses, and other electrical parts are distinguishing features. In order to ensure proper assembly, machine vision systems also look for any holes or slots that are missing. Even with a wide range of different parts, inspection only takes a few seconds, which enables producers to keep productivity and efficiency at a high level.
There are several uses for machine vision systems for assembly inspection. These include inspecting automobile parts, making sure labels are placed correctly on boxes, and checking the fill levels of blisters, chocolate trays, and powder compacts.
High-resolution cameras and 3D sensors are used by machine vision systems for contour inspection to look for deviations (like chips) that could impair the product’s shape and functionality. Additionally, they verify that measures like length, width, and radius fall within predetermined limits.
Injection needles are rendered useless if they are blunt or deformed, thus pharmaceutical companies use machine vision systems in automated production lines to inspect them. As the needles move through the system on powered conveyors, several cameras capture their images. In order to assess the needle’s sharpness and verify the tube’s contour, sophisticated computer software analyses the photos that were taken. This knowledge is used by industrial robots to sort and get rid of defective needles.
Due of their size, injection needles are nearly impossible to examine with the naked eye. The production process can be sped up and expenses reduced by using machine vision systems to inspect 40 needles per minute with 100% correctness. Other contour inspection uses include the measuring of coating structures on capacitor foils, concentricity checks of spark plugs for gasoline engines, and saw blade teeth examination.
Seam Inspection in 3D
Poorly welded parts crack, which leads to the failure of products. When it comes to cars and aeroplanes, this frequently has tragic results and claims lives. In many industries, robotic weld seam inspection and optimization is becoming the norm.
Robotic arms with sensors attached on them are part of machine vision systems for weld inspection. Laser triangulation is a process where a laser in the sensor projects a line of light across the surface of a component joint. A high-speed camera that is also built within the sensor records an elevation profile of the line at the same time. The technology creates a 3D image of the welded seam surface using the relative motion of the component and sensor.
A computer uses this image to evaluate the uniformity of the seam over its whole length. It precisely recognises flaws like pores and profile variances, which weaken the joint, and sends commands to a robotic burner to rework or repair seams as necessary.
Machine vision systems enable easy component tracing by storing inspection results and serial numbers in a database. They work quickly and on several seams of various types, sizes, and forms. To ensure that vehicles are of the highest calibre and are safe to drive, the automobile industry heavily relies on automated weld inspection and optimization systems.
Systems that use machine vision have several uses in industrial automation. They help businesses to achieve previously unattainable levels of production and efficiency while enabling industrial robots to carry out complicated operations with accuracy and reliability. Over the past ten years, machine vision has advanced tremendously and is now indispensable to many businesses.