Machine vision is useful for all industrial and non-industrial applications. A combination of hardware and software provides operational guidance to devices in executing their functions based on capturing and processing images.
Machine vision systems use digital sensors inside industrial cameras with specialized optics to acquire images to process, analyze, and measure various decision-making characteristics of parts. Because of its speed, accuracy, and repeatability, machine vision excels at quantitative measurement of a structured scene, whereas human vision excels at a qualitative interpretation of a complex, unstructured scene.
For example, machine vision systems can inspect hundreds, if not thousands, of parts per minute on a production line. Machine vision systems with the proper camera resolution and optics can easily inspect object details too small for the human eye to see. This post will explore some of the key industrial applications of machine vision.
Guidance is essential for a variety of reasons. Machine vision systems can locate a part’s position and orientation, compare it to tolerance, and ensure it’s at the proper angle to ensure proper assembly. The location and orientation of a part can then be reported to a machine controller or robot, allowing the robot to locate and align the part of the machine. Machine vision-powered guidance achieves far greater speed and accuracy in arranging parts on or off pallets, finding and aligning parts with other components, packaging parts off a conveyor belt, placing parts on a shelf, or removing parts from bins manual positioning.
Alignment to other machine vision tools can also be done with guidance. This is a powerful feature of machine vision because parts may be presented to the camera in unknown orientations during production. Machine vision enables automatic tool fixturing by locating the part and aligning the other machine vision tools. This entails locating key features on a part to precisely position calipers, blobs, edges, and other vision software tools to interact with the part correctly. This method allows manufacturers to produce multiple products on the same line and eliminates costly hard tooling to keep parts in place during the inspection.
Geometric pattern matching is sometimes required for guidance. Pattern matching tools should tolerate large variations in contrast and lighting and changes in scale, rotation, and other factors to find the part reliably. Pattern matching provides location information that allows other machine vision software tools to align.
A machine vision system can read barcodes (1D), data matrix codes (2D), direct part marks (DPM), and characters printed on parts, labels, and packages for part identification and recognition. An optical character recognition (OCR) system reads alphanumeric characters, whereas an optical character verification (OCV) system verifies that a character string exists. Machine vision systems can also recognize parts by looking for a specific pattern or identifying items based on color, shape, or size.
DPM applications write a code or a string of characters directly on the part. Error-proofing, enabling efficient containment strategies, monitoring process control and quality control metrics, and quantifying problematic areas in a plant, such as bottlenecks, are all common uses for this technique among manufacturers across all industries. Direct part marking improves asset tracking and verification of part authenticity. Documenting the genealogy of the parts that make up the finished product also provides unit-level data to drive superior tech support and warranty repair service.
For retail checkout and inventory control, traditional barcodes have gained widespread acceptance. On the other hand, Traceability information necessitates more information than a standard barcode can accommodate. Companies developed 2D codes, such as Data Matrix, to increase data capacity. These codes can store more information, such as the manufacturer, lot number, product identification, and even a unique serial number for virtually any finished good.
Gauging is done using a machine vision system that calculates the distances between two or more points/geometrical locations on an object and determines if the measurements are accurate. If this is not the case, the vision system sends a fail signal to the machine controller, activating a reject mechanism that ejects the object from the line. In practice, a fixed-mount camera captures images of parts as they pass through the camera’s field of view, and the system calculates distances between various points in the image using the software. Because many machine vision systems can measure object features within 0.0254 millimeters, they can replace contact gauging in various applications.
A machine vision system detects defects, contaminants, functional flaws, and other irregularities in manufactured products as part of the inspection process. Inspecting medicine tablets for flaws, displays to verify icons or pixel presence, and touch screens to measure backlight contrast are just a few examples. Machine vision systems can also inspect products for completeness, such as checking caps, safety seals, and rings on bottles in the food and pharmaceutical industries and ensuring a match between product and package in the food and pharmaceutical industries.