Coordinate Measuring Machine
One example is the need to validate the dimensions of a part coming out of a computer numerical control (CNC) machine. The most common system used is a coordinate measuring machine (CMM). In this case, the system touches different surfaces on the part to measure it. Designers usually want CMM machines with an accuracy of approximately 0.25 mm. To reach this level of accuracy with a vision system is currently difficult and probably overly expensive.
Assume that at least 3 pixels are needed to cover the accuracy value of 0.25 mm, and the system has a 150 mm × 150 mm field of view, which is large enough to cover the part. Approximately an 1800 × 1800-pixel camera is needed to do the job. And this does not consider any lighting challenges that may come with the images of shiny metal objects coming out of a CNC machine.
This technique can be used to detect a part's position and its defects. Due to its high sensitivity, it will be able to detect small cracks, deformities, pits, etc. However, using eddy currents is limited to conductive materials, whereas using a vision system is not.
Vision will be the best option when products on a production line are coming out on a conveyor and the system simply needs to check part presence and do some measurement analysis without extreme precision. The camera can take a snapshot of the part as it is moving on the conveyor, allowing for live quality checks on the part without having to remove it from the production process. This will eliminate the need to do batch sample testing. The following list of vision functions can help determine if vision is the best option for a quality inspection process.
Pattern Matching: System operators teach a few good parts and a few bad parts to the camera. The camera will set a pass/fail threshold. Then, when taking an image of a new part, it will indicate if this part matches the threshold that's been set. This is often used when it's necessary to check a part for deformities or for the presence of a specific object on the part (e.g., holes in a part or a cap on a bottle).
Color Matching: Some vision systems can confirm the color of an object.
Optical Character Recognition (OCR): This is often used to confirm some printing on the product, making sure it is visible or written correctly and consistently with what was supposed to be printed.
Measuring: There are various measuring methods available in vision systems that can be used to do measurement checks using images. For example, measuring the distance between edges or the distance between holes.
Filters: A system can detect visual defects (pits, holes, cracks, etc.) using various filtering techniques like blob analysis.
Counting: This is a bit like measuring, but counts the number of objects or holes that are present on a part.
While vision systems can add tremendous value to collaborative robots, they are not always the most appropriate solution. Basic knowledge of machine vision provides insight on choosing the correct device for any given application. It can also help smooth the integration of simple vision capabilities into collaborative robotic systems.
This article was written by Amanda Lee, e-marketing coordinator for Robotiq, Saint-Nicolas, Quebec, Canada. To download the company's eBook, “Vision Systems for Collaborative Robots,” visit here.