When approaching the construction of a single product, it's easy to take the time to use the finest materials, measure carefully, align pieces, and make necessary adjustments to provide the best result. Carpenters choose their preferred wood, work with the grain, and avoid machine jolting knots. Medical instruments are painstakingly honed and tested to ensure a tool that will perform reliably when it really matters. Researchers spend countless hours gathering samples and categorizing them with an eye toward specific qualifications before they can begin their assessments. While these models have the highest levels of quality in lower quantities, human error is still a factor and the margin of accuracy decreases as demand for products and results take precedence.
Machine Vision solutions today have two distinct branches – Traditional Machine Vision and Machine Learning (neural network or artificial intelligence) approach. Both branches have developed tremendously in recent years through advancement in both camera and computing technologies.
In a traditional Machine Vision approach, engineers will use a camera and lighting system to acquire an image that clearly identifies the desired features to be analyzed. Software based processing algorithms further enhance these features and image measurement tools classify or inspect the features accordingly. These systems can be implemented reliably, however, for some challenging applications, they may not perform as well as the human eye.
For those applications, Machine Learning may be more appropriate. In such a system, the algorithm is “trained” by feeding it pre-classified images of known good products and bad products with known flaws. The computer now has a guideline for classifying new images correctly. At this point, the limitations are only those of the software available. There are a number of machine learning tools available for image processing, with different levels of algorithm transparency and adjustable features.
David Chargin, Project Manager & Senior Automation Engineer for Fraunhofer USA Center for Manufacturing Innovation CMI, notes that in some applications, both techniques can be combined in a hybrid approach, with a traditional machine vision system pre-processing images to highlight relevant details before handing them over to a machine learning algorithm.
Past machine vision applications at Fraunhofer USA CMI include: