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Automated inspection systems help to improve the quality of parts and products. With few experienced CMM operators in the field, manufacturers are progressively turning to automated quality control solutions to not only stop production bottlenecks but also to boost the quality of the parts being inspected.
This white paper explores how the automation of operations such as quality inspection and metrology – previously reserved for larger companies with hefty budgets – has changed dramatically with the advent of collaborative robots.
Vision guided robotics (VGR) is an automation technology well-recognized for enabling greater flexibility and higher productivity in a diverse set of manufacturing tasks over a wide range of industries.
Several critical components need to come together to form a machine vision system. This includes the sensor (typically within a camera) that captures a picture for inspection, the processing hardware (a PC or vision appliance) and software algorithms to render and communicate the results. In addition, lighting, staging, and lenses are required to set up a machine vision system.
As developments in machine learning and the Internet of Things (IoT) impact how manufacturers run their businesses, automation can support these changes and boost productivity.
Machine vision processes have become standard practice in quality assurance. Inspecting reflective surfaces, however, presents a challenge. A technology known as deflectometry can be used to reliably detect all types of defect even in these circumstances.
Mold and tool makers are under pressure to reduce their throughput times and unit costs in order to stay in business. How much of a competitive edge do companies enjoy with a zero-point clamping system and a measuring machine? The CEO of WESCHU GmbH ran the numbers.
Russ Hudyma, Chief Technology Officer with Navitar, discusses the benefits of precision lens-to-sensor active alignment within the field of machine vision for high-end inspection.