From accommodating flexibility to minimizing overdetection, the latest AI algorithms have the necessary capabilities to match the sensibility and expertise of professional human inspectors.
Due to rising labor costs and a shortage of skilled inspectors, today’s manufacturers are facing intense pressure to automate processes that rely on the senses of experienced human workers.
When an engineer begins the process of specifying a new machine vision system, they will often think very carefully about the line speed, the optics, and the image processing software.
Systems integration is the process of bringing together diverse and disparate components and sub-systems and making them function as a single unified system.
You’ve learned about light sources, lenses, cameras, camera interfaces, and image processing software. Now, you may be wondering exactly how to design and implement a complete, successful machine vision system.
The future of quality inspection is one that will see quality professionals working side-by-side with collaborative robots fitted with easily-swapped vision systems.
Over the past decade manufacturers have increasingly turned to flexible, customizable automation platforms to meet the demands of high mix/low volume orders and ensure their long-term survival in a competitive manufacturing environment.
In the world of machine vision, as in any tech field, there is a distinct divide between hardware and software. The hardware includes components of machine imaging systems such as the physical camera, lensing, cable interfaces, the PC or processor, etc. and are defined by rigid specifications (i.e. resolution of a camera, processing power, bandwidth of interface).