Mechatronics Canada

May 18, 2022

 

MC Cognex In Sight 2800 Combines Deep Learning and Traditional Vision 1 400

Cognex Corporation, aleader in industrial machine vision, has released the In-Sight 2800 series vision system. In-Sight 2800 puts the power of a full-featured vision system into an easy-to-use package that gets applications running in minutes.

“It has never been easier to apply deep learning to a production line,” said Carl Gerst, Executive Vice President of Products, Platforms and Solutions. “The In-Sight 2800 system can be trained with just a few images to automate everything from simple pass/fail inspections to advanced classification and sorting - no PC or programming is needed.”

In-Sight’s EasyBuilder interface guides users through the application development process step-by-step, making it simple for even new vision users to set up any job. Experienced users will appreciate how In-Sight’s intuitive, point-and-click interface simplifies more complex application development and keeps operations moving fast.

The combination of deep learning and traditional vision tools gives users the flexibility to solve a broad range of inspection applications - operators simply select the tool designed to deliver the highest possible accuracy for their task. Tools can be used individually for simple jobs or chained together for more complex logic sequences.

The toolset also includes ViDi EL Classify. Using as few as five images, this powerful classifying tool can be trained to identify and sort defects into different categories and correctly identify parts with variation. The ability to classify by multiple features or characteristics allows users to solve more tasks with a single vision solution.

The new In-Sight 2800 system also offers a wide variety of accessories and field-changeable components to help users adapt quickly to changes such as new parts, faster line speeds and higher quality standards.

MC Cognex In Sight 2800 Combines Deep Learning and Traditional Vision 2 400

 

 

 

For more information about the In-Sight 2800, download the Product Guide by clicking here or visit the Product Page by clicking here 

 

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MC Omron Tradtional Factories have Data Needs Too 1 400

The right tools and tech can enable advanced predictive maintenance, bottleneck prevention and optimization. Find out how digitalization is bridging the gap between legacy systems and Industry 4.0.
 
Without a doubt, digitalization is setting high standards for efficiency and throughput in production. Today, as manufacturers move ever closer to Industry 4.0, the question has increasingly shifted from ‘if’ to ‘when’ production companies will be able to make use of the data being generated with every piece that comes off the line. How do traditional companies take the first steps to digitalization and put that data to work? How do they make the move to unlocking the potential in every production system to improve and optimise their output? And what is the simplest way to harness the power of cloud computing and artificial intelligence at the edge?
 
As we know, not every modern factory is a greenfield high-tech showroom with pristine workers in lab smocks. In fact, many of them may not even be all that modern, but that doesn’t mean they shouldn’t benefit from the modern tools, processes and best practices that are the hallmarks of a productive and successful factory. In this journey to modernity, digitalization has become a vital bridge, enabling owners and production directors to begin harvesting the data that can provide vital clues to enhancing production.
 
Start with Sensors
As new standards of efficiency and quality become the norm, those seeking a competitive edge are turning increasingly to digitalization. Sensors (from very simple to highly complex) can be incorporated into all types of production lines, to measure everything from unit temperature and speed to output shape and size, weight and hardness and most everything in between. Sensors can form part of a wireless network, sending signals remotely, or can be connected directly into an existing production system, depending on the needs of the factory.
 
From Raw Data to Vital Information
 
The resulting output from these sensors is the raw data that can be transformed into information and insights to streamline efficiency, remove bottlenecks, reduce downtime and optimize production cycles—when used correctly. Before any data can be analysed, it has to be stored, either on an in-house server or using a cloud-based service for greater scope for expansion and off-site processing. Exactly how much storage is needed depends on the application, level of digitalization, output form and required analysis. For comparison, a production line with simple sensors attached to monitor throughput will generate significantly less data than one with quality-control cameras monitoring multiple types of product forms.

 

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