Machine Vision in Smart Factories

Factory automation

Machine vision will be critical to delivering many of the advanced capabilities associated with Industry 4.0

Machine vision is an essential element of automation and will be critical to the creation of Industry 4.0's smart factories. No other aspect of the production line captures more information or is more valuable than machine vision in assessing products and finding defects, as well as in collecting data to direct the operation and optimize the productivity of robots and other equipment. Unlike simple sensors, vision sensors generate large amounts of image data, intensifying their utility in an Industry 4.0 environment

Whitepaper: Introduction to Machine Vision Whitepaper: Introduction to Industrial Barcode Reading

Cognex's analysis of the marketplace predicts a number of exciting developments associated with Industry 4.0 which will affect automation, quality, and productivity.


Self-learning Equipment
Automation line equipment will provide increasing levels of self-learning, self-controlling and self-optimizing capabilities to quickly and accurately complete complex tasks with minimal intervention. This type of capability is available today in some innovative devices in conjunction with operator guidance but will become far more autonomous with the advent of Industry 4.0.

Optimization to Production Lines
Manufacturers will enjoy greater flexibility as robots, machine vision systems, raw materials inputs and other aspects of the production line are able to communicate with each other directly. Benefits may include the ability to produce a wider range of component types on a single line, or manufacture smaller lots of particular products—even customized products—more cost effectively.

Big Data
Industry 4.0 capabilities will transform raw data into actionable information and insights that can drive real, measurable performance improvements. It will do this through the analysis of "Big Data" acquired during the manufacturing process and enabled by cloud computing and deep learning methods to uncover trends for process improvement. In many cases today, data may be collected but remain disconnected and silo-ed. In the future, this data will be shared and used proactively, often led by data-rich technologies such as machine vision.


Communication of Production Data
Manufacturing systems' communication networks will scan for input from the marketplace and use this information to fine-tune production parameters. For example, a pharmaceutical company's systems—alerted by higher than expected allergy cases reported in a particular region—could trigger manufacturing lines to increase production and place additional orders of raw materials.

Cloud Computing
Instant access to data and operations information via the cloud and mobile devices will connect workers to critical and time-sensitive information like downtime notifications, details on out-of-spec production abnormalities, maintenance needs and other critical issues requiring on-site intervention. This will allow personnel to monitor trends, analyze data and intercede from anywhere. Manufacturers will also be able to export information about the performance of their most successful production lines across their operations via the cloud, equalizing all lines at "best practice" levels.

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