The new face of quality-checks: Industry 4.0 Cognitive Visual Inspection

Numerous manufacturing industries are showing interest and are adopting the smart factory concept or we can call it the connected factory or Industry 4.0 roadmap to amplify their productivity and raised competitiveness. Each and every field of industry talk about asset tracking, predictive maintenance and OEE that are being greatly impacted by Industry 4.0. This is because of the tremendous addition of value by coupling the IT (Information Technology) with Operating Technology. Producers are now looking forward to track the utilization of their products by directly taking feedback from their customers in order to rectify their upcoming variants. And for the attainment of this, the comprehensive value chain has established a network of retailer, manufacturer, supplier, warehouse and the consumer.
The manufacturing process needs to be very steady in order to provision higher qualitative outcomes during every phase of the production. More than half of these quality tests incorporate visual inspections to make sure that all the parts are functional and are located at the destined location or is there any possible blemishes or all the colors are intact or is there any shape deformation. Many times due these defects get ignored during visual quality inspection and reaches the end customer, result of which is customer dissatisfaction and negative brand image in the market. As a measure, companies have to recall the products from the market and from end customers. This is not only a rigorous process but also an expensive one.
Leaving these inspections checks on the automated technology can be tough due to the variety in ranges of product and the tremendous amount of inspections.  There can be any possible complexity that directs towards any sort of defect of any size. So it becomes extreme necessity to deploy the newer and more happening cognitive visual inspection, which has a proven track record to deliver the peak value at its best.
The images that are being captured during the distinct phases of the assembly line are passed through the main learning service, which then stamps it with OK and Not OK part, based on the desired characteristics of the elements and parts..  If the product doesn’t qualify the requirements submitted in the program it will be automatically stamped as Not OK and vice versa. Every organization can train and deploy these systems as per their inspection criteria and can set up even the strictest of visual inspections parameter.
As these programs are built using advanced neural networks, these can be easily trained and can be deployed on any hardware that has been already configured. So it curtails the chances of getting latency during the live production phase and the overall production will commence once it has been set up

Wrap Up:

These systems can be easily trained by feeding them the defected images > then allocate the trained system to pre-configured hardware > Inspect the image > and lastly preview the outcomes.
A crosscheck from the human inspectors who can check the results shown by the system will amplify its abilities. This correction information is then fed to the system again for the betterment of the results and to attain desired outcomes. The repeated occurrence of a defect can then be traced back in the production process to identify and eradicate the source of causing that defect thereby preventing it from reoccurring. Such type of cognitive system is a deep necessity for all manufacturing businesses and significantly the ones who are manufacturing products in sectors like automotive, electronics and the ones who are using assembly lines in their companies.
There are some more added benefits of deploying cognitive visual inspection i.e.:
  • Reduction in Inspection time leading to faster time to market
  • The level of quality touches sky with no chances of human mistakes
  • Smoothens Inspection process flow, without much  human intervention
  • Increase in Brand Image
  • Improved Customer Satisfaction
  • Lesser Product Recalls

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