How to prevent potential losses resulting from the need to withdraw entire batches of defective products to the market and downtime on production lines? Investing in the development of quality control systems is crucial!
Continuous control of the correct operation of individual components of the production lines ensures that the products that reach customers are properly made and meet their expectations.A well-planned quality control system has a positive impact on the image of each company, customers are more satisfied because they do not have to advertise the product and will be more willing to use our offer in the future.
As a result of technological progress and the creation of new solutions within Industry 4.0, traditional quality control is currently being replaced by computers. Human capabilities related to quality assessment are much smaller than those of computers with software based on specialized control algorithms. Traditional quality control is also associated with the work of large teams, which ultimately makes it more expensive and vulnerable to human error.
The breakthrough turns out to be artificial intelligence, on which the quality control of an increasing number of companies that focus on production is based. What is the effect compared to the traditional approach to this process? AI-based solutions are faster, more profitable in the long run, also by reducing the number of errors made.
Artificial intelligence allows you to control production and quality processes, and technologies that can be used to create a quality control network in the company include: IoT sensors and computer vision.
IoT sensors placed on the device at a given stage of production, thanks to the Internet connection, are able to share measurement data from this device in real time. So what can be measured? Temperature, pressure, vibration level, humidity, etc.. Virtually all measurable data that are important in the production process and have an impact on the final form of the final product can be tested. Thanks to these sensors, it is possible to react immediately to various types of failures and irregularities.
Computer vision allows you to analyze the AI image provided from the camera placed in the production by comparing it with the created algorithm and determining the quality of the products
Quality control using AI in a company producing LED lamps
(system Comcore SENSORS)
Positive test

Negative test (resulting from intentionally covering the light intensity measurement sensor)

cost reduction
elimination of the human factor
production safety and maintaining quality parameters
real-time process preview and data reports
the ability to react on an ongoing basis
access to measurement history and analysis
ability to predict equipment wear and early response
Quality control is the foundation of quality assurance in industry. Properly conducted, it collects the data needed to modernize and increase the profitability of the company as well as to correct and avoid production errors.
AI is the future, and implementing it in your business will soon be the standard.
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