Energy saving: interventions in industry to reduce costs
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A fundamental process for any production company is quality control.
It becomes essential to verify whether or not a product meets the necessary quality requirements and safety standards.
Over the years, quality control has adopted several reliable frameworks that companies use to improve the production line.
Today, more and more companies rely on advanced technologies that help them implement quality best practices.
Artificial intelligence, data driven technologies and computer vision make Quality Control performance more profitable.
The primary goal of quality control is precisely to track down and make changes to any critical issues that may arise.
Production, therefore, must comply with quality standards and generate a final product in line with customer expectations.
The distribution of products on the market must in fact satisfy the pre-established reference quality requirements as well as integrity.
Quality control is carried out through inspections and tests, analyzing a range of activities that span from supplier selection to final production.
A significant competitive factor, since it analyzes the entire organizational and production flow and the challenges regarding process optimization.
In fact, it is a process made up of small but continuous improvements, aimed at achieving an excellent level of quality.
Automatic visual inspection and data analysis
In a production process, inspection, verification and quality analysis are fundamental steps to ensure an intact product.
In many situations, quality control is still carried out with a traditional approach, with the supervision of a human operator.
However, companies must face a series of critical issues due to long timescales, fixed costs and a high risk of human errors.
Artificial Intelligence (AI) offers support through Visual Inspection tools to improve production processes.
These tools allow you to analyze product images, detect defects and quickly complete checks.
The risk of making mistakes drops dramatically. This increases independence from the operator and the possibility of historicizing data and inserting it into a dedicated database.
Data becomes essential, a history allows you to "record" a certain behavior and recognize it even if not programmed to do so.
In fact, machine learning allows the recognition of patterns in data so that more accurate predictions can be made.
Furthermore, by measuring data, it is possible to determine the need for maintenance intervention, without any human inspection.
Thanks to advanced technologies, it is possible to measure the parameters of the machinery and program corrective activities on the critical points of the assets.
Identifying critical variations allows you to safeguard the life cycle of the machinery, but also allow for more efficient production.
Other key aspects of using AI
Reducing the margins of errors and improving productivity is possible thanks to process automation and AI.
In terms of efficiency and precision, the resolution of imperceptible errors that impact work activities can find an answer in the automation of operations.
It refers to a series of certain technologies in order to automate processes and activities that are generally manual.
The final goal is certainly to successfully create a compliant and competitive product on the market.
Improving your quality control systems is possible thanks to various technological and organizational tools.
To be competitive, it is essential to analyze the critical factors and identify adequate systems to manage operations and improvement interventions.