Automating Production Quality Control Using Machine Learning 

Modern industrial production is increasingly based on modern technologies that allow for increased efficiency and minimized errors. One of the key aspects of production management is quality control, the task of which is to identify defective products and prevent problems related to low quality. Traditional quality control methods are often based on manual inspection, statistical analysis of samples or classic vision systems. However, they are expensive, prone to human error and time-consuming. The solution that is revolutionizing quality control in the production plant is the use of machine learning (ML). ML algorithms allow for automatic detection of defects, analysis of quality trends and optimization of the entire inspection process. Automation of quality control in production can increase the precision of inspection, reduce costs and improve the overall efficiency of the production plant. 

Quality Control – the Foundation of Efficient Production 

Quality control is a set of activities aimed at ensuring that products meet specific standards and requirements. It covers a wide range of processes – from raw material inspection, through monitoring the production process, to testing of finished products. 

Quality control focuses on subjective evaluation of product characteristics, such as appearance or smell, while laboratory quality control uses advanced measurement tools to objectively evaluate technical parameters. Effective quality control in production is crucial to minimize losses, improve brand reputation and ensure customer satisfaction. 

In the context of modern production, implementing a quality management system is becoming not only desirable but also essential. These systems, often based on ISO standards, allow for standardization of processes, monitoring of key indicators and continuous improvement. A well-designed quality control plan defines procedures, responsibilities and acceptance criteria for each stage of production. 

Challenges of Traditional Quality Control Methods 

Despite their long history, traditional quality control methods, which rely heavily on visual inspection and manual measurement, face a number of challenges, including the following: 

• Subjectivity – visual assessment can be susceptible to human error and differences in interpretation, leading to inconsistent results. 

• Time-Consuming and Cost-Intensive – manual inspection of large batches of products is time-consuming and expensive, especially for complex or small components. 

• Limited Scale – traditional methods may be inadequate to monitor all aspects of the production process, especially in high-volume production. 

• Lack of Proactivity – traditional quality control focuses on detecting defects after they occur, rather than preventing them. 

These limitations lead to increased production costs, delivery delays, reduced product quality and potential reputational damage. As a result, there is a need for modern quality control tools that will enable automation, increased precision and proactive quality management. 

What is Quality Control Automation Using Machine Learning? 

Quality control automation is the process of using technologies such as robotics, vision systems, and analytical software to improve and modernize quality control processes. When combined with machine learning, computers are able to “learn” from data and experience, allowing them to independently detect defects, identify anomalies and predict potential quality problems. 

In practice, this means that a machine learning-based system analyzes huge amounts of data (e.g. product images, sensor data, production process parameters) and identifies patterns in them that indicate the occurrence of defects or deviations from the norm. Thanks to this, the system is able to automatically alert about problems and even take corrective actions, minimizing the risk of releasing defective products to the market. 

The process of implementing machine learning in quality control usually takes place in several stages: 

• Data Collection – the key element is to collect the right amount of data, including both correct products and those with defects. This data can include camera images, sensor data, measurements from control devices, and even data from Manufacturing Execution Systems (MES). 

• Data Preparation – the collected data is then cleaned, transformed, and divided into training and test sets. 

• Model Training – a machine learning algorithm (e.g., neural networks) is trained on the training set to learn to recognize patterns and relationships in the data. 

• Model Validation – once trained, the model is tested on the test set to assess its accuracy and effectiveness. 

• Model Deployment – the validated model is deployed to the production environment, where it continuously analyzes the data and identifies potential defects. 

Benefits of quality control automation using machine learning 

Implementing quality control automation using machine learning brings a number of benefits that translate into increased efficiency, reduced costs and improved product quality. The most important ones include the following: 

• Increased Efficiency and Speed – automatic quality control systems operate much faster and more efficiently than humans, which allows for testing more products in a shorter time. 

• Improved Accuracy and Precision – machine learning minimizes the risk of human error, ensuring more accurate and precise detection of defects, even the smallest and hard to notice ones. 

• Reduced Production Costs – quality control automation allows for reducing costs related to manual work, production waste and customer complaints. 

• Minimizing the risk of defective products – thanks to early detection of quality problems, quality control automation helps minimize the risk of releasing defective products to the market, which results in increased customer trust and company reputation. 

• Optimization of Production Processes – analysis of data collected by quality control systems allows for identification of bottlenecks in production processes and their optimization, which leads to increased efficiency and reduced costs. 

• Possibility of Analyzing Large Amounts of Data – machine learning allows for analysis of huge amounts of data, which allows for identification of trends and patterns that may be invisible to humans. Thanks to this, it is possible to predict potential quality problems and take preventive measures. 

• Greater Compliance with Norms and Standards – many industries have strict quality control standards that require precise documentation and analysis of production processes. ML systems enable automatic generation of quality and compliance reports, which facilitates audits and meeting industry standards. 

• Increased Company Competitiveness – companies that implement quality control automation gain a competitive advantage thanks to higher product quality, lower production costs and faster response to market needs. 

Although the manufacturing industry is still in the Industry 4.0 phase, which focuses on the cooperation of IT systems and machines, it is slowly moving towards the fifth industrial revolution (Industry 5.0), where humans will once again be at the center of the production process, and technology will support their activities and harmonious cooperation between humans and machines will occur. 

The Industry 5.0 market is estimated to reach $255.7 billion by 2029 (LINK). Machine learning – as an area of artificial intelligence (AI) – will be one of the areas that will play an even more crucial role at this stage. According to a study published in 2023 by the Manufacturing Leadership Council (LINK), more than a quarter of manufacturing companies are already implementing AI to provide improvements in quality management and many other areas. 

Automatic Quality Control of Moving Reflective Parts with Inspekto Technology 

Ensuring the right level of quality is an essential step in every production process. The visual quality control process can be divided into three successive stages: 

• Acquiring a high-quality image of the object being inspected 

• Item part recognition, during which the inspection system recognizes that the acquired image contains the part to be inspected 

• Item part inspection, i.e. conducting an analysis to determine whether a given part is good or defective 

However, in some cases it can be very difficult – especially when products, components or elements have specific features that are very difficult to inspect. For example, when parts made of highly reflective material move on a conveyor belt, the image acquisition stage becomes particularly difficult for vision systems. 

For this reason, the inspection of highly reflective moving parts has so far been a challenge for traditional machine vision solutions, preventing automation of quality control processes. Fortunately, an innovative solution has appeared on the market that is a response to this challenge. 

The Inspekto S70 (LINK) system is an example of groundbreaking technology that solves the problem of inspecting reflective, moving parts on production lines. Using Autonomous Machine Vision AI (AMV-AI) technology, specifically a combination of advanced optics and AI algorithms, it can dynamically adapt to changes in lighting and eliminate interference from reflections, which is crucial for shiny surfaces. 

The solution works in real time, analyzing images without having to stop the production line. The flexibility of the system allows for quick adjustment to different components, which minimizes downtime and increases overall production efficiency. Additionally, the Inspekto S70 simplifies the implementation of quality control by reducing set-up time and the need for manual programming. This allows manufacturing companies to respond faster to defects and maintain high quality standards. 

If you are interested in the topic of automation of production quality control using machine learning and would like to implement a dedicated solution of this type in your company, schedule a free consultation with Marcin Jabłonowski – Managing Director and AI Solutions Architect at Pragmile now.