In today's world, where precision and efficiency are crucial, quality assurance has found a new ally: Artificial Intelligence (AI). By using AI technologies in quality assurance, companies will be able to revolutionise their quality control processes and set new standards of excellence.
POSSIBLE APPLICATIONS OF KI IN QUALITY ASSURANCE
• AUTOMATION OF PROCESSES
AI-based systems enable the automation of quality assurance processes to an unprecedented extent. From inspecting products to analysing data, AI algorithms can take over repetitive tasks, reducing human error and increasing efficiency.
• PREDICTING QUALITY PROBLEMS
By analysing large amounts of data, AI models can identify trends and patterns that indicate potential quality issues. This allows companies to act proactively to avoid defects and continuously improve product quality.
• OPTIMISED DEFECT DETECTION
AI-supported image recognition systems can identify defects or deviations in production quickly and precisely. By using machine learning algorithms, these systems are continuously improved and can even recognise subtle defects that are difficult for the human eye to detect.
• ADAPTIVE QUALITY CONTROL
Another advantage of AI-based quality assurance systems is their ability to adapt and optimise over time. Through continuous learning and feedback, these systems can continuously improve their performance and adapt to changing production conditions.
• INCREASING EFFICIENCY AND REDUCING COSTS
By using AI in quality assurance, companies can increase their efficiency and reduce costs at the same time. The automation of processes, the early detection of quality problems and the optimisation of resources help to minimise waste and maximise profitability.
EXAMPLES OF KI IN QUALITY ASSURANCE
Image recognition and defect analysis:
AI algorithms can analyse images or videos of production lines to identify defects or deviations. For example, AI systems
in the automotive industry can check car body parts for paint defects or monitor circuit boards for faulty solder joints
in electronics production.
Predictive maintenance:
AI can be used to monitor the condition of production facilities and machines and predict potential failures or quality
problems. By detecting problems at an early stage, companies can plan maintenance work and reduce unplanned downtime.
Real-time quality monitoring:
AI-powered systems can monitor production data in real time and perform analyses to ensure that quality standards are
being met. This enables companies to react immediately to quality problems and adjust production processes accordingly.
Natural language processing (NLP):
In industries such as food production, AI systems can be used to check product labelling and documentation for compliance
with regulatory requirements. By analysing texts, potential risks can be identified and eliminated before products
reach the market.
Robot-based quality inspection:
AI-powered robots can be used in manufacturing to inspect and sort parts. These robots can be equipped with image
recognition systems to detect defects and automatically reject faulty parts.
Data analysis and process optimisation:
AI can be used to analyse large amounts of production data and detect patterns that could indicate quality issues.
By optimising production processes, companies can continuously improve the quality of their products.
Overall, the integration of AI technologies into quality assurance offers enormous opportunities for companies to strengthen their competitiveness and set new standards for product quality and customer satisfaction. By using AI, companies can successfully overcome the challenges of the modern economy and establish themselves as pioneers in their industries.
PEROBA QUALITY MANAGEMENT FROM MUNICH - CONSULTING AROUND KI IN QUALITY MANAGEMENT AND QUALITY ASSURANCE
CONSULTING, IMPLEMENTATION, AUDITS AND QM TOOLS FROM A SINGLE SOURCE
Further information on AI in quality management can be found here.
PeRoBa GmbH Munich is a service provider with many years of experience in quality management, especially in the automotive and mechanical engineering sectors. We help with all important standards (ISO 9001, ISO 27001, ISO 45001, VDA6.3, IATF 16949,...) on the way to certification or re-certification. We also work closely with universities and research institutes. Managing Director Dr Scherb teaches as a lecturer, for example, at the Hamburger Fern-Hochschule, the FOM in Munich and is also a speaker at the TÜV-Süd Akademie, the Bildungswerk der Bayerischen Wirtschaft and many other institutions.