XQuality

Explainable quality assurance and diagnostics in manufacturing processes

XAI (Explainable Artificial Intelligence) is becoming increasingly important in the field of manufacturing and quality assurance, as it enables complex AI models and their decision-making processes to be made more transparent and comprehensible. The use of XAI can have several important meanings in this area:

• Error detection and correction: manufacturing processes can be automated and optimized through the use of AI systems. However, it is important that these systems can detect and rectify errors at an early stage. XAI enables those responsible to understand the decision-making basis of the AI systems and identify sources of errors in order to take targeted measures to improve quality.
• Decision transparency: In production and quality assurance, complex algorithms are often used to make decisions. XAI makes it possible to make these decisions transparent and understandable. This enables engineers and specialists to understand the decisions, identify potential sources of error and increase confidence in the AI systems.
• Regulatory compliance: In some industries, such as the automotive industry or food production, certain regulations and standards must be adhered to. With XAI, companies can ensure that their AI systems comply with these regulations. It enables decisions to be reviewed and audited to ensure that quality standards are met.
• Regulatory compliance: In some industries, such as the automotive industry or food production, certain regulations and standards must be adhered to. With XAI, companies can ensure that their AI systems work in compliance with these regulations. It enables decisions to be reviewed and audited to ensure that quality standards are met.
• Quality analysis and optimisation: XAI enables detailed analysis of production data and quality assurance processes. By making decisions explainable, patterns and correlations can be identified that contribute to the optimization of product quality. This enables companies to make precise adjustments and improvements to production processes.
• Trust and acceptance: The introduction of AI systems in manufacturing and quality assurance can raise concerns among employees about automation and trust in the technology. XAI helps to address these concerns by ensuring transparency and traceability of decisions. This enables employees to better understand and accept the potential of AI systems.

Overall, XAI plays a crucial role in improving the efficiency, quality and reliability of manufacturing processes and quality assurance. It enables better error detection, decision transparency, rule conformity, quality analysis and optimization as well as building trust in AI systems.

Further information can be found on our Project website.