Explainable Quality Assurance and Diagnosis in Manufacturing Processes

XAI (Explainable Artificial Intelligence) is becoming increasingly important in the field of manufacturing and quality assurance as it allows for making complex AI models and their decision-making processes more transparent and understandable. The use of XAI can have several important implications in this area:

• Error detection and resolution: By employing AI systems, manufacturing processes can be automated and optimized. However, it’s crucial for these systems to detect and resolve errors early on. XAI enables decision-makers to understand the basis of the AI systems’ decisions and identify sources of errors to take targeted actions for quality improvement.
• Decision transparency: Complex algorithms often make decisions in manufacturing and quality assurance. XAI enables these decisions to be transparent and comprehensible. This allows engineers and professionals to comprehend decisions, identify potential sources of errors, and enhance trust in AI systems.
• Regulatory compliance: In certain industries like automotive or food production, specific regulations and standards must be adhered to. XAI helps companies ensure that their AI systems operate in accordance with these regulations. It enables the review and auditability of decisions to ensure compliance with quality standards.
• Quality analysis and optimization: XAI enables a detailed analysis of production data and quality assurance processes. Through the explainability of decisions, patterns and correlations contributing to optimizing product quality can be identified. This enables companies to make precise adjustments and improvements in manufacturing processes.
• Trust and acceptance: The introduction of AI systems in manufacturing and quality assurance can raise concerns among employees regarding automation and trust in the technology. XAI helps address these concerns by ensuring transparency and comprehensibility of decisions. This enables employees to better understand and accept the potential of AI systems. Overall, XAI plays a crucial role in enhancing the efficiency, quality, and reliability of manufacturing processes and quality assurance. It allows for better error detection, decision transparency, regulatory compliance, quality analysis and optimization, as well as building trust in AI systems.