Explainable Quality Assurance and Diagnosis in Manufacturing Processes

• Applicant: Professor Dr. Christoph Reich
• Subject Area: Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing ; Production Systems, Operations Management, Quality Management and Factory Planning
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 505519151

French and German manufacturing companies are known for their high-quality production and their orientation towards smart factories. Quality assurance and control of complex production systems is a major challenge and is further exacerbated by the shortage of skilled labour in this domain. Quality problems must be detected and eliminated quickly. When a quality problem is detected, it is necessary to quickly understand the multiple possible causes for it (which can sometimes be contradictory to each other) in order to propose the most appropriate corrective actions to return the manufacturing process to its normal operating mode. The XQuality project is researching hybrid and explainable AI approaches to help manufacturing companies implement intelligent and automated quality assurance. The project combines data-based machine learning, semantic technologies and expert knowledge to monitor and explain product and process quality targets in a company. The goal is to develop an AI-based system that will assist the staff in identifying the main causes of quality issues as early as possible, to achieve reliability engineering in the domain of manufacurint, thanks to the new quality assurance models.

• DFG Programme: Research Grants
• International Connection: France
• Partner Organisation: Agence Nationale de la Recherche / The French National Research Agency
• Cooperation Partners: Professor Dr. Habib Abdulrab; Frédéric Pelascini, Ph.D.; Professor Dr. Ahmet Samet