The Smart Quality department focuses on analytical and technological approaches in industrial quality management. The overarching goal is to ensure and improve quality in production systems through the use of established quality methods and new technologies.
Meeting current Challenges
In order to secure their business success in the long term, manufacturing companies are forced to continuously improve their products and processes while reducing their costs. On the one hand, this requires constant adaptation and, on the other, the use of the latest, innovative methods and technologies in order to meet market requirements.
In order to ensure the use and transfer of results of current research and development activities into industrial practice, scientific support is needed for companies in their introduction and application phases. In addition to research and the offer of training and further education opportunities, transfer in the form of industrial application and consulting therefore also represents a core of the Smart Quality department.
Research and Competence Focuses
The prerequisite for high product quality is capable and stable production processes, which in turn require the efficient design and control of all elements of the entire production system. A multi-perspective view of quality in terms of system, process and product quality therefore forms the basis of current research activities.
The topics addressed in the Smart Quality department include, among others
- Technical cleanliness in production systems
- Maintenance and servicing of production processes
- Quality-assured development, manufacturing and assembly of products
Due to increasing digitalisation, manufacturing companies have access to ever larger amounts of data (process and quality data, measurement and test data, ...), which at the same time present them with the challenge of evaluating these in a targeted manner with the help of suitable methods and using them to optimise products and processes. From a methodological point of view, our research focuses are therefore decisively characterised by the use of data analytical procedures in production. These include, among others
- Analytical methods of classical statistics, including statistical design of experiments (DoE), graphical-explorative data analysis, descriptive statistics.
- Statistical procedures in quality management, e.g. approaches of the (Lean) Six Sigma methodology, statistical process control (SPC), quality control charts (QRK)
- Artificial intelligence, especially machine learning (supervised and unsupervised learning)
- Image mining, e.g. for processing image data from optical testing methods
Together, this results in innovative research and application fields that address the needs of industry using conventional and innovative methods, including
- Predictive Maintenance - Predictive maintenance and servicing to prevent plant and machine downtime.
- Predictive Process Control - predictive quality control in production systems to reduce scrap and rework through early process intervention
- Predictive Quality Inspection - Design of predictive, model-based inspection processes to reduce the scope of inspections and optimise inspection and process settings.
The Smart Quality department supports companies in the introduction and implementation of innovative solutions for industrial quality management. The range of services includes analysis and consulting projects, various training and further education courses and the development of individual solutions. You can find more information under the point Range of Services:
Current Research Projects in the Field of Smart Quality
Quality control in additive manufacturing of copper materials by predicting material and component properties using machine learning
DiKueRec (project processing is carried out by RIF e.V.)
Utilization of digital twins for efficient control of recycling processes in the use-case of refrigerating appliances recycling
Development of a novel self-learning gripper system and adaptation of reinforcement learning algorithms as extension of existing lightweight robots for flexible PCB assembly using Through Hole Technology (THT)
System for adaptive photonic surface testing with adaptive image evaluation in combination with a cleaning system