Competence areas DAMI
Research and Development within DAMI4.0 research center is organized around competence areas which consist of technical experts and leading practitioneers within their field. Competence areas typically meet 3-4 times a year to exchange knowledge and ideas. In addition, they serve as catalyst to initiate activities related to the theme of the area. Such activities typically cover a wide range from presenting some ideas, updating about recent developments, initiating student thesis projects or initiating work on applying for external funding for a new research project.
DAMI4.0 research center has an initial focus on five areas, addressing key properties of how digitalisation impacts production and processes in factories of the future using intelligent data-driven systems: System Aspects, Infrastructures, Intelligence, Organisational, Societal and Human Factors, while also addressing interactions between them.
System Quality Aspects
Current systems are not designed for exploiting, integrating and leveraging novel digitalization trends, tools, and methods such as Machine Learning, Artificial Intelligence, Big Data Analytics, or Cloud/Edge Computing. The focus area “System Aspects” addresses research and development that is required in order to migrate current systems towards intelligent systems that leverage the full potential of Cloud/Edge Computing, AI and Analytics pipelines. Further research is needed to ensure that system quality aspects such as security, performance, and reliability are taken into account when designing and migrating systems.
Infrastructure
Data-driven systems require a huge amount of data to be processed in order to make intelligent decisions. Research is required in order to facilitate intelligent decision making at scale where needed. This requires research in novel process monitoring approaches and Industrial IoT systems to acquire the data and transfer it to where the analysis is carried out. It requires research and developments in the area of Cloud Computing for scalability and elasticity in order to process large volumes of data under timeliness constraints. It requires localised on-factory edge computing for processing data close to the machine where data is generated as this will reduce the volume of data that is exchanged through the Cloud. Finally, research on networks that can provide support for timeliness and reliability is crucial and 5/6G networks and TimeSensitive Networks are an important aspect.
Intelligence
Recent progress in the area of Machine Learning, AI and Optimization enables intelligent decision making. This will improve the quality of the production processes and the final product, the prediction of system properties (such as sales forecasting or power generation), intelligent planning of machine downtime (predictive and condition-based maintenance), or the development and characterization of new materials and its properties. Research in this focus area is required in order to engineer trustworthy AI algorithms for a given use case (e.g. in the area of forest-based bio economy, manufacturing and metal processing, energy systems), and to develop and engineer data analytic tool chains and pipelines. Applications include optimization of processes using AI-based methods or mathematical algorithms, building digital twins for simulating the impact of process parameters on final product quality, thus ensuring zero-defect manufacturing by AI and edge computing.
Organizing and business creation
The introduction of new technologies such as machine learning and big data analytics requires new skills in employees and often new ways of working. It also enables new business models, such as MaaS (Manufacturing as a Service), which builds on lean principles and the adaptive smart factories of the future. New ways of working are needed to exploit the opportunities of digitalization in the creation of optimized and lean industrial systems.
Research in this area is developing new knowledge and evaluating new methods, tools and approaches to support digitalization and digital transformation. An important aspect is the necessary change management to ensure that digitalization is supported at all levels of the organization.
Sustainability, Societal and Human aspects
Digital technologies and digitalisation-related change can affect society at large as well as individuals. This area studies how digitalisation and digital transformation can create synergies between industry, individuals, employees and society at large. Particular focus is on the expansion towards Industry 5.0, where sustainable digital transformation and an interaction between people, the environment and society is central.
Companies are part of a societal context and affect people and society outside the company, as well as the employee inside the company. Central to this is highlighting the benefits, challenges and opportunities for companies to engage with local communities, but also the benefits that local communities can gain from interacting with companies. This increases the competitiveness and attractiveness of industry and can help to accelerate investment in research and innovation.
Agenda 2030 and its link to societal challenges, sustainability and diversity issues, as well as circular production that best supports technological development in harmony with natural resources are central to this area of competence.