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Artificial intelligence (AI) is a crucial technology of industrial digitalization. Especially in the production industry, a great potential is present in optimizing existing processes, e.g., concerning resource consumption, emission reduction, process and product quality improvements, predictive maintenance, and so on. Some of this potential is addressed by methods of industrial analytics beyond specific production technology. Furthermore, particular technological aspects in production systems address another part of this potential, e.g., mechatronics, robotics and motion control, automation systems, and so on. The problem is that the field of AI includes many research areas and methods, and many companies are losing the overview of the necessary and appropriate methods for solving the company problems. The reasons for this are, on the one hand, a lack of expertise in AI and, on the other hand, high complexity and risks of use for the companies (especially for SMEs). As a result, many potentials cannot yet be exploited. The KI-NET project aims to fill this gap, whereby a project overview is presented in this contribution.
In the following extended abstract, an overview including motivation, objectives and expected results of the Interreg Austria-Bavaria project "KI-Net" (AB292; www.kinet.eu/) is summarized. The project develops a cross-border competence network that investigates, researches, and develops fundamental methods for AI-based optimizations in industrial manufacturing.
In manufacturing industry, product failure is costly, as it results in financial and time losses. Understanding the causes of product failure is critical for reducing the occurrence of failure and optimising the manufacturing process. As a result, a number of studies utilising data-driven approaches such as machine learning have been conducted to reduce the occurrence of this failure and to improve the manufacturing process. While these data-driven approaches enable pattern recognition, they lack the advantages associated with knowledge-driven approaches, such as knowledge representation and deductive reasoning. Similarly, knowledge-driven approaches lack the pattern-learning capabilities inherent in data-driven approaches such as machine learning. Therefore, in this paper, leveraging the advantages of both data-driven and knowledge-driven approaches, we present a strategy with a prototype implementation to reduce manufacturing product failure. The proposed strategy combines a data-driven technique, Bayesian structural learning, with a knowledge-based technique, knowledge graphs.