Spatio-temporal and immersive visual analytics for advanced manufacturing

Abstract

The increasing amount of digitally available information in the manufacturing domain is accompanied by a demand to use these data to increase the efficiency of a product’s overall design, production, and maintenance steps. This idea, often understood as a part of Industry 4.0, requires the integration of information technologies into traditional manufacturing craftsmanship. Despite an increasing amount of automation in the production domain, human creativity is still essential when designing new products. Further, the cognitive ability of skilled workers to comprehend complex situations and solve issues by adapting solutions of similar problems makes them indispensable. Nowadays, customers demand highly customizable products. Therefore, modern factories need to be highly flexible regarding the lot size and adaptable regarding the produced goods, resulting in increasingly complex processes. One of the major challenges in the manufacturing domain is to optimize the interplay of human expert knowledge and experience with data analysis algorithms. Human experts can quickly comprehend previously unknown patterns and transfer their knowledge and gained experience to solve new issues. Contrarily, data analysis algorithms can process tasks very efficiently at the cost of limited adaptability to handle new situations. Further, they usually lack a sense of semantics, which leads to a need to combine them with human world knowledge to assess the meaningfulness of such algorithms’ results. The concept of Visual Analytics combines the advantages of the human’s cognitive abilities and the processing power of computers. The data are visualized, allowing the users to understand and manipulate them interactively, while algorithms process the data according to the users’ interaction. In the manufacturing domain, a common way to describe the different states of a product from the idea throughout the realization until the product is disposed is the product lifecycle. This thesis presents approaches along the first three phases of the lifecycle: design, planning, and production. A challenge that all of the phases face is that it is necessary to be able to find, understand, and assess relations, for example between concepts, production line layouts, or events reported in a production line. As all phases of the product lifecycle cover broad topics, this thesis focuses on supporting experts in understanding and comparing relations between important aspects of the respective phases, such as concept relationships in the patent domain, as well as production line layouts, or relations of events reported in a production line. During the design phase, it is important to understand the relations of concepts, such as key concepts in patents. Hence, this thesis presents approaches that help domain experts to explore the relationship of such concepts visually. It first focuses on the support of analyzing patent relationships and then extends the presented approach to convey relations about arbitrary concepts, such as authors in scientific literature or keywords on websites. During the planning phase, it is important to discover and compare different possibilities to arrange production line components and additional stashes. In this field, the digitally available data is often insufficient to propose optimal layouts. Therefore, this thesis proposes approaches that help planning experts to design new layouts and optimize positions of machine tools and other components in existing production lines. In the production phase, supporting domain experts in understanding recurring issues and their relation is important to improve the overall efficiency of a production line. This thesis presents visual analytics approaches to help domain experts to understand the relation between events reported by machine tools and comprehend recurring error patterns that may indicate systematic issues during production. Then, this thesis combines the insights and lessons learned from the previous approaches to propose a system that combines augmented reality with visual analysis to allow the monitoring and a situated analysis of machine events directly at the production line. The presented approach primarily focuses on the support of operators on the shop floor. At last, this thesis discusses a possible combination of the product lifecycle with knowledge generating models to communicate insights between the phases, e.g., to prevent issues that are caused from problematic design decisions in earlier phases. In summary, this thesis makes several fundamental contributions to advancing visual analytics techniques in the manufacturing domain by devising new interactive analysis techniques for concept and event relations and by combining them with augmented reality approaches enabling an immersive analysis to improve event handling during production.