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Understanding the fracture mechanisms in brazed joints offers opportunities to improve joint design and brazing processes. Traditional ex-situ methods cannot capture the material behavior in real-time, making in-situ observation during mechanical testing, such as in SEM, invaluable. In the present work, in-situ bending tests were used to observe the crack propagation in brazed joints exhibiting both ductile and brittle fracture mechanisms. Samples were prepared with precise geometries and notched to initiate cracks in the joining zone. These in-situ tests provide valuable data on the mechanical behavior of brazed joints, offering insights into their failure processes. Three different joints were analyzed: AISI 304 brazed with AgCu filler metal, Mar M 509 brazed with Co-based filler metal and mixed joints of AA 6082 and AISI 304 brazed with AlGeSi filler metal. In the medium strength joint brazed with Ag 272 filler metal, the fracture occurred by slipping through the eutectic. In the high strength joint brazed with Co 900 filler metal, the crack propagated transgranularly through the intermetallic phases and stopped at the interface between the intermetallic and Co solid solution. The AA 6082 / AISI 304 joint was studied using an overlap geometry, showing that microcracks formed as the bending stress increased, finally leading to a failure at the Al7Fe2Si intermetallic layer, a critical microstructural feature. The used test procedure is suitable for further observations on the fracture mechanism in joints brazed in specific geometries as well as using different brazing process parameters and comparing the results with existing investigations.
[Chemische Formeln nicht adäquat darstellbar.]
Development of an Interface for the Synchronization of Delivery Dates in an ERP Migration Phase
(2025)
This paper focuses on designing a robust interface solution for synchronizing critical delivery dates during a complex ERP migration phase, necessitated by the parallel operation of a legacy system and a new target system. The core challenge is that automatic rollback operations triggered between the synchronized ERP systems—due to technical errors—are systematically not communicated to the downstream PPC systems (Production Planning and Control systems). This data inconsistency leads to significant business risks, including production disruptions, delivery delays, and increased inventory costs. The developed solution implements an API-centric approach based on Microservices principles to ensure loose coupling between the systems. It utilizes a two-stage synchronization mechanism to proactively resolve these inconsistencies. First, a time-delayed individual validation employs a strategic 30-minute buffer time to guarantee that potential ERP rollback operations are completed before consistency checks with the PPC system begin. Second, a comprehensive nightly consistency validation systematically reviews all business-relevant delivery dates within a defined time window and initiates automatic corrections. The system's status-based processing model, combined with comprehensive logging and a "Live-Feed" Web Interface, guarantees transaction security and complete traceability. This approach successfully minimizes business risks during the critical transition and offers an effective design pattern for handling rollback situations in similar complex, heterogeneous integration projects.
This thesis presents an architecture design process of a reactive feedback system for a CI/CT platform. The underlying CI/CT platform manages the automated integration and validation of vehicle functions. In such dynamic environment, delayed feedback can significantly impact work cycles. The goal of this work is to design the architecture of a reactive feedback system that delivers near real-time information about test progress or software artifact status through reporting mechanisms. The system design was shaped by a structured user research process, including questionnaires and semi-structured interviews, to derive both functional and non-functional requirements. Based on these insights, architecture decisions were documented using the ADR format and guided by the arc42 framework. The architecture is modeled through C4 and UML diagrams. These artifacts provide developers with a structural foundation, supports implementation, maintenance and future evolution of the system.
Key performance indicators form the basis for controlling large production systems and enable decision-makers to derive appropriate measures. Currently, large production companies are implementing extensive digitization measures for the collection and visualization of these key performance indicators. However, the potential of predictive analytics and automated decision-making has only been exploited in isolated cases.
The potential for improving the quality and efficiency of decision-making processes is not being exploited. This means that an opportunity to reduce production costs remains untapped. In addition, existing approaches are limited to individual solutions that are poorly scalable, adaptable, and reusable. The problem is solved by developing a methodology using the design science research approach. In a requirements analysis, the findings from the literature review are compared with the practical requirements of large production systems in order to create a detailed catalog of requirements.
Based on this, the sub-steps of the methodology are developed and orchestrated into an overall methodology. Validation is carried out through the implementation of concrete use cases, comparison with predefined success criteria, and incorporating expert feedback. The methodology is used to develop forecasting capabilities and decision automation and integrate them into the daily routine of production control in order to optimize the cost efficiency of production. The scalability and reusability of the applications developed with it enable their cross-plant introduction and uniform adaptability.
As the first use case, an automated cloud-based end-to-end data pipeline for container prediction is being developed in the supply centers of the BMW plant in Munich, enabling dynamic personnel planning and cost optimization.
Traffic data plays a significant role among designers who are involved in the proper planning of infrastructure and roads. Permanent counting stations that contain loop detectors and piezoelectric sensors collect the traffic data and are further analyzed for errors. The research centers on a holistic methodology to develop an algorithm and execute it in Python for the detection and correction of errors in datasets. The developed algorithm covers all rule-based checks, historical profiling, and
statistical analysis. The algorithm includes preprocessing of data, error correction, validation, and generating clean data. Validation is conducted with datasets from the Landesamt für Straßenbau und Verkehr, Sachsen (LASuV). The results with
screened data generated by the algorithm can now be effectively used and thus improve the quality of the data. This research lays a pathway for future projects and practical applications, providing high-quality, completely error-free datasets for optimal planning, operations, and decision-making.
