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This course is verified by Probitas Authentication as meeting the AS9104/3A requirements for continuing Professional Development. AS13100 and RM13000 define the Problem-Solving standard for suppliers within the aero-engine sector, with the Eight Disciplines (8D) problem solving method the basis for this standard. This two-day course provides participants with a comprehensive and standardized set of tools to become an 8D practitioner. Successful application of 8D achieves robust corrective and preventive actions to reduce the risk of repeat occurrences and minimize the cost of poor quality.

The mobility industry and the entire ecosystem is currently striving towards sus-tainable mobility which leads to continuous production ramp-up of electrified vehicles. The parallel increase of the charging infrastructure is faced with various challenges regarding needed investments and the connection into the electricity grid. MAHLE chargeBIG offers centralized and large scaled charging infrastruc-ture with more than 1,800 already installed charging points. This presentation and paper is evaluating the functionality of the system by ana-lyzing backend real data of various employer parking installations. It can be shown and proven that a single-phase charging concept is sufficient and able to manage most customer relevant charging events by considering the needs and limitations of the related electricity grid infrastructure. Smart charging algorithms enable the integration of the charging infrastructure in smart grid company environments.

The optimization and further development of automated driving functions offers great potential to relieve the driver in various driving situations and increase road safety. Simulative testing in particular is an indispensable tool in this process, allowing conclusions to be drawn about the design of automated driving functions at a very early stage of development. In this context, the use of driving simulators provides support so that the driving functions of tomorrow can be experienced in a very safe and reproducible environment. The focus of the acceptance and optimization of automated driving functions is particularly on vehicle lateral control functions. As part of this paper, a test person study was carried out regarding manual vehicle lateral control on the dynamic vehicle road simulator at the Institute of Automotive Engineering.

Homologation is an important process in vehicle development and aerodynamics a main data contributor. The process is heavily interconnected: Production planning defines the available assemblies. Construction defines their parts and features. Sales defines the assemblies offered in different markets, where Legislation defines the rules applicable to homologation. Control engineers define the behavior of active, aerodynamically relevant components. Wind tunnels are the main test tool for the homologation, accompanied by surface-area measurement systems. Mechanics support these test operations. The prototype management provides test vehicles, while parts come from various production and prototyping sources and are stored and commissioned by logistics. Several phases of this complex process share the same context: Production timelines for assemblies and parts for each chassis-engine package define which drag coefficients or drag coefficient contributions shall be determined.

Investigating human driver behavior enhances the acceptance of the autonomous driving and increases road safety in heterogeneous environments with human-operated and autonomous vehicles. The previously established driver fingerprint model, focuses on the classification of driving style based on CAN bus signals. However, driving styles are inherently complex and influenced by multiple factors, including changing driving environments and driver states. To comprehensively create a driver profile, an in-car measurement system based on the Driver-Driven vehicle-Driving environment (3D) framework is developed. The measurement system records emotional and physiological signals from the driver, including ECG signal and heart rate. A Raspberry Pi camera is utilized on the dashboard to capture the driver's facial expressions and a trained convolutional neural network (CNN) recognizes emotion. To conduct unobtrusive ECG measurements, an ECG sensor is integrated into the steering wheel.