Gestamp (Germany)

Abstract The optimisation of energy-intensive processes such as the press hardening process plays a key role in reducing waste, lowering energy consumption and promoting sustainable production. In the Emuldan project (‘Increasing energy efficiency in production through multivalent data use’), an architecture-supported process model and corresponding interoperable software tools were developed to systematically tap optimisation potential in production technology systems. The focus is on the use of machine learning (ML) methods regarding the interaction of data-based approaches with physically based simulation models. By combining ML methods and simulation based on physical modelling, a hybrid approach was developed for a new type of control system. This approach enables the precise determination of process windows and the setting of optimum operating points in real production environments. This makes it possible to address conflicting objectives such as reducing waste, improving energy efficiency, monitoring quality and optimising cycle times. The combination of simulation results and real process data in hybrid models makes it possible to optimally fulfil energy efficiency and quality requirements at the same time. The shifting of process parameters to the edges of the process window requires model-based controls to avoid process instabilities. This article presents the possibilities of intelligent process control using hybrid ML methods and multivalent data utilisation, using the example of the hot forming process. It also shows how such approaches can help to make production more sustainable and resource efficient. Finally, the article provides an outlook on future research approaches and further developments in the field of data-driven manufacturing technologies.

<div class="section abstract"><div class="htmlview paragraph">Third generation advanced high strength steels (AHSS) that rely on the transformation of austenite to martensite have gained growing interest for implementation into vehicle architectures. Previous studies have identified a dependency of the rate of austenite decomposition on the amount of strain and the associated strain path imposed on the sheet. The rate and amount of austenite transformation can impact the work hardening behavior and tensile properties. However, a deeper understanding of the impact on toughness, and thus crash performance, is not fully developed. In this study, the strain path and strain amounts were systematically controlled to understand the associated correlation to impact toughness in the end application condition (strained and baked). Impact toughness was evaluated using an instrumented Charpy machine with a single sheet v-notch sample configuration. The instrumented striker provides a load - displacement curve as well as a total impact energy measurement, which is the integration of the load - displacement curve. Using these measurements, this study intends to understand any potential correlation between amount of retained austenite transformation, through the varying of strain paths and amount of strain, and resulting impact toughness after paint bake.</div></div>

Abstract High performance polymeric materials such as poly-ether-ether-ketone (PEEK) are increasingly being used for challenging tribological applications in order to replace metal parts in vehicle engines and transmissions. The tribology of natural PEEK, under oil-lubricated conditions, was studied for different metal counterbody finishes. Two different finishing processes were selected for this study: turning and polishing. The test system used was a tri-pin on disc, with pins made of PEEK and counterbodies made of steel, and then dipped in ATF Dexron VI oil. The conclusion was that the wear rate generated by turning was about seven times as high as the wear rate generated by polishing. The friction coefficient displayed a direct correlation with the lubrication regime, and the level of counterbody roughness. On average, the friction coefficient on the hydrodynamic regime for polishing was more than 3 times lower than the friction coefficient in the boundary regime for turning.

This research presents six simultaneous innovative occupant near side lateral impact protection concepts, including a dynamic door, a high-volume side airbag, a large external airbag that covers doors, sill and B-pillar of the struck vehicle and other concepts for increasing the distance between the occupant and the door panel (active armrest, inflatable door beam and moving seat). All systems are based on pre-crash detection of the impact and are activated as soon as 80 ms before the impact. This paper details the task of integrating these systems into a vehicle using finite element models, sled tests and full scale crash tests.

Tino - an electrically powered solar car is the result of our development effort to replace gasoline fueled-automobiles in certain areas. The light weight glass fiber reinforced plastic (GFK) is the main material for the car body construction. In this newly designed car a conventional solar panel is integrated into the car roof. With 80 Wp it feeds the independent electrical system, i.e. the lighting and indicator circuit. Another novel feature is the arrangement of the batteries in two packages in the front and the rear of the car, which can easily be exchanged by a trolley within a few minutes. Equipped with normal lead-acid batteries the 550 kg light car has a range of almost 80 km. This value can be exceeded by using other battery technologies, i.e. Ni/Cd, Na/S, Zn/Br or a second battery set. In order to reach zero pollution, a stationary solar “petrol” station of about 800 Wp is required. This relatively low power requirement is only possible when the energy consumption of the car is low, and a light-weight car is used. In the future a demonstration project with five cars of similar body construction and a 3 kWp solar “petrol” station is planned. The photovoltaic performance and different electical motors in combination with other battery technologies will be investigated with respect to the best overall efficiency.