Coating of deep rolled and hammer peened deep drawing tools

Mechanical surface treatments like machine hammer peening and deep rolling can substitute an essential part of the manual polishing time in the conventional process chain of die and mold production. However, the increasing use of high strength steels in the automotive industry and the associated wear of deep drawing tools require further wear-protection methods. In this context it is still unknown if hammer peened and deep rolled surfaces can ensure a sufficient adhesive strength of a coating. Therefore, in the present work different coatings are applied on hammer peened and deep rolled surfaces. Finally, the wear behavior is examined in the strip drawing test. The evaluation of the experimental results proves the potential for an industrial application of the mechanically treated and coated tools

Potential of mechanical surface treatment for mould and die production

Abstract The use of mechanical surface treatment methods can extraordinarily increase the productivity in mould and die making processes. The present paper shows how deep rolling and machine hammer peening can smoothen machined surfaces in order to substitute manual polishing processes. Initial roughness of R a &gt; 3 μm can be smoothed to R a &lt; 1 μm, independent from the treated material. For a further improvement of the surface quality, a closer look is given to the influence of geometric effects in hammer peening. Both procedures also increase the surface hardness by work hardening. The influence on the attainable work hardening depth is examined in detail. When combined with thermal hardening operations, hardness and smoothness are still influenced positively, although this combination may be constrained by crack nucleation beneath.</jats:p

xGQA: Cross-Lingual Visual Question Answering

Recent advances in multimodal vision and language modeling have predominantly focused on the English language, mostly due to the lack of multilingual multimodal datasets to steer modeling efforts. In this work, we address this gap and provide xGQA, a new multilingual evaluation benchmark for the visual question answering task. We extend the established English GQA dataset to 7 typologically diverse languages, enabling us to detect and explore crucial challenges in cross-lingual visual question answering. We further propose new adapter-based approaches to adapt multimodal transformer-based models to become multilingual, and -- vice versa -- multilingual models to become multimodal. Our proposed methods outperform current state-of-the-art multilingual multimodal models (e.g., M3P) in zero-shot cross-lingual settings, but the accuracy remains low across the board; a performance drop of around 38 accuracy points in target languages showcases the difficulty of zero-shot cross-lingual transfer for this task. Our results suggest that simple cross-lingual transfer of multimodal models yields latent multilingual multimodal misalignment, calling for more sophisticated methods for vision and multilingual language modeling.Comment: Findings of ACL 202

Eleven years’ data of grassland management in Germany

The 150 grassland plots were located in three study regions in Germany, 50 in eachregion. The dataset describes the yearly grassland management for each grassland plotusing 116 variables.General information includes plot identifier, study region and survey year. Additionally,grassland plot characteristics describe the presence and starting year of drainage andwhether arable farming had taken place 25 years before our assessment, i.e. between1981 and 2006. In each year, the size of the management unit is given which, in somecases, changed slightly across years.Mowing, grazing and fertilisation were systematically surveyed:Mowing is characterised by mowing frequency (i.e. number of cuts per year), dates ofcutting and different technical variables, such as type of machine used or usage ofconditioner.For grazing, the livestock species and age (e.g. cattle, horse, sheep), the number ofanimals, stocking density per hectare and total duration of grazing were recorded. As aderived variable, the mean grazing intensity was then calculated by multiplying thelivestock units with the duration of grazing per hectare [LSU days/ha]. Different grazingperiods during a year, partly involving different herds, were summed up to an annualgrazing intensity for each grassland.For fertilisation, information on the type and amount of different types of fertilisers wasrecorded separately for mineral and organic fertilisers, such as solid farmland manure,slurry and mash from a bioethanol factory. Our fertilisation measures neglect dung droppedby livestock during grazing. For each type of fertiliser, we calculated its total nitrogencontent, derived from chemical analyses by the producer or agricultural guidelinesAll three management types, mowing, fertilisation and grazing, were used to calculate acombined land use intensity index (LUI) which is frequently used to define a measure forthe land use intensity. Here, fertilisation is expressed as total nitrogen per hectare [kg N/ha], but does not consider potassium and phosphorus.Information on additional management practices in grasslands was also recorded includinglevelling, to tear-up matted grass covers, rolling, to remove surface irregularities, seedaddition, to close gaps in the sward.New informationInvestigating the relationship between human land use and biodiversity is important tounderstand if and how humans affect it through the way they manage the land and todevelop sustainable land use strategies. Quantifying land use (the ‘X’ in such graphs) canbe difficult as humans manage land using a multitude of actions, all of which may affectbiodiversity, yet most studies use rather simple measures of land use, for example, bycreating land use categories such as conventional vs. organic agriculture. Here, we providedetailed data on grassland management to allow for detailed analyses and thedevelopment of land use theory. The raw data have already been used for &#62; 100 papers onthe effect of management on biodiversity (e.g. Manning et al. 2015)