Development of a non-intrusive heat transfer coefficient gauge and its application to high pressure die casting : effect of the process parameters

International audienc

A model to predict the heat transfer coefficient at the casting-die interface for the high pressure die casting process

International audienceA model is presented for the prediction of the heat transfer coefficient (HTC) at the casting‐die interface as a function of time for the high pressure die casting process. Contact geometry and interface characteristics are included in the model through die surface roughness, the mean trapped air layer between the casting and the die, the parameters of area density and the radius of contact spots. The density and the radius of contact spots are integrated into a classical thermal flux tube theory in order to calculate HTC at the casting‐die interface. The time dependence of the HTC is derived in terms of the degradation of contact between the casting and the die that occurs during solidification. The calculated HTC is found to agree well with the experimentally determined results for different casting conditions. The presented model provides a valuable tool to predict the effect of various casting process parameters, die surface roughness, casting quality and thickness on the HTC during the high pressure die casting process

Heat transfer at the casting/die interface in high pressure die casting : experimental results and contribution to modelling

Contenu dans : Modeling of casting, welding and advanced solidification processes - XIInternational audienc

Transferts thermiques dans les procédés de fonderie en moule permanent - Qualité des pièces, productivité, intégrité des outillages

Contenu dans : Procédés de transformation des matériaux de structure : contributions écritesInternational audienc

The Accurate Determination of Heat Transfer Coefficient and its Evolutionwith Time During High Pressure Die Casting ofAl-9%Si-3%CuandMg-9%Al-1%ZnAlloys

International audienc

Beyond the imitation game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting.Comment: 27 pages, 17 figures + references and appendices, repo: https://github.com/google/BIG-benc