NUMERICAL SIMULATION OF THE BULK FORMING PROCESS TO MANUFACTURE COUPLING DETAILS FROM TUBE BILLET

Currently, most coupling details in the actuators are made by traditional methods such as bulk forming from block billet and then metal cutting. Such manufacturing methods often lead to material wastes due to cutting a large amount of excess processing. To save material and improve production efficiency, tube billet would be selected for bulk forming. However, when tube billet is used for bulk forming, it should be carefully calculated to avoid instability and folding defects in workpiece. This article presents the research on the forming process of the coupling details using numerical simulation and based on the obtained results, the suitable geometric size of tube billet for the forming operation in closed die can be determined

Photoelastic coupling in gallium arsenide optomechanical disk resonators

We analyze the magnitude of the radiation pressure and electrostrictive stresses exerted by light confined inside GaAs semiconductor WGM optomechanical disk resonators, through analytical and numerical means, and find the electrostrictive force to be of prime importance. We investigate the geometric and photoelastic optomechanical coupling resulting respectively from the deformation of the disk boundary and from the strain-induced refractive index changes in the material, for various mechanical modes of the disks. Photoelastic optomechanical coupling is shown to be a predominant coupling mechanism for certain disk dimensions and mechanical modes, leading to total coupling g$_{om}$ and g$_0$ reaching respectively 3 THz/nm and 4 MHz. Finally, we point towards ways to maximize the photoelastic coupling in GaAs disk resonators, and we provide some upper bounds for its value in various geometries

Quality comparison of Y-shape joints by tube hydroforming with and without counterforce

The design capability, strength, and structural rigidity provided by tube hydroforming (THF) are successfully used in many applications to produce high-strength parts and assemblies with improved mechanical properties, optimized service life, and weight features. In tubular metal forming, output parameters such as branch height, distribution of tube wall material thickness, distribution of damage factor, metal flow, effective stress, and effective strain significantly affect the quality of the product after the forming process. Therefore, this paper aims to evaluate the manufacturing quality of Y-shape joints from AISI304 material steel tube through output parameters of THF process with and without counter punch force on numerical simulation base. The Finite Element Method (FEM) has become an established feature of metal forming technology. The objective of FEM is to replace costly and elaborate experimental testing with fast, low-cost computer simulation. The simulation study uses finite element method-based virtual prototyping techniques to characterize output parameters, gain insight into strain mechanics, and predict mechanical properties of shaped components. The research results are presented clearly and unambiguously through the evaluation of 7 criteria to compare the quality of the specimens hydroformed by two surveyed cases and optimize the crucial input process parameters. And these data can be applied in experiments, more efficient product and process design, calculation, and control of input parameters avoiding costly trial and error in industrial production. The findings can help technologists optimize process parameters in the hydroforming process of products with protrusion from a tubular blan

An efficient cuckoo-inspired meta-heuristic algorithm for multiobjective short-term hydrothermal scheduling

This paper proposes an efficient Cuckoo-Inspired Meta-Heuristic Algorithm (CIMHA) for solving multi-objective short-term hydrothermal scheduling (ST-HTS) problem. The objective is to simultaneously minimize the total cost and emission of thermal units while all constraints such as power balance, water discharge, and generation limitations must be satisfied. The proposed CIMHA is a newly developed meta-heuristic algorithm inspired by the intelligent reproduction strategy of the cuckoo bird. It is efficient for solving optimization problems with complicated objective and constraints because the method has few control parameters. The proposed method has been tested on different systems with various numbers of objective functions, and the obtained results have been compared to those from other methods available in the literature. The result comparisons have indicated that the proposed method is more efficient than many other methods for the test systems in terms of total cost, total emission, and computational time. Therefore, the proposed CIMHA can be a favorable method for solving the multi-objective ST-HTS problems

Effect of loading paths on hydroforming ability of stepped hollow shaft components from double layer pipes

The step hollow shaft components are composed of two layers of different materials, they are formed using tube hydroforming process due to its high strength and rigidity, low weight and flexible profiles, compared to traditional casting, welding, and forming methods. These products are effectively used in industries such as the automotive, shipbuilding, aerospace and defense, and oil and gas sectors. The success of various double layer pipe hydroforming process depends on several factors, with the most important being the internal pressure path and axial loading path. This paper presents research on the effect of input loading paths on the hydroforming ability of a different two-layer metal structure - an outer layer of SUS304 stainless steel and an inner layer of CDA110 copper - using 3D numerical simulations on Abaqus/CAE software. Output criteria were used to evaluate the forming ability of the formed components, including Von Mises stress, Plastic strain component (PEmax), wall thinning, and pipe profile, based on which the input loading paths were combined during the forming process. These output criteria allow for more accurate predictions of material behavior during the hydroforming process, as well as deformation and stress distribution. This can support the design process, improve product quality, reduce errors, and increase production efficiency. The research results can be applied as a basis for optimizing load paths for the next experimental step in the near future, for undergraduate and graduate training, as well as allowing designers and engineers to optimize the process of hydroforming of different 2-layer tubes, reducing costs, improving accuracy, flexible design, minimizing risks, and increasing efficienc

Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback

A key technology for the development of large language models (LLMs) involves instruction tuning that helps align the models' responses with human expectations to realize impressive learning abilities. Two major approaches for instruction tuning characterize supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF), which are currently applied to produce the best commercial LLMs (e.g., ChatGPT). To improve the accessibility of LLMs for research and development efforts, various instruction-tuned open-source LLMs have also been introduced recently, e.g., Alpaca, Vicuna, to name a few. However, existing open-source LLMs have only been instruction-tuned for English and a few popular languages, thus hindering their impacts and accessibility to many other languages in the world. Among a few very recent work to explore instruction tuning for LLMs in multiple languages, SFT has been used as the only approach to instruction-tune LLMs for multiple languages. This has left a significant gap for fine-tuned LLMs based on RLHF in diverse languages and raised important questions on how RLHF can boost the performance of multilingual instruction tuning. To overcome this issue, we present Okapi, the first system with instruction-tuned LLMs based on RLHF for multiple languages. Okapi introduces instruction and response-ranked data in 26 diverse languages to facilitate the experiments and development of future multilingual LLM research. We also present benchmark datasets to enable the evaluation of generative LLMs in multiple languages. Our experiments demonstrate the advantages of RLHF for multilingual instruction over SFT for different base models and datasets. Our framework and resources are released at https://github.com/nlp-uoregon/Okapi

Strong scaling of general-purpose molecular dynamics simulations on GPUs

We describe a highly optimized implementation of MPI domain decomposition in a GPU-enabled, general-purpose molecular dynamics code, HOOMD-blue (Anderson and Glotzer, arXiv:1308.5587). Our approach is inspired by a traditional CPU-based code, LAMMPS (Plimpton, J. Comp. Phys. 117, 1995), but is implemented within a code that was designed for execution on GPUs from the start (Anderson et al., J. Comp. Phys. 227, 2008). The software supports short-ranged pair force and bond force fields and achieves optimal GPU performance using an autotuning algorithm. We are able to demonstrate equivalent or superior scaling on up to 3,375 GPUs in Lennard-Jones and dissipative particle dynamics (DPD) simulations of up to 108 million particles. GPUDirect RDMA capabilities in recent GPU generations provide better performance in full double precision calculations. For a representative polymer physics application, HOOMD-blue 1.0 provides an effective GPU vs. CPU node speed-up of 12.5x.Comment: 30 pages, 14 figure

CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages

The driving factors behind the development of large language models (LLMs) with impressive learning capabilities are their colossal model sizes and extensive training datasets. Along with the progress in natural language processing, LLMs have been frequently made accessible to the public to foster deeper investigation and applications. However, when it comes to training datasets for these LLMs, especially the recent state-of-the-art models, they are often not fully disclosed. Creating training data for high-performing LLMs involves extensive cleaning and deduplication to ensure the necessary level of quality. The lack of transparency for training data has thus hampered research on attributing and addressing hallucination and bias issues in LLMs, hindering replication efforts and further advancements in the community. These challenges become even more pronounced in multilingual learning scenarios, where the available multilingual text datasets are often inadequately collected and cleaned. Consequently, there is a lack of open-source and readily usable dataset to effectively train LLMs in multiple languages. To overcome this issue, we present CulturaX, a substantial multilingual dataset with 6.3 trillion tokens in 167 languages, tailored for LLM development. Our dataset undergoes meticulous cleaning and deduplication through a rigorous pipeline of multiple stages to accomplish the best quality for model training, including language identification, URL-based filtering, metric-based cleaning, document refinement, and data deduplication. CulturaX is fully released to the public in HuggingFace to facilitate research and advancements in multilingual LLMs: https://huggingface.co/datasets/uonlp/CulturaX.Comment: Ongoing Wor