Optimization of Fixture Number in Large Thin-Walled Parts Assembly Based on IPSO

There are lots of researches on fixture layout optimization for large thin-walled parts.Current researches focus on the positioning problem,i.e.,optimizing the positions of a constant number of fixtures.However,how to determine the number of fixtures is ignored.In most cases,the number of fixtures located on large thin-walled parts is determined based on engineering experience,which leads to huge fixture number and extra waste.Therefore,this paper constructs an optimization model to minimize the number of fixtures.The constraints are set in the optimization model to ensure that the part deformation is within the surface profile tolerance.In addition,the assembly gap between two parts is also controlled.To conduct the optimization,this paper develops an improved particle swarm optimization(IPSO)algorithm by integrating the shrinkage factor and adaptive inertia weight.In the algorithm,particles are encoded according to the fixture position.Each dimension of the particle is assigned to a sub-region by constraining the optional position range of each fixture to improve the optimization efficiency.Finally,a case study on ship curved panel assembly is provided to prove that our method can optimize the number of fixtures while meeting the assembly quality requirements.This research proposes a method to optimize the number of fixtures,which can reduce the number of fixtures and achieve deformation control at the same time.

Eliminating shrinkage defects and improving mechanical performance of large thin-walled ZL205A alloy castings by coupling travelling magnetic fields with sequential solidification

ZL205 A alloys with large thin-walled shape were continuously processed by coupling travelling magnetic fields(TMF)with sequential solidification,to eliminate the shrinkage defects and optimize the mechanical performance.Through experiments and simulations,the parameter optimization of TMF and the influence on feeding behavior,microstructure and properties were systematically studied.The results indicate that the magnetic force maximizes at the excitation current of 20 A and frequency of 200 Hz under the experimental conditions of this study,and increases from center to side-walls,which is more convenient to process thin-walled castings.TMF can break secondary dendritic arm and dendrites overlaps,widen feeding channels,prolong the feeding time,optimize the feeding paths,eliminate shrinkage defects and improve properties.Specifically,for as-cast state,TMF with excitation current of 20 A increases ultimate tensile strength,elongation and micro-hardness from 186 MPa,7.3%and 82.1 kg/mm^(2) to 221 MPa,11.7%and 100.5 kg/mm^(2),decreases porosity from 1.71%to 0.22%,and alters brittle fracture to ductile fracture.

Development of pressure control system in counter gravity casting for large thin-walled A357 aluminum alloy components

Counter gravity casting equipments(CGCE) were widely used to produce large thin-walled A357 aluminum alloy components. To improve the pressure control precision of CGCE to get high quality castings, a pressure control system based on fuzzy-PID hybrid control technology and the digital assembled valve was developed. The actual pressure tracking experiment results show that the special system by applying PID controller and fuzzy controller to varied phases, is not only able to inherit the small error and good static stability of classical PID control, but also has fuzzy control’s advantage of fully adapting itself to the object. The pressure control error is less than 0.3 kPa. By using this pressure control system, large complex thin-walled A357 aluminum alloy castings with high quality was successfully produced.

Large animal models for Huntington's disease research

Huntington'sdisease(HD)isahereditary neurodegenerative disorder for which there is currently no effectivetreatmentavailable.Consequently,the development of appropriate disease models is critical to thoroughly investigate disease progression.The genetic basis of HD involves the abnormal expansion of CAG repeats in the huntingtin(HTT)gene,leading to the expansion of a polyglutamine repeat in the HTT protein.Mutant HTT carrying the expanded polyglutamine repeat undergoes misfolding and forms aggregates in the brain,which precipitate selective neuronal loss in specific brain regions.Animal models play an important role in elucidating the pathogenesis of neurodegenerative disorders such as HD and in identifying potential therapeutic targets.Due to the marked species differences between rodents and larger animals,substantial efforts have been directed toward establishing large animal models for HD research.These models are pivotal for advancing the discovery of novel therapeutic targets,enhancing effective drug delivery methods,and improving treatment outcomes.We have explored the advantages of utilizing large animal models,particularly pigs,in previous reviews.Since then,however,significant progress has been made in developing more sophisticated animal models that faithfully replicate the typical pathology of HD.In the current review,we provide a comprehensive overview of large animal models of HD,incorporating recent findings regarding the establishment of HD knock-in(KI)pigs and their genetic therapy.We also explore the utilization of large animal models in HD research,with a focus on sheep,non-human primates(NHPs),and pigs.Our objective is to provide valuable insights into the application of these large animal models for the investigation and treatment of neurodegenerative disorders.

Research status and application of artificial intelligence large models in the oil and gas industry

This article elucidates the concept of large model technology,summarizes the research status of large model technology both domestically and internationally,provides an overview of the application status of large models in vertical industries,outlines the challenges and issues confronted in applying large models in the oil and gas sector,and offers prospects for the application of large models in the oil and gas industry.The existing large models can be briefly divided into three categories:large language models,visual large models,and multimodal large models.The application of large models in the oil and gas industry is still in its infancy.Based on open-source large language models,some oil and gas enterprises have released large language model products using methods like fine-tuning and retrieval augmented generation.Scholars have attempted to develop scenario-specific models for oil and gas operations by using visual/multimodal foundation models.A few researchers have constructed pre-trained foundation models for seismic data processing and interpretation,as well as core analysis.The application of large models in the oil and gas industry faces challenges such as current data quantity and quality being difficult to support the training of large models,high research and development costs,and poor algorithm autonomy and control.The application of large models should be guided by the needs of oil and gas business,taking the application of large models as an opportunity to improve data lifecycle management,enhance data governance capabilities,promote the construction of computing power,strengthen the construction of“artificial intelligence+energy”composite teams,and boost the autonomy and control of large model technology.

Development of Fixture Layout Optimization for Thin-Walled Parts:A Review

An increasing number of researchers have researched fixture layout optimization for thin-walled part assembly during the past decades.However,few papers systematically review these researches.By analyzing existing literature,this paper summarizes the process of fixture layout optimization and the methods applied.The process of optimization is made up of optimization objective setting,assembly variation/deformation modeling,and fixture layout optimization.This paper makes a review of the fixture layout for thin-walled parts according to these three steps.First,two different kinds of optimization objectives are introduced.Researchers usually consider in-plane variations or out-of-plane deformations when designing objectives.Then,modeling methods for assembly variation and deformation are divided into two categories:Mechanism-based and data-based methods.Several common methods are discussed respectively.After that,optimization algorithms are reviewed systematically.There are two kinds of optimization algorithms:Traditional nonlinear programming and heuristic algorithms.Finally,discussions on the current situation are provided.The research direction of fixture layout optimization in the future is discussed from three aspects:Objective setting,improving modeling accuracy and optimization algorithms.Also,a new research point for fixture layout optimization is discussed.This paper systematically reviews the research on fixture layout optimization for thin-walled parts,and provides a reference for future research in this field.

Evolution and Prospects of Foundation Models: From Large Language Models to Large Multimodal Models

Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language intelligence.Language modeling,crucial for AI development,has evolved from statistical to neural models over the last two decades.Recently,transformer-based Pre-trained Language Models(PLM)have excelled in Natural Language Processing(NLP)tasks by leveraging large-scale training corpora.Increasing the scale of these models enhances performance significantly,introducing abilities like context learning that smaller models lack.The advancement in Large Language Models,exemplified by the development of ChatGPT,has made significant impacts both academically and industrially,capturing widespread societal interest.This survey provides an overview of the development and prospects from Large Language Models(LLM)to Large Multimodal Models(LMM).It first discusses the contributions and technological advancements of LLMs in the field of natural language processing,especially in text generation and language understanding.Then,it turns to the discussion of LMMs,which integrates various data modalities such as text,images,and sound,demonstrating advanced capabilities in understanding and generating cross-modal content,paving new pathways for the adaptability and flexibility of AI systems.Finally,the survey highlights the prospects of LMMs in terms of technological development and application potential,while also pointing out challenges in data integration,cross-modal understanding accuracy,providing a comprehensive perspective on the latest developments in this field.

Meter-Scale Thin-Walled Structure with Lattice Infill for Fuel Tank Supporting Component of Satellite:Multiscale Design and Experimental Verification

Lightweight thin-walled structures with lattice infill are widely desired in satellite for their high stiffness-to-weight ratio and superior buckling strength resulting fromthe sandwich effect.Such structures can be fabricated bymetallic additive manufacturing technique,such as selective laser melting(SLM).However,the maximum dimensions of actual structures are usually in a sub-meter scale,which results in restrictions on their appliance in aerospace and other fields.In this work,a meter-scale thin-walled structure with lattice infill is designed for the fuel tank supporting component of the satellite by integrating a self-supporting lattice into the thickness optimization of the thin-wall.The designed structure is fabricated by SLM of AlSi10Mg and cold metal transfer welding technique.Quasi-static mechanical tests and vibration tests are both conducted to verify the mechanical strength of the designed large-scale lattice thin-walled structure.The experimental results indicate that themeter-scale thin-walled structure with lattice infill could meet the dimension and lightweight requirements of most spacecrafts.

An electromagnetic semi-active dynamic vibration absorber for thin-walled workpiece vibration suppression in mirror milling

As critical components of aircraft skins and rocket fuel storage tank shells,large thin-walled workpieces are susceptible to vibration and deformation during machining due to their weak local stiffness.To address these challenges,we propose a novel tunable electromagnetic semi-active dynamic vibration absorber(ESADVA),which integrates with a magnetic suction follower to form a followed ESADVA(follow-ESADVA)for mirror milling.This system combines a tunable magnet oscillator with a follower,enabling real-time vibration absorption and condition feedback throughout the milling process.Additionally,the device supports self-sensing and frequency adjustment by providing feedback to a linear actuator,which alters the distance between magnets.This resolves the traditional issue of being unable to directly monitor vibration at the machining point due to space constraints and tool interference.The frequency shift characteristics and vibration absorption performance are comprehensively investigated.Theoretical and experimental results demonstrate that the prototyped follow-ESADVA achieves frequency synchronization with the milling tool,resulting in a vibration suppression rate of approximately 47.57%.Moreover,the roughness of the machined surface decreases by18.95%,significantly enhancing the surface quality.The results of this work pave the way for higher-quality machined surfaces and a more stable mirror milling process.

Frontogenesis and Frontolysis of a Cold Filament Driven by the Cross-Filament Wind and Wave Fields Simulated by a Large Eddy Simulation

The variations of the frontogenetic trend of a cold filament induced by the cross-filament wind and wave fields are studied by a non-hydrostatic large eddy simulation. Five cases with different strengths of wind and wave fields are studied.The results show that the intense wind and wave fields further break the symmetries of submesoscale flow fields and suppress the levels of filament frontogenesis. The changes of secondary circulation directions—that is, the conversion between the convergence and divergence of the surface cross-filament currents with the downwelling and upwelling jets in the filament center—are associated with the inertial oscillation. The filament frontogenesis and frontolysis caused by the changes of secondary circulation directions may periodically sharpen and smooth the gradient of submesoscale flow fields.The lifecycle of the cold filament may include multiple stages of filament frontogenesis and frontolysis.

Numerical Analysis of Cold-Formed Thin-Walled Steel Short Columns with Pitting Corrosion during Bridge Construction

Pitting corrosion is harmful during bridge construction,which will lead to uneven roughness of steel surfaces and reduce the thickness of steel.Hence,the effect of pitting corrosion on the mechanical properties of cold-formed thin-walled steel stub columns is studied,and the empirical formulas are established through regression fitting to predict the ultimate load of web and flange under pitting corrosion.In detail,the failure modes and load-displacement curves of specimens with different locations,area ratios,and depths are obtained through a large number of non-linear finite element analysis.As for the specimens with pitting corrosion on the web,all the specimens are subject to local buckling failure,and the failure mode will not change with pitting corrosion,but the failure location will change with pitting corrosion location;the size,location,and area ratio of pitting corrosion have little influence on the ultimate load of cold-formed thin-walled steel short columns,but the loss rate of pitting corrosion section area has a greater impact on the ultimate bearing capacity.As for the specimen with flange pitting corrosion,the location and area ratio of pitting corrosion have less influence on the ultimate load of cold-formed thin-walled steel short columns,and the section area loss rate has greater influence on the ultimate bearing capacity;the impact of web pitting corrosion on the ultimate load is greater than that of flange pitting corrosion under the same condition of pitting corrosion section area.The prediction formulas of limit load which are suitable for pitting corrosion of web and flange are established,which can provide a reference for performance evaluation of corroded cold-formed thin-walled steel.

A large language model-powered literature review for high-angle annular dark field imaging

High-angle annular dark field(HAADF)imaging in scanning transmission electron microscopy(STEM)has become an indispensable tool in materials science due to its ability to offer sub-°A resolution and provide chemical information through Z-contrast.This study leverages large language models(LLMs)to conduct a comprehensive bibliometric analysis of a large amount of HAADF-related literature(more than 41000 papers).By using LLMs,specifically ChatGPT,we were able to extract detailed information on applications,sample preparation methods,instruments used,and study conclusions.The findings highlight the capability of LLMs to provide a new perspective into HAADF imaging,underscoring its increasingly important role in materials science.Moreover,the rich information extracted from these publications can be harnessed to develop AI models that enhance the automation and intelligence of electron microscopes.

Improvement in Tol2 transposon for efficient large-cargo capacity transgene applications in cultured cells and zebrafish(Danio rerio)

Most viruses and transposons serve as effective carriers for the introduction of foreign DNA up to 11 kb into vertebrate genomes.However,their activity markedly diminishes with payloads exceeding 11 kb.Expanding the payload capacity of transposons could facilitate more sophisticated cargo designs,improving the regulation of expression and minimizing mutagenic risks associated with molecular therapeutics,metabolic engineering,and transgenic animal production.In this study,we improved the Tol2 transposon by increasing protein expression levels using a translational enhancer(QBI SP163,ST)and enhanced the nuclear targeting ability using the nuclear localization protein H2B(SHT).The modified Tol2 and ST transposon efficiently integrated large DNA cargos into human cell cultures(H1299),comparable to the well-established super PiggyBac system.Furthermore,mRNA from ST and SHT showed a significant increase in transgene delivery efficiency of large DNA payloads(8 kb,14 kb,and 24 kb)into zebrafish(Danio rerio).This study presents a modified Tol2 transposon as an enhanced nonviral vector for the delivery of large DNA payloads in transgenic applications.

Warm bending mechanism of extrados and intrados of large diameter thin-walled CP-Ti tubes

In order to develop the warming bending technology of the large diameter thin-walled（LDTW） commercial pure titanium alloy CP-Ti tubes, the warm bending mechanism of the extrados and intrados of LDTW CP-Ti tubes was researched. By EBSD analysis and Vickers hardness test, the changes of microstructure and strength of the tubes at different bending temperatures of 293, 423 and 573 K, were analyzed. The results show： 1） The extrados of the bent tube deforms mainly by slip, along with few twinning, and the preferred orientation is similar to that of the initial tube; the intrados of the bent tube experiences compression deformation mainly by {1 012} tensile twinning, and the twinning makes the preferred orientation of wall materials change sharply. 2） The Vickers hardness values of both the extrados and intrados of the samples after bending increase greatly; the Vickers hardness values of the intrados are much higher than those of the extrados, and Vickers hardness values of the RD-TD planes are always higher than those of the RD-LD planes, which are related to the different deformation mechanisms.

Shallow Convection Dataset Simulated by Three Different Large Eddy Models

Shallow convection plays an important role in transporting heat and moisture from the near-surface to higher altitudes,yet its parameterization in numerical models remains a great challenge,partly due to the lack of high-resolution observations.This study describes a large eddy simulation(LES)dataset for four shallow convection cases that differ primarily in inversion strength,which can be used as a surrogate for real data.To reduce the uncertainty in LES modeling,three different large eddy models were used,including SAM(System for Atmospheric Modeling),WRF(Weather Research and Forecasting model),and UCLA-LES.Results show that the different models generally exhibit similar behavior for each shallow convection case,despite some differences in the details of the convective structure.In addition to grid-averaged fields,conditionally sampled variables,such as in-cloud moisture and vertical velocity,are also provided,which are indispensable for calculation of the entrainment/detrainment rate.Considering the essentiality of the entraining/detraining process in the parameterization of cumulus convection,the dataset presented in this study is potentially useful for validation and improvement of the parameterization of shallow convection.

The Immense Impact of Reverse Edges on Large Hierarchical Networks

Hierarchical networks are frequently encountered in animal groups,gene networks,and artificial engineering systems such as multiple robots,unmanned vehicle systems,smart grids,wind farm networks,and so forth.The structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower-to higher-level nodes,such as lagging birds’howl in a flock or the opinions of lowerlevel individuals feeding back to higher-level ones in a social group.This study reveals that,for most large-scale real hierarchical networks,the majority of the reverse edges do not affect the synchronization process of the entire network;the synchronization process is influenced only by a small part of these reverse edges along specific paths.More surprisingly,a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%.The effect of such edges depends not on the network size but only on the average in-degree of the involved subnetwork.The overwhelming majority of active reverse edges turn out to have some kind of“bunching”effect on the information flows of hierarchical networks,which slows down synchronization processes.This finding refines the current understanding of the role of reverse edges in many natural,social,and engineering hierarchical networks,which might be beneficial for precisely tuning the synchronization rhythms of these networks.Our study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.

Acetylacetone-TiO_(2) Promoted Large Area Compatible Cascade Electron Transport Bilayer for Efficient Perovskite Solar Cells

In designing efficient perovskite solar cells(PSCs),the selection of suitable electron transport layers(ETLs)is critical to the final device performance as they determine the driving force for selective charge extraction.SnO_(2)nanoparticles(NPs)based ETLs have been a popular choice for PSCs due to superior electron mobility,but their relatively deep-lying conduction band energy levels(ECB)result in substantial potential loss.Meanwhile,TiO_(2)NPs establish favorable band alignment owing to shallower ECB,but their low intrinsic mobility and abundant surface trap sites impede the final performance.For this reason,constructing a cascaded bilayer ETL is highly desirable for efficient PSCs,as it can rearrange energy levels and exploit on advantages of an individual ETL.In this study,we prepare SnO_(2)NPs and acetylacetone-modified TiO_(2)(Acac-TiO_(2))NPs and implement them as bilayer SnO_(2)/Acac-TiO_(2)(BST)ETL,to assemble cascaded energy band structure.SnO_(2)contributes to rapid charge carrier transport from high electron mobility while Acac-TiO_(2)minimizes band-offset and effectively suppresses interfacial recombination.Accordingly,the optimized BST ETL generates synergistic influence and delivers power conversion efficiency(PCE)as high as 23.14%with open-circuit voltage(V_(oc))reaching 1.14 V.Furthermore,the BST ETL is transferred to a large scale and the corresponding mini module demonstrates peak performance of 18.39%PCE from 25 cm^(2)aperture area.Finally,the BST-based mini module exhibit excellent stability,maintaining 83.1%of its initial efficiency after 1000 h under simultaneous 1 Sun light-soaking and damp heat(85℃/RH 85%)environment.

THE OPTIMAL LARGE TIME BEHAVIOR OF3D QUASILINEAR HYPERBOLIC EQUATIONS WITH NONLINEAR DAMPING

We are concerned with the large-time behavior of 3D quasilinear hyperbolic equations with nonlinear damping.The main novelty of this paper is two-fold.First,we prove the optimal decay rates of the second and third order spatial derivatives of the solution,which are the same as those of the heat equation,and in particular,are faster than ones of previous related works.Second,for well-chosen initial data,we also show that the lower optimal L^(2) convergence rate of the k(∈[0,3])-order spatial derivatives of the solution is(1+t)^(-(2+2k)/4).Therefore,our decay rates are optimal in this sense.The proofs are based on the Fourier splitting method,low-frequency and high-frequency decomposition,and delicate energy estimates.

Computing large deviation prefactors of stochastic dynamical systems based on machine learning

We present a large deviation theory that characterizes the exponential estimate for rare events in stochastic dynamical systems in the limit of weak noise.We aim to consider a next-to-leading-order approximation for more accurate calculation of the mean exit time by computing large deviation prefactors with the aid of machine learning.More specifically,we design a neural network framework to compute quasipotential,most probable paths and prefactors based on the orthogonal decomposition of a vector field.We corroborate the higher effectiveness and accuracy of our algorithm with two toy models.Numerical experiments demonstrate its powerful functionality in exploring the internal mechanism of rare events triggered by weak random fluctuations.

Multi-environment BSA-seq using large F3 populations is able to achieve reliable QTL mapping with high power and resolution: An experimental demonstration in rice

Bulked-segregant analysis by deep sequencing(BSA-seq) is a widely used method for mapping QTL(quantitative trait loci) due to its simplicity, speed, cost-effectiveness, and efficiency. However, the ability of BSA-seq to detect QTL is often limited by inappropriate experimental designs, as evidenced by numerous practical studies. Most BSA-seq studies have utilized small to medium-sized populations, with F2populations being the most common choice. Nevertheless, theoretical studies have shown that using a large population with an appropriate pool size can significantly enhance the power and resolution of QTL detection in BSA-seq, with F_(3)populations offering notable advantages over F2populations. To provide an experimental demonstration, we tested the power of BSA-seq to identify QTL controlling days from sowing to heading(DTH) in a 7200-plant rice F_(3)population in two environments, with a pool size of approximately 500. Each experiment identified 34 QTL, an order of magnitude greater than reported in most BSA-seq experiments, of which 23 were detected in both experiments, with 17 of these located near41 previously reported QTL and eight cloned genes known to control DTH in rice. These results indicate that QTL mapping by BSA-seq in large F_(3)populations and multi-environment experiments can achieve high power, resolution, and reliability.