Assembly Of Components Research Articles

Repair of replication-dependent double-strand breaks differs between the leading and lagging strands.

Single-strand breaks (SSBs) are one of the most commonly occurring endogenous lesions with the potential to give rise to cytotoxic double-strand breaks (DSBs) during DNA replication. To investigate how replication-dependent DSBs are repaired, we employed Cas9 nickase (nCas9) to generate site- and strand-specific nicks in the budding yeast genome. We found that nCas9-induced nicks are converted to mostly double-ended DSBs during S phase. Repair of replication-associated DSBs requires homologous recombination (HR) and is independent of classical non-homologous end joining. Consistent with a strong bias to repair these lesions using a sister-chromatid template, we observed minimal induction of inter-chromosomal HR by nCas9. In a genome-wide screen to identify factors necessary for the repair of replication-dependent DSBs, we recovered components of the replication-coupled nucleosome assembly (RCNA) pathway. Our findings suggest that the RCNA pathway is especially important to repair DSBs arising from nicks in the leading-strand template through acetylation of histone H3K56.

A stacked graph neural network with self-exciting process for robotic cognitive strategy reasoning in proactive human-robot collaborative assembly

Proactive human-robot collaborative assembly, a cognitively driven human-robot collaboration, requires research into robot cognitive strategy reasoning to ensure that the robot actively collaborates with the operator in task completion. However, current methods primarily focus on the pairwise relationships of assembly components in discrete snapshots. They could fail to represent the interconnected status of dynamic assembly, leading to inaccurate task allocation, thereby affecting robotic cognitive strategy. To address this problem, we propose a stacked graph neural network (GNN) with self-exciting process to capture the correlation and triggering mechanisms between time-varying tasks. Firstly, a temporal hypergraph with assembly knowledge is constructed to represent the non-pairwise relationships among assembly components in time-varying tasks, aiming to reduce the redundant information brought by pairwise relationships. Then, considering the characteristic of mutual influence between assembly events, a Hawkes process is introduced into the stacked GNN architecture to learn the event correlation representation in the temporal hypergraph. This point process models the self-exciting process of assembly events for simultaneously capturing the individual and collective features of events, thereby revealing the triggering mechanisms of the dynamic events. Finally, the effectiveness of proposed method is demonstrated by comparative experiments and the results of robotic cognitive strategy reasoning on dynamic assembly.

Transitioning to Electric UTVs: Implications for Assembly Tooling

This case report explores the UTVs (utility terrain vehicles) transition from internal combustion engines to electric drive and how the shift will impact the assembly tooling industry. A multiple-case study at manufacturing plants was complemented by an exploratory survey with key stakeholders in the industry. The findings showed that the transition to electric drive is still in its infancy and is likely to accelerate soon. Electric vehicles were generally found to contain fewer components and thus have fewer applications for tightening tools in their assembly. Much of the difference comes from the fact that electric engines require far fewer tightening operations compared to internal combustion engines. However, the assembly of electric components and battery packs requires new advanced tooling solutions. When transitioning to electric drives, manufacturers were found to source their battery packs and electric engines most commonly from external suppliers. This can displace the tooling industry’s business within the segment. Several opportunities and challenges for assembly tool suppliers were identified. Firstly, the transition to electric drive will likely generate significant tooling needs on the manufacturers side. Electric vehicles tend to require more advanced tools and solutions, which likely will benefit premium tool suppliers with Industry 4.0 solutions. There are, however, long-term challenges as electric UTVs have fewer components and fewer tightenings in their assembly process. One long-term opportunity that could potentially offset the decline in tightenings within final assembly is battery pack assembly. This process does not only require a lot of advanced tightenings, but there are also opportunities for other joining techniques. Thus, the assembly tooling business’ biggest opportunities within the UTV industry are likely to shift from the vehicle’s final- to battery pack assembly.

Meta-learning enhanced adaptive robot control strategy for automated PCB assembly

The assembly of printed circuit boards (PCBs) is one of the standard processes in chip production, directly contributing to the quality and performance of the chips. In the automated PCB assembly process, machine vision and coordinate localization methods are commonly employed to guide the positioning of assembly units. However, occlusion or poor lighting conditions can affect the effectiveness of machine vision-based methods. Additionally, the assembly of odd-form components requires highly specialized fixtures for assembly unit positioning, leading to high costs and low flexibility, especially for multi-variety and small-batch production. Drawing on these considerations, a vision-free, model-agnostic meta-method for compensating robotic position errors is proposed, which maximizes the probability of accurate robotic positioning through interactive feedback, thereby reducing the dependency on visual feedback and mitigating the impact of occlusions or lighting variations. The proposed method endows the robot with the capability to learn and adapt to various position errors, inspired by the human instinct for grasping under uncertainties. Furthermore, it is a self-adaptive method that can accelerate the robotic positioning process as more examples are incorporated and learned. Empirical studies show that the proposed method can handle a variety of odd-form components without relying on specialized fixtures, while achieving similar assembly efficiency to highly dedicated automation equipment. As of the writing of this paper, the proposed meta-method has already been implemented in a robotic-based assembly line for odd-form electronic components. Since PCB assembly involves various electronic components with different sizes, shapes, and functions, subsequent studies can focus on assembly sequence and assembly route optimization to further enhance assembly efficiency.

Experimental thermal analysis of brake cooling relative to mass flow of air through a ventilated brake disc

Individual component testing is an integral part of vehicle development and can be performed in multiple stages of the product development process. One such component assembly that often requires extensive testing is the brake system. The brake system is crucial for vehicle safety and, thus, needs to be validated from a functional perspective.A test rig setup to accommodate a brake caliper fixture, as well as a funnel housing an anemometer, was purposefully designed and manufactured. This arrangement allows for measuring brake disc performance, surface temperature and quantifying mass airflow through the disc. The results show that the mass flow of air through the disc vanes highly depends on the disc temperature, but can also vary by more than 10 % for discs with the same specifications. Furthermore, the results highlight non-negligible differences in disc cool-down behaviour based on the braking scenario used to heat the disc up.

Precision compensation method for large-scale measurements based on full-space refractive index field reconstruction with vibratory mirror background oriented schlieren (VMBOS)

With the development of the large-scale equipment manufacturing industry, such as aircraft component assembly, wind turbine rotor blade measurement and large ship assembly the demand for optical measurement accuracy in large-dimensional spaces within industrial environments has become increasingly stringent. However, due to the presence of internal interference sources, light will no longer propagate in a straight line, thereby introducing measurement errors that cannot be ignored. To compensate for the measurement error, the refractive index field distribution within the large-dimensional measurement space must be reconstructed. Therefore, this paper proposed a vibratory mirror background oriented schlieren (VMBOS) based on vibratory scanning. This method scans the measurement space through the rotation of vibratory mirror to obtain light deflection angles at different directions. Subsequently, the refractive index field of the large-dimensional space is reconstructed by using the tomographic reconstruction algorithm. Then the optical path of the light in the optical measurement can be corrected through the Hamiltonian ray tracing algorithm, thereby achieving compensation for the measurement error. Finally, the VMBOS method has been verified through both simulation and experimental methods. The experimental results demonstrated that the method proposed in this paper can be applied to optical path correction and error compensation in large scale spaces.

Cotranslational molecular condensation of cochaperones and assembly factors facilitates axonemal dynein biogenesis

Axonemal dynein, the macromolecular machine that powers ciliary motility, assembles in the cytosol with the help of dynein axonemal assembly factors (DNAAFs). These DNAAFs localize in cytosolic foci thought to form via liquid-liquid phase separation. However, the functional significance of DNAAF foci formation and how the production and assembly of multiple components are so efficiently coordinated, at such enormous scale, remain unclear. Here, we unveil an axonemal dynein production and assembly hub enriched with translating heavy chains (HCs) and DNAAFs. We show that mRNAs encoding interacting HCs of outer dynein arms colocalize in cytosolic foci, along with nascent HCs. The formation of these mRNA foci and their colocalization relies on HC translation. We observe that a previously identified DNAAF assembly, containing the DNAAF Lrrc6 and cochaperones Ruvbl1 and Ruvbl2, colocalizes with these HC foci, and is also dependent on HC translation. We additionally show that Ruvbl1 is required for the recruitment of Lrrc6 into the HC foci and that both proteins function cotranslationally. We propose that these DNAAF foci are anchored by stable interactions between translating HCs, ribosomes, and encoding mRNAs, followed by cotranslational molecular condensation of cochaperones and assembly factors, providing a potential mechanism that coordinates HC translation, folding, and assembly at scale.

Flexural and vibration behaviours of novel covered CFRP composite joints with an MWCNT-modified adhesive

Abstract Co-curing bonding is more efficient than co-bonding and secondary bonding for structural component assembly. This work used novel covered laminas with co-cured joining techniques (CL-CCT) to create carbon fibre-reinforced polymer (CFRP) composite adhesive-bonded joints. Additionally, the researchers evaluated how multi-walled carbon nanotubes (MWCNTs) affect the bending and dynamic properties of CFRP composite joints. The researchers added various weights of MWCNTs to the covered laminas along with co-cured CFRP adhesive-bonded joints. The study revealed that epoxy and 0.25 wt% MWCNT adhesive produced the strongest and most flexible joints. These joints were 118 and 15% stronger than joints made from pure epoxy CL-CC CFRP, respectively. Compared to pure epoxy CC-CFRP composite joints, the strength of CL-CC CFRP composite joints with 0.25 wt% MWCNTs increased by 374 and 109%, respectively. Interestingly, MWCNTs with a wt% of 1.25 had the greatest natural frequency in all three vibration modes, which are 19, 19, and 13% higher than that of the pure epoxy CL-CC CFRP composite joint. There are 28, 30, and 24% more natural frequencies in 1.25 wt% MWCNT-based CL-CC CFRP composite joints than those in pure epoxy-based joints in all three modes. Analysis of variance was employed for statistical investigation. Optimization and prediction were done using an artificial neural network and the Levenberg–Marquardt technique.

Structural insights into Influenza A virus RNA polymerase PB1 binding to nuclear import host factor RanBP5

The genome of influenza A viruses consists of eight RNA segments that form a heterotrimer, and the viral genome undergoes transcription and replication in the nucleus. Thus, during infection, newly synthesized RNA polymerase subunits must be imported into the nucleus. Although several models have been proposed for this process, the consensus is that the RNA polymerase subunits PB1 and PA form a dimer in the cytoplasm and are transported into the nucleus by Ran binding protein 5 (RanBP5). The PB2 subunit undergoes separate transport to complete the nuclear import. However, the molecular mechanism of nuclear import by host factors and their interactions with proteins are largely unknown. Here we present the structural analysis of the RanBP5 and PB1 NLS domain complex by cryo-EM at 3.2 Å resolution. The pattern shows that the NLS domain of PB1 does not exist in a secondary structure and interacts with RanBP5 in a wrapped state. In addition, biochemical analyses of the mutant have identified critical amino acid sites involved in complex binding. The results suggest a stepwise assembly of influenza virus structural components regulated by nuclear import mechanisms and host factor binding, with important implications for drug discovery research.

ADAM8 inactivation modulates cellular component assembly gene expression in the mouse intervertebral disc

ADAM8 inactivation modulates cellular component assembly gene expression in the mouse intervertebral disc

Buckling cluster-based H-bonded icosahedral capsules and their propagation to a robust zeolite-like supramolecular framework

Hydrogen-bonded assembly of multiple components into well-defined icosahedral capsules akin to virus capsids has been elusive. In parallel, constructing robust zeolitic-like cluster-based supramolecular frameworks (CSFs) without any coordination covalent bonding linkages remains challenging. Herein, we report a cluster-based pseudoicosahedral H-bonded capsule Cu60, which is buckled by the self-organization of judiciously designed constituent copper clusters and anions. The spontaneous formation of the icosahedron in the solid state takes advantage of 48 charge-assisted CH···F hydrogen bonds between cationic clusters and anions (PF6-), and is highly sensitive to the surface protective ligands on the clusters with minor structural modification inhibiting its formation. Most excitingly, an extended three-periodic robust zeolitic-like CSF, is constructed by edge-sharing the resultant icosahedrons. The perpendicular channels of the CSF feature unusual 3D orthogonal double-helical patterns. The CSF material not only keeps its single-crystal character in the desolvated phase, but also exhibits excellent chemical and thermal stabilities as well as long-lived phosphorescence emission.

Reinforcement learning for fuselage shape control during aircraft assembly

Critical safety requirements necessitate ultra-high precision quality control during the assembly of large aerospace components to reduce the mismatch between parts to be joined. Traditional methods use heuristic shape adjustment or surrogate model-based control. These methods are limited by reliance on accurate model learning and inadequate robustness to varying initial assembly conditions. To address these limitations, this article proposes a model-free reinforcement learning approach for adaptive fuselage shape control during aircraft assembly. The trained reinforcement learning agent directly adjusts the aircraft components in response to their part variations and enables an autonomous system (like AlphaGo) to learn the optimal shape control policy. Specifically, the reinforcement learning environment is built on the finite element simulator. A reward function is developed to capture the optimization objective and introduces a scheme to enforce the original constraints. The proximal policy optimization algorithm is modified to speed up the learning progress and achieve better final performance. In the case study, the root-mean-square gap between components is reduced by 98.4% on average compared with their initial shape mismatch. Our proposed method outperforms the benchmark methods with smaller final shape errors, smaller maximum forces, and lower variations across different test samples.

Proposed checklist for the implementation of the SMT component assembly process

The manufacture of electronic products has evolved significantly over time: it began with Wire Assembly, passed through THT (Through Hole Technology) with the emergence of Printed Circuit Boards, and reached SMT (Surface Mount Technology), following the trend towards the miniaturization of electronic components. The complete SMT production process generally consists of three distinct stages: the application of solder paste, the positioning of components and solder reflow to electrically connect the components to the board. It is a process of assembling electronic components directly onto the surface of a printed circuit board (PCB), an automatic process that enables the production of electronic boards at high speed and precision. The manufacture of electronic boards using surface mount technology is the most up-to-date, even more so with the advent of Industry 4.0, which is increasingly raising the level of investment required to purchase the equipment/machines that make up a fully automated assembly line. However, it is common to find inefficient SMT lines, increasing manufacturing costs. In this context, this paper aims to present SMT equipment in the electronic board assembly sector, an efficient implementation system, considering everything from Design for Manufacturing (DFM) to Start Up, including Technical Information on the Board and the Components to be inserted into it, through the Programming of Assembly Equipment.

The variegated canalized-1 tomato mutant is linked to photosystem assembly

The recently described canal-1 tomato mutant, which has a variegated leaf phenotype, has been shown to affect canalization of yield. The corresponding protein is orthologous to AtSCO2 -SNOWY COTYLEDON 2, which has suggested roles in thylakoid biogenesis. Here we characterize the canal-1 mutant through a multi-omics approach, by comparing mutant to wild-type tissues. While white canal-1 leaves are devoid of chlorophyll, green leaves of the mutant appear wild-type-like, despite an impaired protein function. Transcriptomic data suggest that green mutant leaves compensate for this impaired protein function by upregulation of transcription of photosystem assembly and photosystem component genes, thereby allowing adequate photosystem establishment, which is reflected in their wild-type-like proteome. White canal-1 leaves, however, likely fail to reach a certain threshold enabling this overcompensation, and plastids get trapped in an undeveloped state, while additionally suffering from high light stress, indicated by the overexpression of ELIP homolog genes. The metabolic profile of white and to a lesser degree also green tissues revealed upregulation of amino acid levels, that was at least partially mediated by transcriptional and proteomic upregulation. These combined changes are indicative of a stress response and suggest that white tissues behave as carbon sinks. In summary, our work demonstrates the relevance of the SCO2 protein in both photosystem assembly and as a consequence in the canalization of yield.

Simultaneous accelerated stress testing of membrane electrode assembly components in polymer electrolyte fuel cells

The durability of polymer electrolyte fuel cells (PEFCs) in fuel cell electric vehicles is important for the shift from passenger cars to heavy-duty vehicles. The components of a PEFC, namely the proton exchange membrane (PEM), catalyst layer (CL), and gas diffusion layer (GDL), contribute to the degradation of the fuel cell performance. In this paper, we propose a method for simultaneously evaluating the degradation rates of these components by combining electrochemical characterization with operando synchrotron X-ray radiography. The open-circuit voltage, electrochemically active surface area (ECSA), and water saturation were used as the degradation indicators for the PEMs, CLs, and GDLs, respectively. The results of two accelerated stress tests (loading and start-stop cycles) after 10,000 cycles showed that the increase in water saturation owing to the loss of hydrophobicity due to carbon corrosion in the cathode GDL occurred on the same timescale as the degradation in the PEM and cathode CL. Specifically, during the load cycle AST, the cathode CL degraded with a 26% reduction in the ECSA along with the cathode GDL degradation with a 10% increase in water saturation. This suggests that more efforts should be devoted to studies on the durability of GDLs for heavy-duty applications.

A study investigating the integrated application of intelligent construction and assembly building based on BIM technology

The construction industry, is facing the inevitable trend of digital transformation in the new wave of technology. The general production and construction build cycle is long and on-site construction activities is associated with a number of environmental issues. Additionally, the construction progress points and management are not clear, which presents a challenge for the industry. Consequently, the objective of this article is to integrate and apply a range of technologies, including BIM (Building Information Modeling), cloud computing, big data, Internet of Things, 5G, RPA (Robotic Process Automation), and others, to on-site operation control by integrating them. This integration will facilitate the analysis of the considerable advantages of intelligent construction and assembly building in its application. The conference center of the Qionglai Talent Service Exchange Center will be used as a case study to demonstrate the potential of these technologies. The paper will also propose new views and ideas for addressing the challenges currently faced by the construction industry. The research findings indicate that the integration of multiple intelligent construction systems and assembly components enhances construction progress, simplifies construction processes, and minimizes construction-related pollution. This evidence supports the viability of efficient, intelligent, and sustainable building design and construction methodologies, and provides a robust foundation for the transformation and advancement of the industry.

Measurement of the absolute efficiency of the X-ARAPUCA photon detector for the DUNE Far Detector 1

The DUNE far detector has been designed to detect photons and electrons generated by the charged products of the interaction of neutrinos with a massive liquid argon (LAr) target. The photon detection system (PDS) of the first DUNE far detector (FD1) is composed of 6000 photon detection units, named X-ARAPUCA. The detection of the prompt light pulse generated by the particle energy release in LAr will complement and boost the DUNE LAr Time Projection Chamber. It will improve the non-beam events tagging and enable at low energies the trigger and the calorimetry of the supernova neutrinos. The X-ARAPUCA is an assembly of several components. Its photon detection efficiency (PDE) depends on the design of the assembly, on the grade of the individual components and on their coupling. The X-ARAPUCA PDE is one of the leading parameters for the PDS sensitivity, that in turn determines the sensitivity of the DUNE for the detection of core-collapse supernova within the galaxy and for nucleon decay searches. In this work we present the final assessment of the absolute PDE of the FD1 X-ARAPUCA baseline design, measured in two laboratories with independent methods and setups. Preliminary results were reported in Palomares (JINST 18(02):C02064, https://doi.org/10.1088/1748-0221/18/02/C02064, 2023). One hundred sixty units of these X-ARAPUCA devices have been deployed in the NP04 facility at the CERN Neutrino Platform, the 1:20 scale FD1 prototype, and will be operated during the year 2024. The assessed value of the PDE is a key parameter both in the NP04 and in the DUNE analysis and reconstruction studies.

An efficient framework for design optimization using parametric reduced order modeling in a virtual prototyping phase

Virtual Prototype Assembly is a tool enabling the virtual assembly of components, the prediction of noise and vibration performance, and the evaluation of component modification scenarios. FRF-based substructuring is the technique used to assemble the components, either obtained from experimental measurements or numerical simulation models. However, numerical simulations of components can be time-consuming, especially when exploring several component design modifications of large models. The paper proposes a framework combining Virtual Prototype Assembly with a parametric Model Order Reduction technique for vehicle design optimization. The proposed parametric Model Order Reduction technique generates a Reduced Order Model of a component maintaining an explicit dependency on material parameters and properties, such as: Young's Modulus, density, Poisson coefficient, connection stiffness, etc.. This enables fast and efficient simulation of components designs without the need of recomputing numerical models, thus streamlining the component design optimization process. The effect of component design modifications will be assessed at the predicted target vibration levels on an academic application case.

ITER full model in MCNP for radiation safety demonstration

The development of nuclear fusion as a safe and virtually limitless power source is receiving growing attention in the context of looming energy crisis and climate change. ITER project stands as the flagship international initiative and is advancing steadily. The construction of the Tokamak Complex is nearly finished, and the assembly of core components has begun on site. Simultaneously, the design is being finalized, and the safety case is becoming more concrete. Current approaches to radiation safety demonstration using 3D nuclear analysis with the Monte Carlo code MCNP require sophisticated artifacts to sew together simulations in separate models for the Tokamak and the rest of the facility. This results in cumbersome studies and, consequently, challengeable conclusions. To address this issue, we have built the an integral MCNP model of the ITER facility: the ITER full model. Along with improvements to the D1SUNED code, we illustrate its computational practicality and pertinence in two meaningful simulations for ITER safety case. This work represents the culmination of a two-decade-long effort of ITER modelling aiming to demonstrate adequate radiation safety. Beyond supporting the remaining design tasks, this model simplifies the corresponding 3D nuclear analysis and improves the robustness of the ITER safety case.

Data-driven through-process modelling of aluminum extrusion: Predicting mechanical properties

This paper reports on a case study on zero-defect manufacturing, using a data driven approach for predicting final product properties from material and manufacturing process information. The case is a company that produces aluminum automotive parts, having an integrated value chain from casting, extrusion, forming and component assembly. This value chain is unique in terms of the intervened and interdependent system of material, process and product properties, where the traditional scientific approach has been to apply physics-based, through-process models to explain the complex thermo-mechanical evolution. Here, an alternative and complementary data-driven model is trained and tested on process data for a 6xxx-alloy product range using machine learning, which then is compared to a physics-based as well as a simple hybrid model. The presented results indicate that a machine learning approach is advantageous when numerous relevant observations are available, inherently adapting to the specific company-level conditions. The main strengths of a physics-based model, on the other hand, is general applicability and self-sufficiency with no need of prior observations.