Assembly Simulation Research Articles

Point cloud-based feature extraction and surface reconstruction for parts paring in high-precision assembly

Abstract Assembly plays a crucial role in industrial manufacturing in industrial manufacturing, but the efficiency of conventional manual methods for parts pairing is limited. Previous research has demonstrated the feasibility of deep learning for point cloud feature extraction and 3D reconstruction. An innovative method utilizing deep learning for high-precision feature extraction and surface reconstruction is introduced to optimize parts pairing in this paper. Geometric dimensions and surface topography data are obtained by defining key assembly features and utilizing the Random Sample Consensus method. Deep learning is then used to directly regress the Surface Distance Function from point samples, facilitating comprehensive part surface modeling and supporting assembly simulation in the digital twin. To validate this approach, a case study demonstrates successful matching of 30 shaft parts and 30 hole parts after optimization, with an increase in average uniformity by 0.024. This highlights the proposed method’s superior effectiveness and accuracy in feature extraction and surface reconstruction.

Monte Carlo Simulations of Spent Nuclear Fuel Dry Storage Cask Measurements with a New External Remote Monitoring System for Developing Safeguards Approaches

Until a long-term solution for the disposal of spent nuclear fuel (SNF) is available, interim dry casks will be increasingly used for the storage of SNF discharged from civilian nuclear power reactors. Dry casks containing commercial SNF may hold several significant quantities of plutonium, so appropriate nuclear material safeguards monitoring is needed. An external remote monitoring system (RMS) has been developed to advance dry cask safeguards monitoring beyond the current method of containment and surveillance used to maintain continuity of knowledge. In this study, neutron transport simulations of SNF assemblies in a dry cask were performed for several special nuclear material diversion scenarios. The simulations considered various loading patterns and fuel storage durations as long as 100 years. For each fuel loading pattern and storage time investigated, the simulation results were used to calculate the required measurement time to achieve a nondetection probability β ≤ 10% for the diversion of any single fuel assembly in the cask. The calculations were performed for false alarm probabilities α as low as 0.0001% (or 10−6). A Monte Carlo postprocessing approach was developed to consider the impact on the required measurement time of uncertainty in the burnup of fuel assemblies. The study found that the external RMS is well suited for the surveillance of SNF in dry cask storage for nuclear safeguards or other purposes and is able to detect the diversion of a single SNF assembly even after decades of storage and with a very low false alarm probability.

Assembly Simulation and Optimization Method for Underconstrained Frame Structures of Aerospace Vehicles

Aerodynamic contour dimensional accuracy is very important for the stable and safe flight of aerospace vehicles. Nevertheless, due to the influence of various factors such as material properties, machining and manufacturing deviations, and assembly and installation deviations, key structural geometric dimensions are frequently exceeded. Therefore, this paper investigates a data-driven combined vector loop method (VLM)–Skin Model Shapes (SMS) method to realize aerospace vehicle structural geometric accuracy analysis; assembly optimization targeting contour deviation is also achieved. Tests are carried out on a typical aerospace vehicle’s underconstrained structural workpieces to validate the effectiveness of the proposed method.

DEM modelling of particle crushing of single carbonate sand using the improved bonded particle model

Carbonate sands have complex particle morphology and abundant intra-particle pores, influencing their mechanical beahviours. This paper presents a detailed modelling procedure for single-particle crushing of carbonate sands with different shapes. The particles were scanned, and their realistic profiles were simplified using a curvature-based simplification technique before being imported into the DEM program. An improved bonded particle model using coplanar Voronoi polyhedrons as cells was adopted, and the internal porosity of carbonate sands, which follows a Weibull distribution, was modelled by randomly removing internal cells. The effects of local damping and loading rate on crushing responses are discussed and their values are carefully determined. Four loading stages were observed during platen compression. Particles with different shapes experienced varying degrees of tension and shear failure in internal bonds and exhibited different probabilities of splitting, explosive, and chipping failures. The work presented lays the groundwork for subsequent simulations of carbonate assemblies.

A colloidal model for the equilibrium assembly and liquid-liquid phase separation of the reflectin A1 protein

Reflectin is an intrinsically disordered protein known for its ability to modulate the biophotonic camouflage of cephalopods based on its assembly-induced osmotic properties. Its reversible self-assembly into discrete, size-controlled clusters and condensed droplets are known to depend sensitively on the net protein charge, making reflectin stimuli-responsive to pH, phosphorylation, and electric fields. Despite considerable efforts to characterize this behavior, the detailed physical mechanisms of reflectin’s assembly are not yet fully understood. Here, we pursue a coarse-grained molecular understanding of reflectin assembly using a combination of experiments and simulations. We hypothesize that reflectin assembly and phase behavior can be explained from a remarkably simple colloidal model whereby individual protein monomers effectively interact via a short-range attractive and long-range repulsive (SA-LR) pair potential. We parameterize a coarse-grained SA-LR interaction potential for reflectin A1 from small-angle x-ray scattering measurements, and then extend it to a range of pH values using Gouy-Chapman theory to model monomer-monomer electrostatic interactions. The pH-dependent SA-LR interaction is then used in molecular dynamics simulations of reflectin assembly, which successfully capture a number of qualitative features of reflectin, including pH-dependent formation of discrete-sized nanoclusters and liquid-liquid phase separation at high pH, resulting in a putative phase diagram for reflectin. Importantly, we find that at low pH size-controlled reflectin clusters are equilibrium assemblies, which dynamically exchange protein monomers to maintain an equilibrium size distribution. These findings provide a mechanistic understanding of the equilibrium assembly of reflectin, and suggest that colloidal-scale models capture key driving forces and interactions to explain thermodynamic aspects of native reflectin behavior. Furthermore, the success of SA-LR interactions presented in this study demonstrates the potential of a colloidal interpretation of interactions and phenomena in a range of intrinsically disordered proteins.

Evidence of Lactobacillus strains shared between the female urinary and vaginal microbiota.

Lactobacillus species are common inhabitants of the 'healthy' female urinary and vaginal communities, often associated with a lack of symptoms in both anatomical sites. Given identification by prior studies of similar bacterial species in both communities, it has been hypothesized that the two microbiotas are in fact connected. Here, we carried out whole-genome sequencing of 49 Lactobacillus strains, including 16 paired urogenital samples from the same participant. These strains represent five different Lactobacillus species: L. crispatus, L. gasseri, L. iners, L. jensenii, and L. paragasseri. Average nucleotide identity (ANI), alignment, single-nucleotide polymorphism (SNP), and CRISPR comparisons between strains from the same participant were performed. We conducted simulations of genome assemblies and ANI comparisons and present a statistical method to distinguish between unrelated, related, and identical strains. We found that 50 % of the paired samples have identical strains, evidence that the urinary and vaginal communities are connected. Additionally, we found evidence of strains sharing a common ancestor. These results establish that microbial sharing between the urinary tract and vagina is not limited to uropathogens. Knowledge that these two anatomical sites can share lactobacilli in females can inform future clinical approaches.

AR-based human-in-the-loop manual assembly process evaluation with high-fidelity assembly simulation method

AR-based human-in-the-loop manual assembly process evaluation with high-fidelity assembly simulation method

Effect of flexible membrane in triaxial test on the mechanical behaviour of rockfill material using Discrete Element Method

The investigation of rockfill materials poses challenges due to their large particle size, associated high cost, and long laboratory testing duration. As a result, empirical correlations based on historical experimental studies are commonly used to design and analyse rockfill structures. However, the extensive use of rockfill in a wide range of applications and limited understanding of its mechanical behaviour emphasize the need for further research. These make it necessary to develop a robust technique capable of capturing key parameters such as particle shape and breakage, allowing for the simulation and study of large-scale assemblies with realistic boundary conditions. Given that the behaviour of rockfill is highly scale-dependent, primarily due to particle breakage, the simplified laboratory tests on the scaled-down assemblies can be misleading. Particle breakage is a fundamental phenomenon in the mechanical behaviour of rockfill and significantly affects shear strength, deformability, and porosity under different stress levels. The particle breakage is influenced by factors such as the rockfill’s maximum particle size, mineralogy, particle shape, gradation, and confining stresses. This study adopts a computationally efficient breakage method called the Modified Particle Replacement Method (MPRM) based on the Discrete Element Method. A Tile-Based Flexible Membrane (TBFM) for triaxial test modelling has been developed by employing segmental rectangular walls to create a deformable membrane. The effects of critical parameters, including particle shape, confining stress, membrane resolution, degree of flexibility, and the characteristic strength of the particles, are examined. The findings of the combined MPRM-TBFM approach demonstrate the significant influence of membrane flexibility on volumetric-related behaviour.Graphical

RI2AP: Robust and Interpretable 2D Anomaly Prediction in Assembly Pipelines.

Predicting anomalies in manufacturing assembly lines is crucial for reducing time and labor costs and improving processes. For instance, in rocket assembly, premature part failures can lead to significant financial losses and labor inefficiencies. With the abundance of sensor data in the Industry 4.0 era, machine learning (ML) offers potential for early anomaly detection. However, current ML methods for anomaly prediction have limitations, with F1 measure scores of only 50% and 66% for prediction and detection, respectively. This is due to challenges like the rarity of anomalous events, scarcity of high-fidelity simulation data (actual data are expensive), and the complex relationships between anomalies not easily captured using traditional ML approaches. Specifically, these challenges relate to two dimensions of anomaly prediction: predicting when anomalies will occur and understanding the dependencies between them. This paper introduces a new method called Robust and Interpretable 2D Anomaly Prediction (RI2AP) designed to address both dimensions effectively. RI2AP is demonstrated on a rocket assembly simulation, showing up to a 30-point improvement in F1 measure compared to current ML methods. This highlights its potential to enhance automated anomaly prediction in manufacturing. Additionally, RI2AP includes a novel interpretation mechanism inspired by a causal-influence framework, providing domain experts with valuable insights into sensor readings and their impact on predictions. Finally, the RI2AP model was deployed in a real manufacturing setting for assembling rocket parts. Results and insights from this deployment demonstrate the promise of RI2AP for anomaly prediction in manufacturing assembly pipelines.

Development of a dedicated process simulator for the digital twin in apparel manufacturing: a case study

PurposeThe purpose of this study is to introduce a dedicated simulator to automatically generate and simulate a balanced apparel assembly line, which is critical to the digital twin concept in apparel manufacturing. Given the low automation level in apparel manufacturing, this is a first step toward the implementation of a smart factory based on cyber-physical systems.Design/methodology/approachThe mixed task assignment algorithm was implemented to automatically generate a module-based apparel assembly line in the developed simulator. To validate the developed simulator, a case study was conducted using process analysis data of technical jackets obtained from an apparel manufacturer. The case study included three scenarios: calculating the number of workers, selecting orders based on factory capacity and managing unexpected worker absences.FindingsThe developed simulator is approximately 97.2% accurate in assigning appropriate tasks to workstations using the mixed task assignment algorithm. The simulator was also found to be effective in supporting decision-making for production planning, order selection and apparel assembly line management. In addition, the module-based line generation algorithm made it easy to modify the assembly line.Originality/valueThis study contributes a novel approach to address the challenge of low automation levels in apparel manufacturing by introducing a dedicated simulator. This dedicated simulator improves the efficiency of virtual apparel assembly line generation and simulation, which distinguishes it from existing commercial simulation software.

Upsampling Monte Carlo Reactor Simulation Tallies in Depleted Sodium-Cooled Fast Reactor Assemblies Using a Convolutional Neural Network

The computational demand of neutron Monte Carlo transport simulations can increase rapidly with the spatial and energy resolution of tallied physical quantities. Convolutional neural networks have been used to increase the resolution of Monte Carlo simulations of light water reactor assemblies while preserving accuracy with negligible additional computational cost. Here, we show that a convolutional neural network can also be used to upsample tally results from Monte Carlo simulations of sodium-cooled fast reactor assemblies, thereby extending the applicability beyond thermal systems. The convolutional neural network model is trained using neutron flux tallies from 300 procedurally generated nuclear reactor assemblies simulated using OpenMC. Validation and test datasets included 16 simulations of procedurally generated assemblies, and a realistic simulation of a European sodium-cooled fast reactor assembly was included in the test dataset. We show the residuals between the high-resolution flux tallies predicted by the neural network and high-resolution Monte Carlo tallies on relative and absolute bases. The network can upsample tallies from simulations of fast reactor assemblies with diverse and heterogeneous materials and geometries by a factor of two in each spatial and energy dimension. The network’s predictions are within the statistical uncertainty of the Monte Carlo tallies in almost all cases. This includes test assemblies for which burnup values and geometric parameters were well outside the ranges of those in assemblies used to train the network.

Blockchain as a Learning Tool: Analyzing Transaction Speeds at Various Gas Limits in Computer Assembly Simulations

Blockchain as a Learning Tool: Analyzing Transaction Speeds at Various Gas Limits in Computer Assembly Simulations

Limits of economy and fidelity for programmable assembly of size-controlled triply periodic polyhedra

We propose and investigate an extension of the Caspar-Klug symmetry principles for viral capsid assembly to the programmable assembly of size-controlled triply periodic polyhedra, discrete variants of the Primitive, Diamond, and Gyroid cubic minimal surfaces. Inspired by a recent class of programmable DNA origami colloids, we demonstrate that the economy of design in these crystalline assemblies-in terms of the growth of the number of distinct particle species required with the increased size-scale (e.g., periodicity)-is comparable to viral shells. We further test the role of geometric specificity in these assemblies via dynamical assembly simulations, which show that conditions for simultaneously efficient and high-fidelity assembly require an intermediate degree of flexibility of local angles and lengths in programmed assembly. Off-target misassembly occurs via incorporation of a variant of disclination defects, generalized to the case of hyperbolic crystals. The possibility of these topological defects is a direct consequence of the very same symmetry principles that underlie the economical design, exposing a basic tradeoff between design economy and fidelity of programmable, size controlled assembly.

On the use of neutron flux gradient with ANNs for the detection of diverted spent nuclear fuel

One of the main tasks in nuclear safeguards is regular inspections of Spent Nuclear Fuel (SNF) assemblies to detect possible diversions of special nuclear material such as 235U and 239Pu. In these inspections, characteristic signatures of SNF such as emissions of neutrons and gamma rays from the radioactive decay, are measured and their consistency with the declared assemblies is verified to ensure that no fuel pins have been removed. Research in this field is focused on both the development of detection equipment and methods for the analysis of the acquired measurement data. In this paper, the use of the neutron flux gradient, which is not considered in regular SNF verification, is investigated in combination with the scalar neutron flux as input to artificial neural network models for the quantification of fuel pins in SNF assemblies. The training and testing of these ANN models rely on a synthetic dataset that is generated from Monte Carlo simulations of a typical intact pressurized water reactor assembly and with different patterns of fuel pins replaced by dummy pins. The dataset consists of unique scenarios so that the ANN can be assessed over “unknown” cases that are not part of the learning phase. Results show that the neutron flux gradient is advantageous for a more accurate reconstruction of diversions within SNF assemblies.

Structure-Based Protein Assembly Simulations Including Various Binding Sites and Conformations.

Many biological functions are mediated by large complexes formed by multiple proteins and other cellular macromolecules. Recent progress in experimental structure determination, as well as in integrative modeling and protein structure prediction using deep learning approaches, has resulted in a rapid increase in the number of solved multiprotein assemblies. However, the assembly process of large complexes from their components is much less well-studied. We introduce a rapid computational structure-based (SB) model, GoCa, that allows to follow the assembly process of large multiprotein complexes based on a known native structure. Beyond existing SB Go̅-type models, it distinguishes between intra- and intersubunit interactions, allowing us to include coupled folding and binding. It accounts automatically for the permutation of identical subunits in a complex and allows the definition of multiple minima (native) structures in the case of proteins that undergo global transitions during assembly. The model is successfully tested on several multiprotein complexes. The source code of the GoCa program including a tutorial is publicly available on Github: https://github.com/ZachariasLab/GoCa. We also provide a web source that allows users to quickly generate the necessary input files for a GoCa simulation: https://goca.t38webservices.nat.tum.de.

Compensating thermal bending of the runner disk in hydrodynamic thrust bearings: Simple approach for passively improving the performance of gas bearings

This paper presents a method for passively compensating thermal bending distortions of the thrust disk in gas thrust bearings by passive approaches. Thermal bending displays a prominent problem in hydrodynamic thrust bearings, especially in connection with air bearings. Axial temperature gradients cause the rotor disk to dish which in turn diminishes the lubricating gap topology and reduces the load capacity of the bearing. Consequently, the bearing performance is decreased and the bearing may even fail. The here proposed straightforward solutions are small changes in the rotor and runner disk design and are therefore rather simple and inexpensive to adopt. They comprise asymmetric mass distributions, recesses, disk cuts, and dual-material rotor disks. In order to show the benefit for the thrust bearing performance, detailed multiphysical numerical simulations of a representative rotor assembly with a foil thrust bearing are presented. The fully coupled finite element model calculates the deformations of the foil package (2D Reissner–Mindlin type shell equations), lubricating air film pressure (2D compressible Reynolds equation), air film temperatures (3D energy equation), and thermo-elastic deformations of the rotor and rotor disk (2D Navier-Lamé equations including centrifugal forces and thermal stresses). When the proposed design changes are applied, the model predicts reduced thermal bending, marked improvements in load capacity as well as decreased temperatures. The load capacity increase ranges up to 43% for an optimized design when compared against a standard symmetric design. Also, the sensitivity of different design parameters is discussed in detail. The suggested disk design changes may be applied to a broad variety of rotor-bearing systems. However, the design has to be tailored to the specific and individual machine operating conditions.

Non-circular interference fit analysis in harmonic reducer based on sequential assembling and loading simulation

The flexible bearing is usually connected to the cam with an interference fit in harmonic reducers. Studies show that interference fit can cause changes in the structural parameters of the bearing and lead to changes in the service performance of the bearing and its supporting system. This paper proposes a general simulation method to investigate the influence of interference fit in harmonic reducers with the concept of the equivalent interference for non-circular fit between the flexible bearing and cam. Then, the four steps based on the sequential assembling and loading process are designed to ensure assembly concentricity and simulation convergence. The analysis results indicate that appropriately increasing the equivalent interference can improve the service life of the flexspline without degrading the meshing performance and, at the same time, increase the number of loaded balls to make the operation of the harmonic reducer more stable. Moreover, an optimal equivalent interference can be designed to ensure the harmonic reducer operates at its best, where the number of loaded balls and the maximum Mises stress of the inner ring appear inflection point, and the number of meshing tooth pairs reaches the maximum.

Measurement and Simulation of Mechanical Strength of Back-End-Of-Line Layer in Advanced CMOS Dies

The back-end-of-line (BEOL) layers of ICs are subjected to considerably high mechanical forces during processing and when operating in harsh conditions. Without proper design and flip chip process control, the forces induced from flip chip bumps may exceed the ultimate strength of the BEOL resulting in critical failures. It is therefore of high interest to have a measurement method to quantify the strength of the BEOL structure of a functional IC. Ultimate fracture stress values are extracted from these measurements which can be used in thermo-mechanical simulations of flip chip assemblies for survivability evaluations.

Development of Machine Tool Disassembly and Assembly Training and Digital Twin Model Building System based on Virtual Simulation

In response to the issues of poor safety and insufficient training equipment in traditional machine tool assembly and disassembly training, this paper proposes a machine tool assembly and disassembly training system based on virtual simulation. The system utilizes Unity 3D to construct an interactive virtual training environment, achieving highly simulated simulations and teaching of the machine tool assembly and disassembly process. Additionally, the system is capable of establishing communication with actual machine tools, achieving bidirectional mapping between digital and physical spaces, allowing learners to observe the dynamic responses of machine tool models through the virtual environment. This virtual-real interaction mechanism enriches the application scenarios of the system, introducing digital twin technology into the virtual simulation of machine tool assembly and disassembly training. This paper also provides a detailed introduction to the design and implementation of core technologies such as the system's overall architecture, scene modeling, and virtual-real interaction, offering significant support for intelligent teaching in machine tool assembly and disassembly.

Formalizing Coarse-Grained Representations of Anisotropic Interactions at Multimeric Protein Interfaces Using Virtual Sites.

Molecular simulations of biomacromolecules that assemble into multimeric complexes remain a challenge due to computationally inaccessible length and time scales. Low-resolution and implicit-solvent coarse-grained modeling approaches using traditional nonbonded interactions (both pairwise and spherically isotropic) have been able to partially address this gap. However, these models may fail to capture the complex anisotropic interactions present at macromolecular interfaces unless higher-order interaction potentials are incorporated at the expense of the computational cost. In this work, we introduce an alternate and systematic approach to represent directional interactions at protein-protein interfaces by using virtual sites restricted to pairwise interactions. We show that virtual site interaction parameters can be optimized within a relative entropy minimization framework by using only information from known statistics between coarse-grained sites. We compare our virtual site models to traditional coarse-grained models using two case studies of multimeric protein assemblies and find that the virtual site models predict pairwise correlations with higher fidelity and, more importantly, assembly behavior that is morphologically consistent with experiments. Our study underscores the importance of anisotropic interaction representations and paves the way for more accurate yet computationally efficient coarse-grained simulations of macromolecular assembly in future research.