Assembly Constraints Research Articles

Research on assembly constraints reconstruction based on Unreal Engine

Abstract In the process of digital twin modeling, the assembly constraints of the model in the Unreal Engine usually requires manual intervention. To realize the intelligence and high efficiency of digital twin modeling, an Unreal Engine based assembly constraints reconstruction method is proposed. The method explores the high-fidelity assembly constraints mapping from the modeling software to the Unreal Engine, divides the model in the modeling software into two parts: the entity structure and the assembly constraints, and reconstructs the assembly constraints in the Unreal Engine, to realize the transformation from the modeling software model to the Unreal Engine model. Finally, the proposed method is applied to the digital twin engineering of various institutions to verify its rapidity and effectiveness in practical application scenarios.

Estimating the relationship between South China and Gondwana based on big data analysis

The situation of South China within the ancient continent Gondwana remains a topic of discussion and controversy. The reconstruction of South China's linking to East Gondwana is crucial for understanding the evolutionary history of the Gondwana breakup, the growth of Asia, and the development of the Paleo-Tethys Ocean. Based on the newly established database of detrital zircons (https://repository.deep-time.org), this study integrates UPb age dating and Hf isotope analysis of detrital zircons from Early Paleozoic strata in South China. The aim is to investigate potential sources, provenance, and reconstruct its tectono-paleogeography during the early Paleozoic on the northeast margin of Gondwana. The UPb age distributions from detrital zircon populations predominantly reveal peaks in the Paleoproterozoic, Mid/Neoproterozoic, and Early Paleozoic eras, ca. 2480, 960, 830, 520, and 440 Ma, with a minor peak at approximately 1750 Ma. The significant temporal shift in the major age populations from approximately 2480 to 440 Ma reveals the global continental crustal growth of South China through a sequence of subduction and collision processes. These geodynamic processes contributed to the development of the mid-Paleozoic Kwangsian intraplate orogeny in the late early Paleozoic (ca. 440 Ma) in South China, ultimately leading to the final assembly of Gondwana. The latter orogeny is marked by regional unconformity between pre-Devonian and Devonian strata, accompanied by prevalent magmatic and metamorphic events. These earliest Proterozoic and mid-Paleozoic detrital zircon grains match with ages from India- Tethyan Himalaya region, but different from those from East Antarctica, western margin of Australia and Lhasa Block where Australia and India were detached by the Kuunga Ocean, suggesting assembly and paleogeographical constraints of South China. It fully shows that South China is located on the northeast margin of the Indian during early Paleozoic, which is of great significance for the reconstruction of the tectonic paleogeography of the continent.

Design of a Floating Vertical Axis Wind Turbine for Wind-Wave Basin Experiments

This paper presents the design and manufacturing of two novel small floating Darrieus vertical axis wind turbines (VAWT) developed for a wind-wave basin test campaign. As with typical designs, the rotor design needed to satisfy the traditional structural safety requirements (such as strain, deflection, resonant-free dynamics) from design standards along with other manufacturing and assembly constraints. In addition, for this particular design, some special conditions are present as the facility (the wind-wave basin itself) and use of existing Floating Offshore-wind and Controls Advanced Laboratory (FOCAL) semi-submersible floating platform (originally designed for HAWT test) imposed an additional set of design requirements including wind speed and size constraints, and specific, target overturning moments and rotor mass. Addressing all these constraints (facility, existing hull, structural safety, and manufacturing) presented a challenging design task in this case, thus the focus of this paper is presenting the design approach and results leading to final designs satisfying all these competing requirements. Using the presented design process, two Darrieus troposkein-shaped vertical axis wind turbines (two-bladed and three-bladed versions) were designed and manufactured, after ensuring compliance with all the design requirements. The presented study can aid researchers interested in developing similar floating turbine test campaigns.

Refining clinically relevant parameters for mis-splicing risk in shortened introns with donor-to-branchpoint space constraint

Intronic deletions that critically shorten donor-to-branchpoint (D-BP) distance of a precursor mRNA impose biophysical space constraint on assembly of the U1/U2 spliceosomal complex, leading to canonical splicing failure. Here we use a series of β-globin (HBB) gene constructs with intron 1 deletions to define D-BP lengths that present low/no risk of mis-splicing and lengths which are critically short and likely elicit clinically relevant mis-splicing. We extend our previous observation in EMD intron 5 of 46 nt as the minimal productive D-BP length, demonstrating spliceosome assembly constraint persists at D-BP lengths of 47-56 nt. We exploit the common HBB exon 1 β-thalassemia variant that strengthens a cryptic donor (NM_000518.5(HBB):c.79G > A) to provide a simple barometer for the earliest signs of space constraint, via cryptic donor activation. For clinical evaluation of intronic deletions, we assert D-BP lengths > 60 nt present low mis-splicing risk while space constraint increases exponentially with D-BP lengths < 55 nt, with critical risk and profound splicing abnormalities with D-BP lengths < 50 nt.

A novel method-based reinforcement learning with deep temporal difference network for flexible double shop scheduling problem

This paper studies the flexible double shop scheduling problem (FDSSP) that considers simultaneously job shop and assembly shop. It brings about the problem of scheduling association of the related tasks. To this end, a reinforcement learning algorithm with a deep temporal difference network is proposed to minimize the makespan. Firstly, the FDSSP is defined as the mathematical model of the flexible job-shop scheduling problem joined to the assembly constraint level. It is translated into a Markov decision process that directly selects behavioral strategies according to historical machining state data. Secondly, the proposed ten generic state features are input into the deep neural network model to fit the state value function. Similarly, eight simple constructive heuristics are used as candidate actions for scheduling decisions. From the greedy mechanism, optimally combined actions of all machines are obtained for each decision step. Finally, a deep temporal difference reinforcement learning framework is established, and a large number of comparative experiments are designed to analyze the basic performance of this algorithm. The results showed that the proposed algorithm was better than most other methods, which contributed to solving the practical production problem of the manufacturing industry.

Galaxy assembly revealed by globular clusters

Many observable properties of globular clusters (GCs) provide valuable insights for unveiling the hierarchical assembly of their host galaxy. For the Milky Way (MW) in particular, GCs from different accreted satellite galaxies show distinct chemical, spatial, kinematic, and age distributions. Here we examine such clustering features for model GC populations in simulated galaxies, which are carefully selected to match various observational constraints of the MW assembly. We evaluate several widely used clustering, dimensionality reduction, and supervised classification methods on these model GCs, using 10 properties that are observable in the MW. We can categorize in-situ and ex-situ formed GCs with about 90% accuracy, based solely on their clustering features in these 10 variables. The methods are also effective in distinguishing the last major merger in MW analogs with similar accuracy. Although challenging, we still find it possible to identify one, and only one, additional smaller satellite. We develop a new technique to classify the progenitors of MW GCs by combining several methods and weighting them by the validated accuracy. According to this technique, about 60% of GCs belong to the in-situ group, 20% are associated with the Gaia-Sausage/Enceladus event, and 10% are associated with the Sagittarius dwarf galaxy. The remaining 10% of GCs cannot be reliably associated with any single accretion event.

A modified memetic algorithm with multi-operation precise joint movement neighbourhood structure for the assembly job shop scheduling problem

This paper presents an adaptive memetic algorithm based on a new neighbourhood structure (AMA) for solving the assembly job shop scheduling problem, with the aim of minimising the maximum completion time (makespan). To utilise the knowledge of problem, a theoretical analysis is conducted to explore the criteria for feasible and effective movement of operations under assembly constraints, and a multi-operation precise joint movement neighbourhood structure is proposed accordingly. In the AMA, to ensure the feasibility of solutions during the evolution process, a feasible encoding mechanism based on the constraint degree of operations is designed, a greedy active decoding method as well as feasible crossover operation based on independent operation chains are designed specifically for this encoding method. To avoid premature convergence of the population, a population update operator with diversity adaptive control is proposed. Finally, by comparing the results with five state-of-the-art algorithms, the superiority of AMA in terms of solution quality and stability is verified, particularly with the update of known optimal solutions for 11 instances.

APLAN: open assembly planning framework in FreeCAD

In the latest decade, the consumer’s desire for personalisation has clearly taken of, challenging manufacturers to diversify their product portfolio while maintaining the same (or near) mass-produced prices. As a result, production is nowadays geared towards low volumes with high variety for which adaptive assembly systems are required. Hence, being able to automatically map out the assembly of complex products yields significant strategic advantages over competing manufacturers. Although a wide range of assembly sequencing algorithms have been developed in the past, researchers and companies still experience intellectual and financial thresholds to applying these software packages themselves. Either the algorithms have to be largely re-engineered from scientific literature, or are offered as part of costly commercial CAD software in the form of plugins and add-ons. However, in some cases, the developed source code is made publicly available via online collaboration platforms though in a variety of programming languages and often requiring the cumbersome installation of additional software libraries.Out of these needs, the proposed APLAN module arose: a software framework allowing developers to share their (dis)assembly planning algorithms in a centralised, uniform manner through the open-source 3D parametric modelling software FreeCAD. As impetus, several in-house developed algorithms for determining topological and geometrical (dis)assembly constraints are included as well as an academic (dis)assembly planner. Previously, such algorithms had to be executed via the terminal, after which the extensive liaison, blocking, and AND/OR graphs were outputted as text or static images. Through APLAN, users are now able to inspect and edit these graphs interactively and dynamically. To conclude, this manuscript finally reports on the benchmarking of the aforementioned connection and obstruction detectors, and their academic and industrial application for human-robot collaboration.

Dynamics Analysis Research of Large Pipe System of Launch Vehicle in Low Temperature

Assembly constraints and load conditions are very complicated for large-diameter pipe systems in low temperatures of launch vehicles. It might not be possible to simulate realistic flight conditions on ground tests. Malfunctions may occur if the vibration environment and constraint condition are not fully considered. Launch vehicles might burn and explode in serious conditions. The paper studies dynamics simulation and analysis methods of pipe systems by choosing stereotypes of large-diameter pipe systems in low temperatures, including the modeling and analysis for simulation. The comparison between experiment results and analysis outputs shows the validity of the modeling and analysis methods, offering technical support for the future simulation of pipe system loading conditions.

Multicomponent Topology Optimization Method Considering Stepwise Linear Assemblability with a Fictitious Physical Model

This paper proposes a multicomponent topology optimization method that considers assemblability. Generally, it is difficult to consider assemblability in topology optimization; however, in this study, we achieve it by introducing a fictitious physical model. To perform multicomponent topology optimization, the extended level set method is used to represent multiple components. First, the assembly constraints are formulated using a fictitious physical model limited to two components. Then, by considering stepwise assembly, the constraint is extended to three or more components. In addition, topology optimization algorithms are constructed using the finite element method. Several numerical examples demonstrate that the proposed method can obtain structures with assemblability and has low initial structure dependence.

Mitigation of assembly constraints for floating offshore wind turbines using discrete event simulation

There is a large and increasing pipeline of floating offshore wind projects with total global floating offshore wind capacity projected to grow year on year by, on average, between 59 and 104 % in the 2020s. This will lead to competition for infrastructure resources, in particular, port facilities for the construction and marshalling of the floating foundations and turbines. It is likely that multiple ports will need to be combined to provide the necessary fabrication capacity for a floating offshore wind farm of commercial scale. To enable an efficient and coordinated utilisation of multiple fabrication ports, it is crucial to understand the likely duration of different assembly and construction activities at different locations. However, at present this task is difficult to perform using top-down estimation models, as commercial-scale floating offshore wind farms comprising many tens of units have not been built to date. In this work we present a methodology, based on discrete event simulation (DES) and time series analysis, to produce an explicit simulation-based estimate of assembly activity durations, which are sensitive to setting specific factors. Three example case studies are outlined to demonstrate the ability to capture the variation in activity duration due to resource availability, and the season and location of activity. The methodology will be of use to project planners as it can be used at an early stage in the project life-cycle to appraise and adopt different construction strategies.

Effect of temperature rise on a tandem of ball-bearings with an O-configuration in motorized spindle under oil-air lubrication

Accurately predicting the thermal behavior of motorized spindles is a critical task in manufacturing industry. In this study, a thermo-mechanical-solid coupling-behavior on the motorized spindle in working condition was investigated and analyzed. Firstly, the bearing mechanical analysis of the motorized spindle under assembly constraints was discussed and a new coupling model was founded. Concurrently, the impacts of oil film thickness and the spacing rings on the displacement were considered into this model. Secondly, for the deformation of bearing, combined with Jones-Harris's quasi-static force–deformation bearing model, a mathematical model about a tandem of ball-bearings was built. And in this model, a new analytical method was proposed to decouple the deformation generated by thermal and centrifugal forces from the Hertz contact deformation, making the deformation analysis clear and orderly. Thirdly, from the perspective of heat generation mechanism and principle, based on the above bearing model, the elastohydrodynamic lubrication analysis on the bearing was executed, the thermal network model of bearing with multiple heat sources was set up, and the bearing node's scheme was optimized. Eventually, the effects of parameters such as speed, cooling water flow, and air flow on temperature rise and bearing parameters were calculated and analyzed, and the theoretical results were examined by tests, which provides some reference and guidance for the structural design of precision spindle.

Mathematical model and adaptive simulated annealing algorithm for mixed-model assembly job-shop scheduling with lot streaming

Mixed-model assembly and lot streaming are two key technologies to improve efficiency and reduce setups in a multi-variety and small-batch discrete manufacturing environment. Yet few studies consider the two technologies in assembly job-shop scheduling problems. Thus, this paper investigates the mixed-model assembly job-shop scheduling problem with lot streaming, which contains two closely coordinated stages with differentiated production lot size: the processing stage with lot streaming and the mixed-model assembly stage with single-piece flow. To tackle this problem, a mathematical model including new constraints i.e. production sequence constraints within/among sublots, sequence-dependent setup constraints between sublots and mixed-model assembly constraints, is developed to establish the temporal and spatial links between the processing and assembly stages. Besides, an adaptive simulated annealing algorithm is proposed. In this algorithm, a sublot sequence rule considering sublot and assembly features is discovered via gene expression programming to promote the performance of initial solutions; a Q-learning-based temperature control mechanism is designed to adaptively adjust the acceptance probability of inferior solutions and accordingly avoid local optima; an adaptive selection mechanism of neighborhood operators is designed to increase the chance of finding potential solutions; an adaptive adjustment mechanism of neighborhood size is put forward to balance computing resources and the coverage of neighborhood space. Experimental results show that both the discovered rule and the adaptive mechanisms are effective, and the developed algorithm is more adapt to fix the studied problem than other state-of-the-art algorithms.

Inference of phylogenetic trees directly from raw sequencing reads using Read2Tree

Current methods for inference of phylogenetic trees require running complex pipelines at substantial computational and labor costs, with additional constraints in sequencing coverage, assembly and annotation quality, especially for large datasets. To overcome these challenges, we present Read2Tree, which directly processes raw sequencing reads into groups of corresponding genes and bypasses traditional steps in phylogeny inference, such as genome assembly, annotation and all-versus-all sequence comparisons, while retaining accuracy. In a benchmark encompassing a broad variety of datasets, Read2Tree is 10–100 times faster than assembly-based approaches and in most cases more accurate—the exception being when sequencing coverage is high and reference species very distant. Here, to illustrate the broad applicability of the tool, we reconstruct a yeast tree of life of 435 species spanning 590 million years of evolution. We also apply Read2Tree to >10,000 Coronaviridae samples, accurately classifying highly diverse animal samples and near-identical severe acute respiratory syndrome coronavirus 2 sequences on a single tree. The speed, accuracy and versatility of Read2Tree enable comparative genomics at scale.

Single assembly sequence to flexible assembly plan by Autonomous Constraint Generation

The factory of the future is steering away from conventional assembly line production with sequential conveyor technology, towards flexible assembly lines, where products dynamically move between work-cells. Flexible assembly lines are significantly more complex to plan compared to sequential lines. Therefore there is an increased need for autonomously generating flexible robot-centered assembly plans. The novel Autonomous Constraint Generation (ACG) method presented here will generate a dynamic assembly plan starting from an initial assembly sequence , which is easier to program. Using a physics simulator, variations of the work-cell configurations from the initial sequence are evaluated and assembly constraints are autonomously deduced. Based on that the method can generate a complete assembly graph that is specific to the robot and work-cell in which it was initially programmed, taking into account both part and robot collisions. A major advantage is that it scales only linearly with the number of parts in the assembly. The method is compared to previous research by applying it to the Cranfield Benchmark problem. Results show a 93% reduction in planning time compared to using Reinforcement Learning Search. Furthermore, it is more accurate compared to generating the assembly graph from human interaction. Finally, applying the method to a real life industrial use case proves that a valid assembly graph is generated within reasonable time for industry. • ACG method autonomously generates all assembly sequences based on initial sequence. • Physics simulator evaluates deviations from initial sequence to deduce constraints. • Execution time scales only linearly with number of parts, states and skills. • Compared to previous research ACG is 93% faster and more accurate. • Industrial use case shows that ACG finds result within reasonable time for industry.

Autonomous discovery of emergent morphologies in directed self-assembly of block copolymer blends.

The directed self-assembly (DSA) of block copolymers (BCPs) is a powerful approach to fabricate complex nanostructure arrays, but finding morphologies that emerge with changes in polymer architecture, composition, or assembly constraints remains daunting because of the increased dimensionality of the DSA design space. Here, we demonstrate machine-guided discovery of emergent morphologies from a cylinder/lamellae BCP blend directed by a chemical grating template, conducted without direct human intervention on a synchrotron x-ray scattering beamline. This approach maps the morphology-template phase space in a fraction of the time required by manual characterization and highlights regions deserving more detailed investigation. These studies reveal localized, template-directed partitioning of coexisting lamella- and cylinder-like subdomains at the template period length scale, manifesting as previously unknown morphologies such as aligned alternating subdomains, bilayers, or a "ladder" morphology. This work underscores the pivotal role that autonomous characterization can play in advancing the paradigm of DSA.

Paper-based Structures &amp; Pop-Up Architecture: Challenges and Opportunities

Pop-up architecture encompasses ephemeral structures, built to be disassembled and reassembled. Among several available materials, paper is extensively used to build these temporary structures due to fabrication and sustainable reasons. Although by applying adequate production techniques paper can be used as a load-bearing component, its strength and durability might be compromised when exposed to environmental conditions. This paper aims at defining the challenges of designing and implementing paper-based structures, as well as at unveiling the potentials of using paper in pop-up architecture. In the first part, related literature and examples are reviewed to evaluate how rapid assembly, convenience of transportation, low-cost, less specialized labour in the production of components and in the assembly process, are parameters weighing in the choice of paper as structural material. The second part discusses selected case-studies, to show how paper-based components, connections, envelope insulation, transportation and assembly constraints are addressed to shape these temporary structures, thus becoming a source of inspiration for pop-up architecture.

A feature-based assembly information modeling method for complex products’ 3D assembly design

During the assembly design of complex products, the process data management is disordered and the data expression is not standardized, and depends mainly on the experience of technical personnel resulting in a lack of unified data source, low design efficiency, and low automation. This paper proposes a feature-based assembly information model to address these problem, providing standardized original design data for complex product 3D-assembly design. Taking the geometric feature of the mating faces as the study object, the overall structure of complex products and the information requirements of each assembly design stage were analyzed to construct the assembly feature information model. By focusing on the composition and expression form of information in the assembly feature information model, a geometric feature classification criterion applicable to assembly design is presented, and the matching rules of feature pairs, assembly relationship, and assembly constraint instructions are established. Based on these, assembly design information is extracted and expressed in a standardized form. As a validation, the modeling method was conducted on a marine diesel engine cylinder cap as a case study; the performance and applicability of the established rules and the extraction and expression methods were verified through the comparison with the assembly process of the cylinder cap.

Insights on the evolution of Coronavirinae in general, and SARS-CoV-2 in particular, through innovative biocomputational resources.

The structural proteins of coronaviruses portray critical information to address issues of classification, assembly constraints, and evolutionary pathways involving host shifts. We compiled 173 complete protein sequences from isolates belonging to the four genera of the subfamily Coronavirinae. We calculate a single matrix of viral distance as a linear combination of protein distances. The minimum spanning tree (MST) connecting the individuals captures the structure of their similarities. The MST re-capitulates the known phylogeny of Coronovirinae. Hosts were mapped onto the MST and we found a non-trivial concordance between host phylogeny and viral proteomic distance. We also study the chimerism in our dataset through computational simulations. We found evidence that structural units coming from loosely related hosts hardly give rise to feasible chimeras in nature. This work offers a fresh way to analyze features of SARS-CoV-2 and related viruses.

Investigating workspace and positional accuracy in 3 Degrees of Freedom planar manipulator under the link tolerances

Parallel manipulators are playing a significant role in robotic applications for the last few decades due to their merits over the serial manipulators. In this paper, kinematic analysis for the 3-RPR planar parallel manipulator having three degrees of freedom through a simple geometric approach is presented. The aim of the study presented here consist of two parts: improving the reachable workspace and investigating the positional error at platform due to link tolerance. The reachable workspace is estimated at various magnification ratios of manipulator at different position and orientation of tool point. The geometric approach is applied to calculate the workspace. The validation of kinematic results is carried out through the Computer-Aided Drafting (CAD) model. The positional error at platform and deviation in workspace due to tolerance on the link length are studied. The link tolerance results due to manufacturing and assembly constraints. The positional error for variation in link tolerance is presented. The proposed method is capable of quantification of maximum reachable workspace as well as worst error for the given tolerance value. The manipulator with highest possible accuracy over largest workspace area can be obtained applying proposed method as an optimized design.