Assembly System Research Articles

Deep learning-based augmented reality work instruction assistance system for complex manual assembly

The manual assembly process of complex products is lengthy, the assembly requirements are difficult to recall, and the assembly quality requirements are high. The separation of the operation guidance information from the real physical object in the traditional paper manual easily distracts the operator, increasing their cognitive burden. To address this issue, we integrate augmented reality (AR) and artificial intelligence (AI) technologies for manual assembly assistance. A novel encoding and decoding convolutional neural network (CNN) is built to realize accurate AR registration under mark-less assembly environment. An assembly quality inspection method integrating a neural network and virtual model matching was proposed for AR systems. The specific process of the proposed method includes two stages: offline and online. In the first stage, a monocular RGB image dataset was built for assembling object keypoints and assembly object detection. CNN models were designed and trained for deployment in an AR-assisted assembly system. Second, the deployed CNN models were used to perform AR registration and assembly quality inspection. Experimental results demonstrate that the proposed method can accurately present AR work instruction guidance and assembly quality inspection for manual assembly. The proposed AR registration method can achieve a frame rate of 25FPS, which satisfies the timeliness requirements of the AR system. The accuracy rate of the proposed assembly quality inspection method for the MONA component assembly reached 92.5%.

CIAO1 and MMS19 deficiency: A lethal neurodegenerative phenotype caused by cytosolic Fe-S cluster protein assembly disorders

PurposeThe functionality of many cellular proteins depends on cofactors; yet, they have only been implicated in a minority of Mendelian diseases. Here, we describe the first 2 inherited disorders of the cytosolic iron-sulfur protein assembly system. MethodsGenetic testing via genome sequencing was applied to identify the underlying disease cause in 3 patients with microcephaly, congenital brain malformations, progressive developmental and neurologic impairments, recurrent infections, and a fatal outcome. Studies in patient-derived skin fibroblasts and zebrafish models were performed to investigate the biochemical and cellular consequences. ResultsMetabolic analysis showed elevated uracil and thymine levels in body fluids but no pathogenic variants in DPYD, encoding dihydropyrimidine dehydrogenase. Genome sequencing identified compound heterozygosity in 2 patients for missense variants in CIAO1, encoding cytosolic iron-sulfur assembly component 1, and homozygosity for an in-frame 3-nucleotide deletion in MMS19, encoding the MMS19 homolog, cytosolic iron-sulfur assembly component, in the third patient. Profound alterations in the proteome, metabolome, and lipidome were observed in patient-derived fibroblasts. We confirmed the detrimental effect of deficiencies in CIAO1 and MMS19 in zebrafish models. ConclusionA general failure of cytosolic and nuclear iron-sulfur protein maturation caused pleiotropic effects. The critical function of the cytosolic iron-sulfur protein assembly machinery for antiviral host defense may well explain the recurrent severe infections occurring in our patients.

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.

An aircraft assembly process formalism and verification method based on semantic modeling and MBSE

The aircraft assembly system is highly complex involving different stakeholders from multiple domains. The design of such a system requires comprehensive consideration of various industrial scenarios aiming to optimize key performance indicators. Traditional design methods heavily rely on domain expert knowledge using documents to define assembly solutions which are later verified through simulations. However, these document-centric approaches cannot provide graphical notations for engineers to efficiently understand the entire assembly process. Moreover, it is difficult to analyze the performance of the designed assembly processes using simulations since the simulation models have to be developed based on the documents manually rather than be generated automatically from the design models. In this paper, a semantic-driven approach is proposed to support aircraft assembly process formalism and performance analysis. First, meta-models of aircraft assembly processes are developed based on SysML and discrete-event simulation models using a semantic modeling language named KARMA. Then an application ontology is defined for generating semantic models from KARMA architecture models to capture domain knowledge, system requirements and simulation model information of the aircraft assembly process. A model transformer is developed to transform the KARMA models to discrete-event simulation models based on the application ontology. Then the generated simulation models are executed to obtain the simulation results for verifying the designed assembly process. Finally, the obtained simulation results are used to support decision-making of selecting the optimal aircraft assembly process. A case study is conducted to verify the proposed method.

Harnessing Catalytic RNA Circuits for Construction of Artificial Signaling Pathways in Mammalian Cells.

Engineering of genetic networks with artificial signaling pathways (ASPs) can reprogram cellular responses and phenotypes under different circumstances for a variety of diagnostic and therapeutic purposes. However, construction of ASPs between originally independent endogenous genes in mammalian cells is highly challenging. Here we report an amplifiable RNA circuit that can theoretically build regulatory connections between any endogenous genes in mammalian cells. We harness the system of catalytic hairpin assembly with combination of controllable CRISPR-Cas9 function to transduce the signals from distinct messenger RNA expression of trigger genes into manipulation of target genes. Through introduction of these RNA-based genetic circuits, mammalian cells are endowed with autonomous capabilities to sense the changes of RNA expression either induced by ligand stimuli or from various cell types and control the cellular responses and fates via apoptosis-related ASPs. Our design provides a generalized platform for construction of ASPs inside the genetic networks of mammalian cells based on differentiated RNA expression.

Harnessing Catalytic RNA Circuits for Construction of Artificial Signaling Pathways in Mammalian Cells

AbstractEngineering of genetic networks with artificial signaling pathways (ASPs) can reprogram cellular responses and phenotypes under different circumstances for a variety of diagnostic and therapeutic purposes. However, construction of ASPs between originally independent endogenous genes in mammalian cells is highly challenging. Here we report an amplifiable RNA circuit that can theoretically build regulatory connections between any endogenous genes in mammalian cells. We harness the system of catalytic hairpin assembly with combination of controllable CRISPR‐Cas9 function to transduce the signals from distinct messenger RNA expression of trigger genes into manipulation of target genes. Through introduction of these RNA‐based genetic circuits, mammalian cells are endowed with autonomous capabilities to sense the changes of RNA expression either induced by ligand stimuli or from various cell types and control the cellular responses and fates via apoptosis‐related ASPs. Our design provides a generalized platform for construction of ASPs inside the genetic networks of mammalian cells based on differentiated RNA expression.

Impact of Local Microenvironments on the Selectivity of Electrocatalytic Nitrate Reduction in a BPM‐MEA System

AbstractElectrochemical nitrate reduction reaction (NO3RR) has garnered increasing attention as a pathway for converting a harmful pollutant (nitrate) into a value‐added product (ammonia). However, high selectivity toward ammonia (NH3) is imperative for process viability. Optimizing proton availability near the catalyst is important for achieving selective NH3 production. Here, the aim is to systematically examine the impacts of proton availability on NO3RR selectivity in a bipolar membrane (BPM)‐based membrane electrode assembly (MEA) system. The BPM generates a proton flux from the membrane toward the catalyst during electrolysis. Thus, the BPM‐MEA system can modulate the proton flux during operation. The impact of interposer layers, proton scavenging electrolytes (CO32−), and catalyst configurations are also examined to identify which local microenvironments favor ammonia formation. It is found that a moderate proton supply allows for an increase in ammonia yield by 576% when compared to the standard MEA setup. This also results in a high selectivity of 26 (NH3 over NO2−) at an applied current density of 200 mA cm−2.

Energy-efficient scheduling model and method for assembly blocking permutation flow-shop in industrial robotics field

Implementing green and sustainable development strategies has become essential for industrial robot manufacturing companies to fulfill their societal obligations. By enhancing assembly efficiency and minimizing energy consumption in workshops, these enterprises can differentiate themselves in the fiercely competitive market landscape and ultimately bolster their financial gains. Consequently, this study focuses on examining the collaborative assembly challenges associated with three crucial parts: the body, electrical cabinet, and pipeline pack, within the industrial robot manufacturing process. Considering the energy consumption during both active and idle periods of the industrial robot workshop assembly system, this paper presents a multi-stage energy-efficient scheduling model to minimize the total energy consumption. Two classes of heuristic algorithms are proposed to address this model. Our contribution is the restructuring of the existing complex mathematical programming model, based on the structural properties of scheduling sub-problems across multiple stages. This reformation not only effectively reduces the variable scale and eliminates redundant constraints, but also enables the Gurobi solver to tackle large-scale problems. Extensive experimental results indicate that compared to traditional workshop experience, the constructed green scheduling model and algorithm can provide more precise guidance for the assembly process in the workshop. Regarding total energy consumption, the assembly plans obtained through our designed model and algorithm exhibit approximately 3% lower energy consumption than conventional workshop experience-based approaches.

Cross-catenation between position-isomeric metallacages

The study of cross-catenated metallacages, which are complex self-assembly systems arising from multiple supramolecular interactions and hierarchical assembly processes, is currently lacking but could provide facile insights into achieving more precise control over low-symmetry/high-complexity hierarchical assembly systems. Here, we report a cross-catenane formed between two position-isomeric Pt(II) metallacages in the solid state. These two metallacages formed [2]catenanes in solution, whereas a 1:1 mixture selectively formed a cross-catenane in crystals. Varied temperature nuclear magnetic resonance experiments and time-of-flight mass spectra are employed to characterize the cross-catenation in solutions, and the dynamic library of [2]catenanes are shown. Additionally, we searched for the global-minimum structures of three [2]catenanes and re-optimized the low-lying structures using density functional theory calculations. Our results suggest that the binding energy of cross-catenanes is significantly larger than that of self-catenanes within the dynamic library, and the selectivity in crystallization of cross-catenanes is thermodynamic. This study presents a cross-catenated assembly from different metallacages, which may provide a facile insight for the development of low-symmetry/high-complexity self-assemble systems.

Decentralization and Local Government Performance: Empirical Evidence From Ghana

Development agencies often promote decentralization as a solution for poverty reduction and a means of addressing the unique needs of local populations in developing countries. This manuscript examines the District Assembly system’s effectiveness in delivering services within the Bawku Municipality in the Upper East Region of Ghana. The study employs a mixed-methods approach to illuminate two contrasting perspectives on the local assembly’s service provision. On one hand, citizens generally express satisfaction with the delivery of services related to health infrastructure, education, electricity, and water supply. On the other hand, a significant portion of the population is dissatisfied with the local assembly’s performance in governance, sanitation, gender-related issues, and addressing vulnerability. Notably, the study also uncovers that despite the intended objective of decentralization to empower local citizens, a degree of central government control and influence undermines its effectiveness. This study presents practical and theoretical implications and identifies avenues for future research in this field.

High-quality genome assembly and genetic transformation system of Lasiodiplodia theobromae strain LTTK16-3, a fungal pathogen of Chinese hickory.

Lasiodiplodia theobromae, as one of the causative agents associated with Chinese hickory trunk cankers, has caused huge economic losses to the Chinese hickory industry. Although the biological characteristics of this pathogen and the occurrence pattern of this disease have been well studied, few studies have addressed the related mechanisms due to the poor molecular and genetic study basis of this fungus. In this study, we sequenced and assembled L. theobromae strain LTTK16-3, isolated from a Chinese hickory tree (cultivar of Linan) in Linan, Zhejiang province, China. Phylogenetic analysis and comparative genomics analysis presented crucial cues in the prediction of LTTK16-3, which shared similar regulatory mechanisms of transcription, DNA replication, and DNA damage response with the other four Chinese hickory trunk canker-associated Botryosphaeria strains including, Botryosphaeria dothidea, Botryosphaeria fabicerciana, Botryosphaeria qingyuanensis, and Botryosphaeria corticis. Moreover, it contained 18 strain-specific protein clusters (not conserved in the other L. theobromae strains, AM2As and CITRA15), with potential roles in specific host-pathogen interactions during the Chinese hickory infection. Additionally, an efficient system for L. theobromae protoplast preparation and polyethylene glycol (PEG) -mediated genetic transformation was firstly established as the foundation for its future mechanisms study. Collectively, the high-quality genome data and the efficient transformation system of L. theobromae here set up the possibility of targeted molecular improvements for Chinese hickory canker control.IMPORTANCEFungi with disparate genomic features are physiologically diverse, possessing species-specific survival strategies and environmental adaptation mechanisms. The high-quality genome data and related molecular genetic studies are the basis for revealing the mechanisms behind the physiological traits that are responsible for their environmental fitness. In this study, we sequenced and assembled the LTTK16-3 strain, the genome of Lasiodiplodia theobromae first obtained from a diseased Chinese hickory tree (cultivar of Linan) in Linan, Zhejiang province, China. Further phylogenetic analysis and comparative genomics analysis provide crucial cues in the prediction of the proteins with potential roles in specific host-pathogen interactions during the Chinese hickory infection. An efficient PEG-mediated genetic transformation system of L. theobromae was established as the foundation for the future mechanisms exploration. The above genetic information and tools set up valuable clues to study L. theobromae pathogenesis and assist in Chinese hickory canker control.

Solving line balancing and AGV scheduling problems for intelligent decisions using a Genetic-Artificial bee colony algorithm

Due to the rapid advancement of technology, the demand for electronic devices in various sectors such as consumer electronics, automotive, telecommunications, healthcare, and industrial applications, as well as customized printed circuit board (PCB) products, has significantly increased. Balancing the PCB assembly line is crucial for improving productivity to meet market demand, which means balancing the workload distributed among various assembly stations on the assembly line. However, the workload balancing depends on material-handling systems in production environments that facilitate the transportation of raw materials to the buffer storage of the assembly stations. In the PCB assembly system, the material handling is carried out using automated guided vehicles (AGVs). This paper studies the assembly line balancing and AGV scheduling collectively because of their dependency on each other. A mixed integer linear programming (MILP) model is formulated to balance the workload distribution among stations and schedule the AGVs. An existing intelligent platform of the real-life PCB industry is considered to solve this integrated problem with intelligent decisions. The platform includes three layers modules: physical layer, data management layer and application service layer. A novel genetic artificial bee colony (GABC) algorithm is proposed and embedded with an application service layer to optimize the current problem and give optimum solutions for the real-life physical layer. The proposed GABC algorithm incorporates the operators of the genetic algorithm (GA) i.e., crossover and mutation into the search process of the ABC algorithm. Additionally, the greedy selection feature is employed in GABC which significantly enhances the exploitation capabilities, leading to faster convergence, higher-quality solutions, and improved robustness. The performance of the proposed GABC algorithm is tested based on different parameters by comparing the results with GA, ABC and PSO algorithms. Experimental analysis indicates that the proposed GABC algorithm outperforms the compared algorithms. Therefore, the yielded solutions in terms of prompt responses help managers to expedite the production through intelligent decision-making based on customer demands.

THE USE OF DATABASE IN THE PLANNING OF ASSEMBLY WORKPLACES AND SYSTEMS

In the context of advancing technologies and industrial modernization, attention must be directed not only towards production technology but also assembly processes. This article aims to formulate a conceptual solution for rationalizing assembly workplaces, creating an integrated methodology and tools. The theoretical segment reviews the current state of assembly workplaces and systems. An analysis based on predefined criteria reveals the need for a classification system. The practical section proposes a methodology leveraging databases, algorithms, and simulation. A crucial aspect is an electronic database, a key component for rationalizing assembly workplaces and systems in both new and existing production systems.

Rapid Design of an ICU Ventilator: An Approach Based on Smart Switching of Compressed Air-Oxygen.

Breathing support is provided by regulating volume or pressure of lungs using ventilators. Due to COVID-19 pandemic, there was a sudden shortage of resuscitating devices such as ventilators. Additionally, ventilators being one of the primary critical care devices are also very costly. In order to address this situation, a number of low-cost designs have been proposed, however, many of these lack either an efficient control system or a hardware comparable to a standard ICU ventilator. In this regard, this article presents a comprehensive cost-effective solution that covers all aspects of the ventilator design (named as NED-Vent) such as hardware/pneumatic assembly, electronic design, user interface and control system. The proposed design works on compressed air-oxygen switching via proportional valves to produce basic volume and pressure modes as well as their derivatives such as assist ventilation, intermittent ventilation and spontaneous modes. The NED-Vent also features an interactive single-knob-single-touch user interface along with an automated mechanism for adjusting air-oxygen ratio in breathing gas mixture as an improvement on existing designs. The pressure regulated control is based on two mathematical models of human lungs with dynamic lung parameters estimated using machine learning approach. Additionally, the controller is tuned to optimized stability using Jury's Test and Ziegler-Nichols methods. The obtained results of breath profile are validated for the employed mathematical models (SLSC & SLMC) as well as the tuning methods in compliance with the tolerances provided by international standards.

Chemically Conjugated Branched Staples for Super-DNA Origami.

DNA origami, comprising a long folded DNA scaffold and hundreds of linear DNA staple strands, has been developed to construct various sophisticated structures, smart devices, and drug delivery systems. However, the size and diversity of DNA origami are usually constrained by the length of DNA scaffolds themselves. Herein, we report a new paradigm of scaling up DNA origami assembly by introducing a novel branched staple concept. Owing to their covalent characteristics, the chemically conjugated branched DNA staples we describe here can be directly added to a typical DNA origami assembly system to obtain super-DNA origami with a predefined number of origami tiles in one pot. Compared with the traditional two-step coassembly system (yields <10%), a much greater yield (>80%) was achieved using this one-pot strategy. The diverse superhybrid DNA origami with the combination of different origami tiles can be also efficiently obtained by the hybrid branched staples. Furthermore, the branched staples can be successfully employed as the effective molecular glues to stabilize micrometer-scale, super-DNA origami arrays (e.g., 10 × 10 array of square origami) in high yields, paving the way to bridge the nanoscale precision of DNA origami with the micrometer-scale device engineering. This rationally developed assembly strategy for super-DNA origami based on chemically conjugated branched staples presents a new avenue for the development of multifunctional DNA origami-based materials.

Multi agent reinforcement learning for online layout planning and scheduling in flexible assembly systems

AbstractManufacturing systems are undergoing systematic change facing the trade-off between the customer's needs and the economic and ecological pressure. Especially assembly systems must be more flexible due to many product generations or unpredictable material and demand fluctuations. As a solution line-less mobile assembly systems implement flexible job routes through movable multi-purpose resources and flexible transportation systems. Moreover, a completely reactive rearrangeable layout with mobile resources enables reconfigurations without interrupting production. A scheduling that can handle the complexity of dynamic events is necessary to plan job routes and control transportation in such an assembly system. Conventional approaches for this control task require exponentially rising computational capacities with increasing problem sizes. Therefore, the contribution of this work is an algorithm to dynamically solve the integrated problem of layout optimization and scheduling in line-less mobile assembly systems. The proposed multi agent deep reinforcement learning algorithm uses proximal policy optimization and consists of a decoder and encoder, allowing for various-sized system state descriptions. A simulation study shows that the proposed algorithm performs better in 78% of the scenarios compared to a random agent regarding the makespan optimization objective. This allows for adaptive optimization of line-less mobile assembly systems that can face global challenges.

Human-robot collaborative task planning for assembly system productivity enhancement

PurposeIn the current era of Industry 4.0, the manufacturing industries are striving toward mass production with mass customization by considering human–robot collaboration. This study aims to propose the reconfiguration of assembly systems by incorporating multiple humans with robots using a human–robot task allocation (HRTA) to enhance productivity.Design/methodology/approachA human–robot task scheduling approach has been developed by considering task suitability, resource availability and resource selection through multicriteria optimization using the Linear Regression with Optimal Point and Minimum Distance Calculation algorithm. Using line-balancing techniques, the approach estimates the optimum number of resources required for assembly tasks operating by minimum idle time.FindingsThe task allocation schedule for a case study involving a punching press was solved using human–robot collaboration, and the approach incorporated the optimum number of appropriate resources to handle different types of proportion of resources.Originality/valueThis proposed work integrates the task allocation by human–robot collaboration and decrease the idle time of resource by integrating optimum number of resources.

On-line posture measurement for automatic positioning of foldable-wing with priority-based multi-constraints

The digital assembly system is widely used in the automatic positioning of large aircraft components. Accurate posture measurement of the components remains challenging for high-precision assembly of the components with various engineering constraints. Existing methods often require repeated measurements of key features and are susceptible to the pronounced coupled effect, where various constraints mutually influence the solution of pose parameters. To enhance the efficiency and precision of posture measurement, this study proposes an on-line measurement method for the Foldable-wing Digital Assembly System. The key features and several reference points are measured once by the laser tracker. According to the assembly task sequence, a novel posture evaluation model with priority-based multi-constraints is established to calibrate these reference points which will be utilized as the new datum of posture measurement instead of the traditional key features. This model separately satisfies the various engineering constraints, such as coaxiality, gap, and basic positioning accuracy, in order of priority to avoid the coupling effect between different types of constraints. Consequently, features with higher priority can achieve higher accuracy without weight setting. Next, the reference points are automatically measured to calculate the posture parameters. Considering the uncertainty characteristics of the measuring instrument, the registration algorithm based on weighted total least squares adjustment is applied to reduce measurement uncertainty. Monte Carlo simulation results demonstrate the proposed method's ability to enhance positioning accuracy and reduce posture uncertainty, outperforming conventional methods. Finally, the practical application was successfully implemented, and all key features remained within tolerance.

Local geometric edge features based registration for textureless object in augmented reality assisted assembly

Image-based methods have been widely used in augmented reality (AR) assistant assembly systems. However, due to the lack of sufficient texture information on the surface of assembly part, traditional image feature matching methods still face challenges. This paper proposes a coarse-to-fine AR registration method for textureless assembly part. In the first stage, a new feature matching method which is called line neighborhood edge descriptor (LNED) is presented to find the coarse camera pose from textureless image. The LNED take the contour line of assembly part as the description object, and use local geometric edge of assembly part to describe the contour line. During the image matching, the binary encoding is used to reduce the computational consumption for LNED. In the second stage, spatial points in the CAD model of assembly part are reverse projected to the textureless image based on the coarse camera pose. And the bundle adjustment method based on the edge distance of the textureless image is adopted to iteratively calculate the precise camera pose. In the experimental evaluation, the proposed registration method shows high accuracy and fast speed in comparison with conventional registration methods, which demonstrates that our method can effectively solve the problem of AR registration for textureless assembly part.

Parametric reduced-order modeling for component-oriented treatment and localized nonlinear feature inclusion

We propose coupling a physics-based reduction framework with a suited response decomposition technique to derive a component-oriented reduction (COR) approach, which is suitable for assembly systems featuring localized nonlinearities. Dependencies on influencing parameters are injected into the reduced-order model (ROM), thus ensuring robustness and validity over a domain of parametric inputs, while capturing nonlinear effects. The implemented approach employs individual component modes to capture localized features while additionally relying on reduced modes of a global nature to approximate the system’s dynamics accurately. The global modes are derived from a linear monolithic system, defined as a result of a coordinate separation scheme, which permits the proposed COR-ROM to naturally couple the response between linear and nonlinear subdomains. The derived low-order representation utilizes a proper orthogonal decomposition projection and is additionally reinforced with the inclusion of a hyper-reduction technique to capture the underlying high-fidelity model response while providing accelerated computations. The resulting approach is exemplified in the synthetic case studies of a four-story shear frame with multiple nonlinear regions driven by hysteresis and a large-scale kingpin connection featuring plasticity.