Flexible Assembly Research Articles

A Two-Individual-Based Evolutionary Algorithm for Flexible Assembly Job Shop Scheduling Problem with Uncertain Interval Processing Times

The assembly job shop is a prevalent production organization mode in manufacturing enterprises. During the processing and assembly of products, operation processing times are influenced by numerous factors, leading to significant uncertainty. This paper investigates the flexible assembly job shop scheduling problem (FAJSP) with uncertain processing times, where processing times are represented as variable interval numbers. We develop a robust optimization model for the FAJSP, utilizing confidence level estimation to determine the ranges of processing times and reformulating the model based on the chance-constrained method. A two-individual-based master–apprentice evolutionary (MAE) algorithm is proposed. Two effective encoding schemes are designed to prevent the generation of infeasible solutions under assembly sequence constraints. Additionally, a decoding method based on interval scheduling theory is devised to accurately represent interval processing times. Case studies are conducted to validate the effectiveness of the proposed robust optimization model and demonstrate the superiority of the MAE algorithm.

Construction of a Digital Twin System and Dynamic Scheduling Simulation Analysis of a Flexible Assembly Workshops with Island Layout

Assembly Workshops with Island Layout (AWIL) possess flexible production capabilities that realize product diversification. To cope with the complex scheduling challenges in flexible workshops, improve resource utilization, reduce waste, and enhance production efficiency, this paper proposes a production scheduling method for flexible assembly workshops with an island layout based on digital twin technology. A digital twin model of the workshop is established according to production demands to simulate scheduling operations and deal with complex scheduling issues. A workshop monitoring system is developed to quickly identify abnormal events. By employing an event-driven rolling-window rescheduling technique, a dynamic scheduling service system is constructed. The rolling window decomposes scheduling problems into consecutive static scheduling intervals based on abnormal events, and a genetic algorithm is used to optimize each interval in real time. This approach provides accurate, real-time scheduling decisions to manage disturbances in workshop production, which can enhance flexibility in the production process, and allows rapid adjustments to production plans. Therefore, the digital twin system improves the sustainability of the production system, which will provide a theoretical research foundation for the real-time and unmanned production scheduling process, thereby achieving sustainable development of production.

Selenium(II)-Nitrogen Exchange (SeNEx) Chemistry: A Good Chemistry Suitable for Nanomole-Scale Parallel Synthesis, DNA-encoded Library Synthesis and Bioconjugation.

The continuous development of click reactions with new connecting linkage is crucial for advancing the frontiers of click chemistry. Selenium-nitrogen exchange (SeNEx) chemistry, a versatile chemistry in click chemistry, represents an all-encompassing term for nucleophilic substitution events that replace nitrogen at an electrophilic selenium(II) center, enabling the flexible and efficient assembly of linkages around a Se(II) core. Several SeNEx chemistries have been developed inspired by the biochemical reaction between Ebselen and cysteine residue, and demonstrated significant potential in on-plate nanomole-scale parallel synthesis, selenium-containing DNA-encoded library (SeDEL) synthesis, as well as peptide and protein bioconjugation. This concept aims to present the origins, advancements, and applications of selenium(II)-nitrogen exchange (SeNEx) chemistry while also outlining the potential directions for future research in this field.

Graduation-inspired manufacturing system and synchronization mechanism for hybrid assembly cell lines

This paper presents an innovative flexible assembly system – hybrid assembly cell lines (HACL) to adapt to small batch, high product variety and dynamic demands. HACL provides specially designed trolleys for assembly operations, materials and tools to form the workstations flexibly and quickly at low reconfiguration cost. To conduct accurate and effective control for this complex assembly system, this paper presents a modified Graduation-inspired Manufacturing System (GMS), a recently proposed new card-based control system. GMS for HACL designs three kinds of tickets and the ticketing mechanism to absorb the uncertainties of HACL. Job tickets are for workload control and synchronization of part deliveries; setup tickets are for cell line formation and synchronization of resources of tools and machines; operation tickets are for production execution and synchronization of workers and operations in cell lines. GMS for HACL develops Internet of Things-enabled (IoT-enabled) architecture for managing the smart tickets to achieve automatic and intelligent interoperability between GMS and the physical workstations of HACL. This work designs synchronization mechanism based on specific model and genetic algorithm to synchronize manufacturing resources under GMS for HACL

A constraint programming approach for resource-constrained flexible assembly flow shop scheduling problem with batch direct delivery

Production and distribution are both crucial components of supply chains. Integrated production and distribution scheduling (IPDS) in the context of flexible assembly flow shop scheduling and batch delivery problems is often overlooked. A realistic problem inspired by the production and distribution processes of a dishwasher factory can be modeled as a resource-constrained flexible assembly flow shop scheduling problem with batch direct delivery (RCFAFSP-BDD). In this problem, order requirements are decomposed into several production tasks processed at different stages of the workshop, and are then delivered in batches via a third-party logistics provider to regional distributors at various locations. Auxiliary resource restrictions, hierarchical coupling constraints, machine eligibility restrictions and sequence-dependent setup times, are incorporated into the problem as operational constraints. To the best of our knowledge, this is the first attempt to solve this problem. This work formulates a mixed-integer linear programming (MIP) model to minimize total costs, including tardiness, inventory, and delivery costs. Given the problem’s strong NP-hard nature, the focus is on developing an efficient solution approach using constraint programming (CP). A CP model is proposed and enhanced with multiple redundant constraints. To reduce runtime, two branching strategies are designed for the CP model. Numerical experiments with varying instance scales reveal that the proposed CP model outperforms the MIP model in accuracy and efficiency within the given time limit. The redundant constraints and search strategy can reduce CP model runtime by up to 263.83%. Compared to manual scheduling at the studied factory, the CP model can cut costs by up to 26.59% for real data, offering viable alternatives for factory planners.

Reconfigurable flexible assembly model and implementation for cross-category products

As the production orders are becoming multi-category and small-batch in the era of product personalization, these require frequent reconfiguration of reconfigurable flexible assembly system for cross-category products (RFAS-CCP). However, there is no suitable theoretical assembly model and systematic implementation framework. We first propose a five-element assembly model (FAM) for RFAS-CCP, i.e. product, process, resource, knowledge, and decision. The product, process, and resource element describe the objects, steps to be assembled, and the tools, fixtures, and other equipment used for assembly, respectively. The knowledge element is a form representation of various heterogeneous data, such as a knowledge graph. The decision element includes various assembly methods to achieve assembly automation, flexibility, and intelligence. Then, in order to standardize and easy the frequent reconfiguration process, we reorganize various decision methods into a three-phase systematic implementation framework according to which stage they are used: design, configuration, and operation phases. The design phase methods primarily design various assembly modules for a product family, forming an assembly resource library. The configuration phase methods primarily configure suitable assembly lines for a specific product in the product family. The operation phase methods monitor the status of the assembly line and ensures its stable operation through health management. Finally, the effectiveness and practicality of the proposed five-element assembly model and three-phase systematic implementation framework are experimented with a pressure reducing valve product.

Flexible assembly job shop scheduling problem considering reconfigurable machine: A cooperative co-evolutionary matheuristic algorithm

With popularity of mass customization, enterprises urgently need to improve reconfigurability to handle rapidly changing market demands. Reconfigurable machines have been widely applied in the flexible assembly job shop. These reconfigurable machines can be equipped with various and limited auxiliary modules (AMs) to provide multiple alternative functions to manufacture numerous products. Therefore, this study addresses flexible assembly job shop scheduling problem considering reconfigurable machines with limited AMs to minimize makespan. First, a mixed-integer linear programming (MILP) model is formulated to define the problem. Then, a cooperative co-evolutionary matheuristic algorithm (CCMA) is presented to efficiently tackle large-scale FAJSP-RM problem. In this algorithm, a MILP-based evolution method, which relies on a decomposed MILP (D-MILP) with known sequence decisions, is proposed to explore the optimal machine and AM assignments. Meanwhile, a collaborative initialization and dual collaborative strategy are proposed to improve the performance of the proposed CCMA. Comprehensive experiments are conducted on 720 instances, extended from the well-known Fdata and BRdata benchmarks, to evaluate the proposed MILP model and CCMA algorithm. The results illustrate that the MILP model can produce optimal solutions for small-scale instances. The MILP-based evolution method improves the performance of the CCMA algorithm by 12.63, and the CCMA outperforms other well-known algorithms from numerical, statistical, differential and stable analysis.

Designing a digital-twin based dashboard system for a flexible assembly line

The current dashboard system is limited of coordination, flexibility and scalability. This study proposed a digital-twin based dashboard system. The digital twin components are divided into four, namely the operator, screw machine, industrial robot and automatic optical inspection in a flexible GPU card assembly line. This study designs a protocol enabling digital twins to interact and operate the system collaboratively and flexibly. In addition, this study presents a landscape of digital twin dashboards to demonstrate the relationship between the digital twins and the physical modules, providing a use case for the future digital-twin based dashboard design. The proposed system is implemented in a flexible GPU card assembly line and constructed by using JMobile to build a real-time dashboard system capable of scalability. The proposed dashboard framework for the GPU card assembly line offers several advantages over existing dashboards in terms of productivity, quality, and accident reduction. This study contributes to a scalable real-time dashboard system framework by incorporating human–machine collaboration, laying the foundation for advanced dashboard system development.

A multi-level action coupling reinforcement learning approach for online two-stage flexible assembly flow shop scheduling

Multi-product centralized delivery and kitting assembly present significant challenges to hierarchical co-processing in multi-stage manufacturing systems. The combinations of priority dispatching rules at each level are transiently adaptive, and the performance in online scheduling deteriorates rapidly with changing environment. This paper investigates the selection of rule combinations for sustained high-performance responsive scheduling in two-stage flexible assembly flow shop scheduling problem with asynchronous execution and complex decision correlation. A Multi-Level Action Coupling Deep Q-Network (MALC-DQN) approach is proposed for adaptive integrated scheduling in hybrid processing and assembly shops. Firstly, the problem is skillfully established as an event-triggered integrated decision markov decision process. The prioritized batch experience replay mechanism is employed to retain the complete correlation information of key decision sequences. Then, coupling and sequence feature extraction modules are developed to enhance the agent’s ability to perceive execution process and the environment. Furthermore, the multi-level wait-limit mechanism and efficient action filtering mechanism are designed to mitigate ineffective waiting waste and action space explosion during learning. Finally, a series of sophisticated experiments are conducted to validate the effectiveness of the proposed methodology. In 20 actual instances of different sizes, MLAC-DQN outperformed its closest competitor, with a 26.6% improvement in average tardiness. Moreover, extraordinary robustness is demonstrated in 16 sets of experiments involving different configurations of resources, orders, and arrival concentration levels.

On Efficient and Flexible Autonomous Robotic Insertion Assembly in the Presence of Uncertainty

On Efficient and Flexible Autonomous Robotic Insertion Assembly in the Presence of Uncertainty

HMM-Based Blockchain Visual Automatic Deployment System

The traditional blockchain deployment process is too complicated and has high technical requirements for blockchain deployers. Deploying a blockchain requires building a complex software-dependent environment, being able to use Linux commands for cumbersome parameter configurations, as well as the need to consider whether the hardware meets the requirements for running a blockchain. To address these current challenges in blockchain deployment both domestically and internationally, a web-based automatic deployment system with an interactive front-end and back-end has been developed. This system streamlines the process by automatically configuring and deploying blockchains while providing deployers with a graphical interface to monitor the entire deployment procedure. Meanwhile, in order to improve the efficiency of blockchain deployment, a Hidden Markov Model has been designed for blockchain deployment, which can predict the best deployment method for blockchain deployment under the current software environment. As one of the excellent blockchain platforms in China, Chainmaker has the outstanding features of independent control, flexible assembly, software and hardware integration, open source, and openness. The system takes Chainmaker as an experimental object and after a lot of tests, it can easily build a blockchain network on the server.

Multi-objective evolutionary algorithm based flexible assembly job-shop rescheduling with component sharing for order insertion

This paper addresses a rescheduling problem in an order-driven flexible assembly job-shop, which encompasses a complete production line from machining to assembly. Some products have a multi-level assembly structure, and certain components can be shared among different products. When an order with tight delivery date is inserted, rescheduling is necessary for adjusting original production scheme and tradeoffs among makespan, total tardiness and count of machine changes. To formulate the flexible assembly job-shop rescheduling problem with component sharing (FAJRP-CS), a mixed-integer linear programming model is established. The model involves three categories of decision variables that determine the machine assignment, shared component allocation, and operation sequencing. Meanwhile, solution of FAJRP-CS is analyzed by using graph model, and a neighborhood structure containing two-type move for critical operations is proposed for local optimizing. Then, a hybrid non-dominated sorting genetic algorithm with tabu search (NSGATS) is developed to maintain diversity while improving convergence. The approach is first compared with 8 algorithms on nine test instances, and the effectiveness of the operators, framework and neighborhood structure are discussed separately. Subsequently, NSGATS is applied to solve the multi-stage rescheduling problem in a medical device workshop with 6 products and 56 components. The experimental results demonstrate that NSGATS is effective for solving the FAJRP-CS.

Considering environmental impacts in flexible assembly job shop scheduling: non-dominated sorting memetic algorithm

Considering environmental impacts in flexible assembly job shop scheduling: non-dominated sorting memetic algorithm

Coupled vibration analysis of the spacecraft with the flexible shaft and solar panels assembly

Dynamical characteristics of the spacecraft with flexible components are investigated in this paper. The component consists of a flexible shaft and solar panels, where solar panels are completely fixed on the shaft. A novel power series multiplier polynomial method is proposed to describe the connecting condition between the shaft and solar panels. The deformation and force matching conditions between the one dimensional shaft and two dimensional panels are established, which is the major contribution about the multidimensional combined structure of this study. According to constraining boundary conditions of the shaft and solar panels, the bending-torsion coupled deformation of the component is expressed. The attitude maneuvering of the central platform and the elastic vibration of the component cause the rigid-flexible coupled effect. Then the discrete dynamical equation of the spacecraft is obtained by using the rigid-flexible coupled modal shapes. The correctness of the proposed method is verified by comparing the natural characteristics with that of the finite element model. The relative error of the higher order frequency is not more than 4%. Numerical results show that the degree of the bending angle polynomial should be at least quadratic to ensure the accuracy of the calculation. The proposed power series multiplier polynomial breaks through the barriers that the traditional Lagrange multiplier cannot express the continuous constraint. It is an effective method to fit the constrained boundary by the polynomial rather than by the discrete multipoint method.

A Wide-Band Low-Profile Antenna for a High-Integration Phased Array System.

In this paper, a wide-band, low-profile antenna is presented for a high-integration phased array system. The proposed antenna, implemented using a tightly coupled array, operates over roughly the X-K frequency band and is performant at 8 GHz-18.5 GHz. The antenna can scan to ±60 degrees in both the E- and H-planes. Compared to previous tightly coupled antennas with smaller element spacing, the antenna in this paper reaches 9.4 mm, which corresponds to 0.58 λ of high frequency, suitable for engineering application conditions in production. The antenna can be soldered to BGA T/R chips in this space. Additionally, to facilitate flexible assembly for large arrays, the antenna is manufactured modularly using four elements and its parasitic radiation is analyzed. Then, a method for repressing parasitic radiation is presented. Finally, the antenna is fabricated and measured in a microwave chamber, exhibiting an excellent pattern and scanning radiation. The measured performance agrees with the full-wave finite array simulations.

Matheuristic and learning-oriented multi-objective artificial bee colony algorithm for energy-aware flexible assembly job shop scheduling problem

With the increase of mass customization, flexible job shop scheduling problem considering assembly stage has widely existed in many manufacturing industries, such as die-casting mould factories. This problem is to find a reasonable machine assignment and operation sequence both in fabrication and assembly stages and simultaneously maximize production efficiency. In reality, energy shortages and environmental pollution have given an impetus to the development of energy-aware production scheduling problems. In this study, we address an energy-aware flexible assembly job shop scheduling problem (EFAJSP) with the objectives of minimizing flow time and energy consumption and first develop a mixed-integer linear programming (MILP) model to solve EFAJSP problem. Then, the model-specific characteristics are extracted and applied to a matheuristic decoding method for exploring the Pareto optimal solution. Due to the complexity of EFAJSP problem, a matheuristic and learning-oriented multi-objective artificial bee colony algorithm (MLABC), which combines the advantages of mathematical programming, reinforcement learning and meta-heuristic algorithm, is proposed. In addition, an initialization, destruction/construction operator and population update operator are proposed and work together to improve the exploration and exploitation performance of the proposed MLABC. Finally, numerical experimental results demonstrate the effectiveness of the proposed MILP model and the superiority of the MLABC over other algorithms in the literature.

Intelligent design of reconfigurable flexible assembly fixture for aircraft panels based on smart composite jig model and knowledge graph

The current demand for aircraft panel assembly fixtures necessitates agile manufacturing solutions. Traditional welding fixtures struggle to meet diverse panel types, high quantities, and short manufacturing cycles. Conversely, reconfigurable flexible assembly fixtures offer universality, efficiency and ease of maintenance but heavily rely on designers' experience, hindering knowledge reuse. Existing research on computer-aided fixture design (CAFD) primarily addresses specialized fixtures, neglecting reconfigurable flexible ones, lacking fixture design knowledge systems. Aiming at the above problems, the study proposes an intelligent design method for the reconfigurable flexible assembly fixture for aircraft panels based on the smart composite jig model (SCJM) and knowledge graph (KG). Firstly, based on the SCJM of the aircraft panel type assembly fixture (PSCJM), a configuration design method is proposed to carry out the multi-level design of reconfigurable assembly fixture to instantiate PSCJM containing fixture configuration rules and a module library based on model definition (MBD). Then, a knowledge retrieval method is proposed to search the suitable fixture design knowledge based on KG, assisting designers in achieving the flexible design of aircraft panel reconfigurable assembly fixtures. Finally, practical engineering design validation confirms the method's feasibility and effectiveness in obtaining the reconfigurable flexible assembly fixture design scheme for aircraft panels.

Heteromultivalent DNA Enhances the Assembly Yield of Hybrid Nanoparticles and Facilitates Dynamic Disassembly for Bioanalysis Using ICP-MS.

To obtain enhanced physical and biological properties, various nanoparticles are typically assembled into hybrid nanoparticles through the binding of multiple homologous DNA strands to their complementary counterparts, commonly referred to as homomultivalent assembly. However, the poor binding affinity and limited controllability of homomultivalent disassembly restrict the assembly yield and dynamic functionality of the hybrid nanoparticles. To achieve a higher binding affinity and flexible assembly choice, we utilized the paired heteromultivalency DNA to construct hybrid nanoparticles and demonstrate their excellent assembly characteristics and dynamic applications. Specifically, through heteromultivalency, DNA-functionalized magnetic beads (MBs) and gold nanoparticles (AuNPs) were efficiently assembled. By utilizing ICP-MS, the assembly efficiency of AuNPs on MBs was directly monitored, enabling quantitative analysis and optimization of heteromultivalent binding events. As a result, the enhanced assembly yield is primarily attributed to the fact that heteromultivalency allows for the maximization of effective DNA probes on the surface of nanoparticles, eliminating steric hindrance interference. Subsequently, with external oligonucleotides as triggers, it was revealed that the disassembly mechanism of hybrid nanoparticles was initiated, which was based on an increased local concentration rather than toehold-mediated displacement of paired heteromultivalency DNA probes. Capitalizing on these features, an output platform was then established based on ICP-MS signals that several Boolean operations and analytical applications can be achieved by simply modifying the design sequences. The findings provide new insights into DNA biointerface interaction, with potential applications to complex logic operations and the construction of large DNA nanostructures.

WalkingWizard—A Truly Wearable EEG Headset for Everyday Use

Electroencephalography (EEG) provides an opportunity to gain insights to electrocortical activity without the need for invasive technology. While increasingly used in various application areas, EEG headsets tend to be suited only to a laboratory environment due to the long preparation time to don the headset and the need for users to remain stationary. We present our design of a dry, dual-electrodes flexible PCB assembly that realizes accurate sensing in the face of practical motion artifacts. Using it, we present WalkingWizard, our prototype dry-electrode EEG baseball cap that can be used under motion in everyday scenarios. We first evaluated its hardware performance by comparing its electrode-scalp impedance and ability to capture alpha rhythm against both wet EEG and commercially available dry EEG headsets. We then tested WalkingWizard using steady-state visual evoked potential (SSVEP) experiments, achieving high classification accuracy of 87% for walking speeds up to 5.0 km/h, beating state-of-the-art. Expanding on WalkingWizard, we integrated all necessary electronic components into a flexible PCB assembly—realizing WalkingWizard Integrated , in a truly wearable form-factor. Utilizing WalkingWizard Integrated, we demonstrated several applications as proof-of-concept: classification of SSVEP in VR environment while walking, real-time acquisition of emotional state of users while moving around the neighbourhood, and understanding the effect of guided meditation for relaxation.

Ykt6 functionally overlaps with vacuolar and exocytic R-SNAREs in the yeast Saccharomyces cerevisiae

The soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) complex forms a 4-helix coiled-coil bundle consisting of 16 layers of interacting side chains upon membrane fusion. The central layer (layer 0) is highly conserved and comprises three glutamines (Q) and one arginine (R), and thus SNAREs are classified into Qa-, Qb-, Qc-, and R-SNAREs. Homotypic vacuolar fusion in Saccharomyces cerevisiae requires the SNAREs Vam3 (Qa), Vti1 (Qb), Vam7 (Qc), and Nyv1 (R). However, the yeast strain lacking NYV1 (nyv1Δ) shows no vacuole fragmentation, whereas the vam3Δ and vam7Δ strains display fragmented vacuoles. Here, we provide genetic evidence that the R-SNAREs Ykt6 and Nyv1 are functionally redundant in vacuole homotypic fusion in vivo using a newly isolated ykt6 mutant. We observed the ykt6-104 mutant showed no defect in vacuole morphology, but the ykt6-104 nyv1Δ double mutant had highly fragmented vacuoles. Furthermore, we show the defect in homotypic vacuole fusion caused by the vam7-Q284R mutation was compensated by the nyv1-R192Q or ykt6-R165Q mutations, which maintained the 3Q:1R ratio in the layer 0 of the SNARE complex, indicating that Nyv1 is exchangeable with Ykt6 in the vacuole SNARE complex. Unexpectedly, we found Ykt6 assembled with exocytic Q-SNAREs when the intrinsic exocytic R-SNAREs Snc1 and its paralog Snc2 lose their ability to assemble into the exocytic SNARE complex. These results suggest that Ykt6 may serve as a backup when other R-SNAREs become dysfunctional and that this flexible assembly of SNARE complexes may help cells maintain the robustness of the vesicular transport network.