Flexible Assembly Research Articles

A matheuristic-based multi-objective evolutionary algorithm for flexible assembly jobs shop scheduling problem in cellular manufacture

The flexible assembly job shop scheduling problem (FAJSP) and its extensions have received increased attention. However, multi-objective FAJSP in cellular manufacture (FAJSP-CM), a real-world extension of FAJSP with important applications in the mass customization of complex products, is barely considered in most previous studies. Hence, this study first addresses the multi-objective FAJSP-CM problem via a mixed-integer linear programming model (MILP). Then, a matheuristic-based multi-objective evolutionary algorithm (MMOEA), with a matheuristic decoding method, knowledge-guided initialization and learning-based local intensification method, is presented to efficiently tackle the FAJSP-CM problem in polynomial-time. The matheuristic decoding method, which hybrids a decomposition linear programming model (D-LP) that can quickly and optimally allocate the execution times of all operations, to search for the optimal solution in a specific space. The knowledge-guided initialization is extended to get a high-quality and great-diversity initial population for accelerated convergence. Meanwhile, the learning-based local intensification method is developed to adaptively coordinate five problem-specific local search operators to enhance the exploitation ability of the MMOEA algorithm. Finally, comprehensive experiments based on 720 instances are conducted to evaluate the proposed MILP model and MMOEA algorithm. The results of the first experiments illustrate the MILP model can obtain the Pareto solutions of small-scale instances. The remaining experiments demonstrate that the proposed MMOEA algorithm outperforms its competitors by 0.21 and 0.72 in hypervolume ratio and coverage indictors, along with approximately a 25 % improvement in success rate.

Photoinduced radical tandem annulation of 1,7-diynes: an approach for divergent assembly of functionalized quinolin-2(1H)-ones.

The first photocatalytic trichloromethyl radical-triggered annulative reactions of amide-linked 1,7-diynes with polyhalomethanes were established for the flexible assembly of functionalized quinolin-2(1H)-ones with generally acceptable yields. With the installation of the aryl group (R1) into the alkynyl moiety, C-center radical-initiated Kharasch-type addition/nucleophilic substitution/elimination cascade to produce quinolin-2(1H)-ones-incorporating gem-dihaloalkene, whereas three examples of polyhalogenated quinolin-2(1H)-ones were afforded when amide-linked 1,7-diynes bearing two terminal alkyne units were subjected to BrCX3 by exploiting dry acetonitrile as a solvent.

Dynamic Affect-Based Motion Planning of a Humanoid Robot for Human-Robot Collaborative Assembly in Manufacturing

The objective was to investigate the impacts of the robot’s dynamic affective expressions in task-related scenarios on human–robot collaboration (HRC) and performance in human–robot collaborative assembly tasks in flexible manufacturing. A human–robot hybrid cell was developed to facilitate a human co-worker and a robot to collaborate to assemble a few parts into a final product. The collaborative robot was a humanoid manufacturing robot with the ability to display its affective states due to changes in task scenarios on its face. The assembly task was divided into several subtasks, and based on an optimization strategy, the subtasks were optimally allocated to the human and the robot. A computational model of the robot’s affective states was derived inspired by that of humans following the biomimetic approach, and an affect-based motion planning strategy for the robot was proposed to enable the robot to adjust its motions and behaviors with task situations and communicate (inform) the situations to the human co-worker through affective expressions. The HRC and the assembly performance for the affect-based motion planning were experimentally evaluated based on a comprehensive evaluation scheme and were compared with two alternative conditions: (i) motion planning that did not display affective states, and (ii) motion planning that displayed text messages instead of displaying affective states to communicate the situations to the human co-worker. The results clearly showed that the dynamic affect-based motion planning produced significantly better HRC and assembly performance than that produced by motion planning associated with the display of no affective states or text messages. The results encouraged employing manufacturing robots with dynamic affective expressions to collaborate with humans in flexible assembly in manufacturing to improve HRC and assembly performance.

Design and Experiment of Profiling Furrow-Ridge Terrain by Cane Leaf-Chopping and Returning Machine

Design and Experiment of Profiling Furrow-Ridge Terrain by Cane Leaf-Chopping and Returning Machine

A new redundancy strategy for enabling graceful degradation in resilient robotic flexible assembly cells

The development of resilience in manufacturing systems has drawn more attention than ever. Using redundant components is one of the key strategies for building and enhancing the resilience of a manufacturing system. However, current redundancy strategies require duplicated machinery employed either in active or in standby status. This in turn causes extra costs in designing and achieving resilience. Achieving an efficient deployment of the redundant component in the face of failures is also challenging. In this paper, we introduce a novel redundancy strategy, called adaptive standby redundancy (ASR), to achieve resilient performance for discrete manufacturing systems while reducing the cost of employing the duplicated components that are typically used in traditional systems. This novel strategy permits achievement of high levels of utilisation of the system and graceful degradation in case of failure, keeping the system functional. The strategy is then validated in a developed robotic flexible assembly cell (RFAC), which is tested and results on its efficacy and performance enhancement are discussed.

Terahertz bi-functional polarization converter based on interference mechanism supported by diatomic metasurfaces

Polarization manipulation based on the Jones matrix facilitates the enhancement of light-matter interactions. Recently, arbitrarily tailorable polarization states generated with the assistance of a diatomic metasurface effectively reduce the complexity of the system. Nevertheless, a single polarization switching behavior hinders the application of meta-platforms in cryptographic imaging. Here, we theoretically propose and design a single-layer diatomic all-dielectric metasurface working in the terahertz band, which can efficiently realize bi-functional polarization switching according to the Jones matrix. Such a meta-platform is assembled from two anisotropic silicon pillars with carefully optimized lateral dimensions and in-plane twist angles. Benefiting from the flexible assembly of half-wave plate and quarter-wave plate, the polarization states generated by the constructed metasurfaces in the transmission mode can be arbitrarily tailored. The feasibility of this diatomic metasurface is further validated by a broadband near-field imaging device, paving the way for broader system applications in cryptographic imaging, data storage, and chiral sensing.

Nonbonding Electron Delocalization Stabilizes the Flexible N8 Molecular Assembly.

Electron delocalization has an important impact on the physical properties of condensed materials. However, the L-electron delocalization in inorganic, especially nitrogen, compounds needs exploitation to improve the energy efficiency, safety, and environmental sustainability of high-energy-density materials (HEDMs). This Letter presents an intriguing N8 molecule, ingeniously utilizing nitrogen's L-electron delocalization. The molecule, exhibiting a unique lollipop-shaped conformation, can fold at various angles with very low energy barriers, self-assembling into environmentally stable, all-nitrogen crystals. These crystals demonstrate unparalleled stability, high energy density, low mechanical sensitivity, and optimal electronic thermal conductivity, outperforming existing HEDMs. The remarkable properties of these designed materials are attributed to two distinct delocalized systems within nitrogen's L-shell: π- and lone pair σ-electrons, which not only stabilize the molecular structure but also facilitate interconnected 3D networks of intermolecular nonbonding interactions. This work might pave the way to the experimental synthesis of environmentally stable all-nitrogen solids.

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.

The Aemulus Project. VI. Emulation of Beyond-standard Galaxy Clustering Statistics to Improve Cosmological Constraints

There is untapped cosmological information in galaxy redshift surveys in the nonlinear regime. In this work, we use the Aemulus suite of cosmological N-body simulations to construct Gaussian process emulators of galaxy clustering statistics at small scales (0.1–50 h −1 Mpc) in order to constrain cosmological and galaxy bias parameters. In addition to standard statistics—the projected correlation function w p(r p), the redshift-space monopole of the correlation function ξ 0(s), and the quadrupole ξ 2(s)—we emulate statistics that include information about the local environment, namely the underdensity probability function P U(s) and the density-marked correlation function M(s). This extends the model of Aemulus III for redshift-space distortions by including new statistics sensitive to galaxy assembly bias. In recovery tests, we find that the beyond-standard statistics significantly increase the constraining power on cosmological parameters of interest: including P U(s) and M(s) improves the precision of our constraints on Ωm by 27%, σ 8 by 19%, and the growth of structure parameter, f σ 8, by 12% compared to standard statistics. We additionally find that scales below ∼6 h −1 Mpc contain as much information as larger scales. The density-sensitive statistics also contribute to constraining halo occupation distribution parameters and a flexible environment-dependent assembly bias model, which is important for extracting the small-scale cosmological information as well as understanding the galaxy–halo connection. This analysis demonstrates the potential of emulating beyond-standard clustering statistics at small scales to constrain the growth of structure as a test of cosmic acceleration.

Alkane-Strengthened Viscoelasticity in Micellar Solutions of Surface-Active Ionic Liquids and Their Potential Application in Enhanced Oil Recovery.

Wormlike micelles (WLMs) are highly sensitive to alkanes, resulting in structural destruction and loss of viscosity. Therefore, the study of WLMs against alkanes holds great significant importance. Surface-active ionic liquids have shown increasing promise for different situations for customizing molecular structures with the specialty of flexible functional assembly. In this paper, we found that WLMs constructed from the long-chain fatty acid surface-active ionic liquid (N,N-dimethylbenzylamine-oleic acid, abbreviated as BD-OA) exhibit strengthened viscoelasticity with the introduction of alkanes, expanding the resistance range to alkane damage. Here, the rheological behavior, microstructure, and dissipative particle dynamics (DPD) simulations of BD-OA WLMs were investigated at macro-, micro-, and mesoscopic scales, before (and after) the introduction of alkane. Our findings confirm the structural transformation of the micellar system from WLMs to lamellar micelles with higher viscoelasticity after alkane induction. The rearrangement of the micelle configuration may be attributed to the infiltration of alkane molecules into the fence layer formed by the BD-OA WLMs, leading to an increase in the boundary accumulation parameter and ultimately resulting in the formation of lower curvature lamellar micelles. More importantly, the against alkanes BD-OA WLMs have exhibited excellent in enhanced oil recovery, which has a promise for substituting common oil-displacing agents in tertiary oil recovery processes.

Application and structure of carbon nanotube and graphene-based flexible electrode materials and assembly modes of flexible lithium-ion batteries toward different functions

Application and structure of carbon nanotube and graphene-based flexible electrode materials and assembly modes of flexible lithium-ion batteries toward different functions

A feedback learning-based selection hyper-heuristic for distributed heterogeneous hybrid blocking flow-shop scheduling problem with flexible assembly and setup time

Distributed manufacturing is increasingly common due to economic globalization. It has important practical significance to optimize global supply chains by considering cooperative scheduling of distributed production and flexible assembly. This paper studies a distributed heterogeneous hybrid blocking flow-shop scheduling problem with flexible assembly and setup time (DHHBFSP-FAST). The objective is to minimize makespan of all products. To tackle such problem, a mixed-integer linear programming model (MILP) is presented to formulate it. Then, a feedback learning-based selection hyper-heuristic (FLS-HH) is proposed, which contains high-level control strategies and low-level heuristics. For the high-level control strategies, the transition information between each pair of low-level heuristics is collected, then a feedback learning-based selection method is presented based on such transition information, which can automatically select the appropriate low-level heuristics. For the low-level heuristics, to assign jobs/products to machines, a composite dispatch rule is proposed. To generate an initial domain solution with high quality, a constructive heuristic based on double evaluation indexes is developed. The critical jobs and products are analyzed to avoid invalid searching. Based on such analysis, several low-level heuristics are presented to search the domain solution space, which combine six problem-specific perturbation heuristics and two local searches to balances exploration and exploitation. Comprehensive numerical experiments are carried out. The effectiveness of the proposed MILP and the special designs of FLS-HH are verified. To verify the effectiveness of FLS-HH, we compare it with 10 existing high-performing approaches. The comparison results show that FLS-HH significantly outperforms its competitors in solving DHHBFSP-FAST.

CubeSat standardized modular assembly method and design optimization

In order to solve the deficiencies of traditional board-level assembly CubeSat, such as low modularity, complex assembly process and poor scalability, a sandwich module assembly structure and method with no drive components is proposed, which can achieve quick connection and flexible assembly between modules. First, the proposed CubeSat standard module can be compatible with the standard interface of traditional CubeSat. The 4-in-1 interface of Mechanical-Electrical-Thermal-Data does not occupy the internal space of the standard module, and the electrical and thermal interfaces meet the requirements of CubeSat in-orbit work. Secondly, in order to improve the reliability of the connection between the modules, a plug-in connection scheme of the mechanical interface was designed. Finally, a genetic algorithm was used to optimize the interface size, so that it has better plug-in performance. The simulation results show that the performance of the male interface of titanium alloy material is the best, and the error between the finite element analysis and genetic algorithm results is less than 4.8 %, which is consistent with the actual tensile test results of the optimized prototype, and meets the mechanical requirements of the launch process and space orbit operation for CubeSat. The test results can guide production and provide a reference for subsequent engineering applications of CubeSat modular assembly.

Physiological workload assessment for highly flexible fine-motory assembly tasks using machine learning

In assembly of small-volume products, tasks are still frequently executed manually. However, the lead times foreseen for these tasks, often do not take into account the actual capabilities of the employees, which in turn leads to increased workload and the associated stress among the employees. This paper investigates how a commercially available wearable low-cost sensor and two machine learning algorithms can be applied to measure and evaluate heart rate, heart rate variability and respiration rate to establish a relationship with workload. The investigated algorithms, namely Random Forest and K-Nearest-Neighbours are able to distinguish between tasks phases and rest phases as well as between easy and difficult tasks executed by the employee, which is the main novelty of this paper.

Fault Detection Algorithm for Pneumatic Measuring and Sorting Station

The aim of this paper is to present basic hardware components of a Flexible Assembly System (FAS) and investigate possible faults, classify the errors and implement real diagnosis. The mentioned system offers professional training according to the industrial reality, simulating a real assembly process and including different technologies from Industry 4.0. This equipment consists of an automated flexible assembly cell with five different stages: assembly, handling, quality inspection, transfer and warehouse. The pneumatic system repeats the identical programmed sequence during it operation. The data was collected when the pneumatic system worked perfectly and had some generated faults including empty magazine, zero vacuum, inappropriate material, no pressure, closed manual pressure valve, missing drilling stroke, poorly located material, not vacuuming the material and low air pressure. The faults are simulated according to developed algorithms in this investigation. The case study with fuzzy logic is used to develop a new sequence for one pneumatic module diagnosis. The analysis of the simulated faults is discussed and the obtained results can be applied in the similar pneumatic systems in industry.

Additively manufactured vacuum grippers for the flexible handling and assembly of hairpin coils in electric motor production

Hairpin technology has recently become the predominant winding technology for the manufacturing of stators used in electric traction motors. In contrast to conventional winding techniques, the manufacturing process chain is based on rectangular wire which is bent into a three-dimensional U-shape – so-called hairpin coils. These hairpin coils have to be individually gripped and assembled to form the stator winding. There can be hundreds of hairpin coils in a single stator, which can also differ in shape. Therefore, gripping and moving the individual hairpin coils is a challenging task in terms of flexibility and cycle time. This paper presents an approach to meet the outlined requirements using additively manufactured vacuum grippers. A concept is investigated in which the vacuum is generated in a conventional vacuum ejector and fed to the vacuum gripper via hose lines. In contrast, the concept of a functionally integrated vacuum gripper manufactured by a new laser-sintering process with automated continuous fibre reinforcement is presented. Based on the laser-sintering process, a lightweight vacuum gripper with an integrated vacuum ejector can be manufactured in a single process step without additional costs for purchased parts. After a review of the state of the art in both hairpin-specific and additively manufactured lightweight grippers, experimental test series are carried out concerning the vacuum quality and the grip strength of the different vacuum gripper designs. Pull-off tests show that high holding forces of several Newtons are possible proving the basic usability of vacuum grippers for hairpins, even though the achievable vacuum quality of the functionally integrated vacuum gripper is lower than that of the conventional ejector. Comparison of the novel approaches with a conventional mechanical hairpin gripper regarding weight, production costs and production time show promising improvements.

Software architecture for adaptable assembly systems

Modern manufacturing methods demand highly flexible and reusable assembly systems. Such flexibility and reusability can be attained using systems that are modular. These modular systems have the ability to react faster to market forces and are adaptable beyond the limits of the so-called flexibility corridor. Also, placing the constraints of flexibility and reusability on software paves the way for design and usage of generic systems that have a relatively low initial cost.However, these advantages are often overshadowed by the complexity of these software systems. Especially, one faces many difficulties in software modularizing the assembly system so that each module can be integrated seamlessly into the entire system. Defining an interface in the development phase is practically impossible given that systems evolve a lot before reaching production phase.

Using Goal-Conditioned Reinforcement Learning With Deep Imitation to Control Robot Arm in Flexible Flat Cable Assembly Task

Using Goal-Conditioned Reinforcement Learning With Deep Imitation to Control Robot Arm in Flexible Flat Cable Assembly Task

Flexible Assembly Job Shop Scheduling Considering Assembly Sequence Variation Under Dual-resource Constraints Using Discrete Barnacles Mating Optimizer Algorithm

Flexible Assembly Job Shop Scheduling Considering Assembly Sequence Variation Under Dual-resource Constraints Using Discrete Barnacles Mating Optimizer Algorithm

Rb3MgB5O10 and LiBaAl(BO3)2: covalent tetrahedra MO4-containing borates with deep-ultraviolet cutoff edges.

Borates are favored by materials scientists and chemists because of the significant electronegativity difference between B and O atoms and their flexible assembly modes resulting in abundant structures and excellent properties. For the design of deep-ultraviolet (DUV) optical crystals with excellent macroscopic performance, it is crucial to choose appropriate cations and anionic groups and microscopically reasonable assembly patterns. Herein, by introducing covalent tetrahedra ([MO4], M = Mg, Al), two new mixed alkali metal and alkaline earth metal borates, Rb3MgB5O10 and LiBaAl(BO3)2, were synthesized using the melt method and high-temperature solution method. They contain M-B-O two-dimensional (2D) layers (2∞[MgB5O10] and 2∞[Al(BO3)2], respectively) composed of isolated B-O groups ([B5O10]5- and [BO3]3-, respectively) and metal-centered tetrahedral connectors ([MgO4]6- and [AlO4]5-, respectively). Combining experiments and theoretical calculations shows that the two compounds have short cutoff edges (<200 nm) and moderate birefringences. Further analysis manifests that the isolated [MO4] covalent tetrahedra can optimize the arrangement of anion groups, guarantee the balanced optical properties of materials, and point out the direction for further exploration of novel borate structures.