Flexible Manufacturing Research Articles

Immersive Digital Twin under ISO 23247 Applied to Flexible Manufacturing Processes

Digital twin (DT) technology provides a path for implementing cyber–physical systems (CPS) and developing smart manufacturing because they are essential tools for monitoring and controlling manufacturing processes. It is considered a vital technology in smart manufacturing and is being widely researched in academia and industry. Furthermore, the combination of DTs and immersive environments has shown great potential for integrating novel capabilities into the new generation of CPS. This research presents an architecture for implementing immersive digital twins under ISO 23247 in flexible manufacturing processes. The proposed system is based on the integration of DT technologies in conjunction with augmented reality (AR) and gesture tracking, and validation was performed in the sorting station of the MPS 500 to increase the interaction and flexibility between physical and virtual environments in real time, thus enhancing the capabilities of the DT. The methodology used for the design and implementation of the DT includes (1) general principles and requirements; (2) models with functional views based on domains and entities; (3) attributes of the observable manufacturing elements; and (4) protocols for the exchange of information between entities. The results show that the integration of these technologies improves the monitoring, control, and simulation capabilities of processes using 3D resources and immersive environments, achieving a higher level of interactivity. In addition, error detection tests were carried out, where a reduction of time was observed in the resolution of errors that may be caused by internal or external disturbances of the process, thus avoiding production delays.

Energy-aware dynamic rescheduling of flexible manufacturing system using edge-cloud collaborative decision-making method

ABSTRACT With the development of cloud computing technology, energy-aware scheduling based on cloud computing is an essential means to achieve energy saving and carbon reduction in manufacturing systems. Due to various production disturbances in flexible production, the scheduling based on cloud computing has data security problems and poor real-time performance. How to use real-time information to improve the security and responsiveness of cloud scheduling is a research gap. This paper establishes a cloud-edge collaborative dynamic flexible job-shop energy-aware rescheduling decision-making model. According to the characteristics of dynamic production in a flexible manufacturing system, create a learning model for cloud scheduling with Deep Q Network (DQN) and send the training model to the edge for scheduling decisions. Based on real-time production interference data, edge scheduling model real-time update scheduling scheme. To improve the robustness of the cloud scheduling model based on DQN, Dynamic scheduling data at the edge will be uploaded to update the cloud model. In addition, the effectiveness and practicability of the model are verified in an extrusion workshop. The experimental results show that this method can improve the comprehensive objective evaluation of minimum energy consumption and completion time by 3.6% −49.3% compared with the traditional scheduling rules.

Stretchable and Flexible Painted Thermoelectric Generators on Japanese Paper Using Inks Dispersed with P- and N-Type Single-Walled Carbon Nanotubes.

As power sources for Internet-of-Things sensors, thermoelectric generators must exhibit compactness, flexibility, and low manufacturing costs. Stretchable and flexible painted thermoelectric generators were fabricated on Japanese paper using inks with dispersed p- and n-type single-walled carbon nanotubes (SWCNTs). The p- and n-type SWCNT inks were dispersed using the anionic surfactant of sodium dodecylbenzene sulfonate and the cationic surfactant of dimethyldioctadecylammonium chloride, respectively. The bundle diameters of the p- and n-type SWCNT layers painted on Japanese paper differed significantly; however, the crystallinities of both types of layers were almost the same. The thermoelectric properties of both types of layers exhibited mostly the same values at 30 °C; however, the properties, particularly the electrical conductivity, of the n-type layer increased linearly, and of the p-type layer decreased as the temperature increased. The p- and n-type SWCNT inks were used to paint striped patterns on Japanese paper. By folding at the boundaries of the patterns, painted generators can shrink and expand, even on curved surfaces. The painted generator (length: 145 mm, height: 13 mm) exhibited an output voltage of 10.4 mV and a maximum power of 0.21 μW with a temperature difference of 64 K at 120 °C on the hot side.

Variability propagation in manufacturing systems: the impact of the processing time distribution

ABSTRACT In manufacturing and service systems, the negative effects of variability propagation are usually addressed through approximation models and other tools, such as simulation and optimization algorithms. This paper investigates the conditions in which these approaches are ineffective, and considering only the mean and variance of the processing time distribution is misleading. A scenario analysis deepens the impacts of considering the entire processing time distribution (beyond its mean and variance) on the inter-departure times in balanced and unpaced lines modeled through Discrete Event Simulation. The results correlate the impacts on variability propagation of approximating the processing time distribution with system characteristics such as utilization levels, line sizes, and inter-arrival and processing time variability. Production planning approaches can benefit from these results to reduce variability propagation, particularly in flexible and reconfigurable manufacturing systems, largely adopted in Industry 4.0, that can highly influence processing time distributions by varying product mix and line configuration.

A matrix-based approach to step-wise assess the safety of collaborative robots in manufacturing

AbstractCollaborative robots (cobots) allow for flexible manufacturing, supporting more customised product designs. Although safety is key for socio-technical human-cobot workplaces, existing safety assessment support like standards and guidelines require extensive experience and can be experienced as overwhelming. To make cobot risk assessments more accessible, especially for novices, and increase traceability from hazard to risk to mitigation, this paper presents a matrix-based approach that decomposes this daunting activity into smaller better manageable steps.

Using tensile and compressive stress superposition during incremental forming to manipulate martensitic transformation in SS304

Single point incremental forming (SPIF) is a flexible manufacturing process that has applications in industries ranging from biomedical to automotive. In addition to rapid prototyping, which requires easy adaptations in geometry or material for design changes, control of the final part properties is desired. One strategy that can be implemented is stress superposition, which is the application of additional stresses during an existing manufacturing process. Tensile and compressive stresses applied during SPIF showed significant effects on the resulting microstructure in stainless steel 304 truncated square pyramids. Specifically, the amount of martensitic transformation was increased through stress superposed incremental forming. Finite element analyses with advanced material modeling supported that the stress triaxiality had a larger effect than the Lode angle parameter on the phase transformation that occurred during deformation. By controlling the amount of tensile and compressive stresses superposed during incremental forming, the microstructure of the final component can be manipulated based on the intended application and desired final part properties.

Development of flexible manufacturing system

The focus of this project is the development and analysis of a Flexible Manufacturing System (FMS) for the production of a range of tables. Tables are typically used in homes and offices, where different variants are selected for different environments and uses. By implementing a flexible manufacturing system, different versions of tables can be produced in the same factory to fulfil different customer needs and preferences. This project designed and evaluated four different variants in the FMS, demonstrating aesthetic and functional differences while maintaining core similarities; this demonstrates FMSs flexibility and adaptability and provides insights into the trade-offs between design choices and manufacturing considerations. The article uses Mejabi and Solberg analysis for detailed FMS design and analysis. This analytical approach allows for thoroughly examining the FMS design, including factors such as productivity, efficiency and cost-effectiveness. The analysis further helps understand and optimise the implementation of the FMS to ensure that it effectively meets the production goals. This paper paves the way for enhancing productivity, increasing product range and improving customer satisfaction in the furniture manufacturing industry.

A novel hexagonal grid map model and regenerated heuristic factor based strategy for intelligent manufacturing system’s AGV path planning problem solving

In modern industry, flexible manufacturing systems play an indispensable role, and the precision and connectivity of map modeling are crucial for improving the efficiency and flexibility of AGV (Automated Guided Vehicle) transportation systems. Facing challenges such as spatial constraints, equipment failures, and unexpected task changes in real manufacturing environments, this paper proposes an innovative AGV path planning method based on hexagonal grid map modeling. This method significantly enhances the connectivity, sampling frequency, and safety of AGV path planning by replacing traditional square grids with hexagonal ones. Additionally, this study employs an improved ant colony algorithm combined with the hexagonal grid map for AGV path planning. The algorithm incorporates heuristic factors to effectively avoid local optima. Furthermore, by dividing the ant colony into odd and even groups and implementing a bidirectional search strategy, the comprehensive exploration capability of the algorithm is further enhanced. Experimental results show that compared to traditional square grids, the hexagonal grid reduces the optimal path length by 11.1%, and the application of heuristic factors further shortens the path length by 18.6%. The bidirectional search strategy also significantly reduces the number of iterations required by the algorithm, with a maximum reduction of 46.46%. In summary, this paper provides a new, efficient, stable, and energy-saving solution for AGV path planning systems, with thorough optimizations and improvements made in every aspect from map modeling to path planning algorithms.

Design and operation of modular biorefinery supply chain under uncertainty using generalized Benders decomposition

AbstractBiomass supply chain performance is heavily affected by uncertainties stemming from supply, demand, or unexpected disruptions. Unlike petrochemical plants that use crude oil, biorefineries often have to deal with the uneven spatial‐temporal distribution of feedstock supply. The modular production strategy provides more flexibility in chemical manufacturing by allowing fast capacity expansion and unit movement. However, modeling and optimizing modular biomass supply chain under uncertainties becomes challenging due to high dimensionality and the existence of discrete decisions. This work optimizes the multiperiod biomass supply chain using the rolling horizon planning and two‐stage stochastic programming framework. We then applied generalized Benders decomposition to reduce the computational complexity of the stochastic mixed integer nonlinear programming supply chain optimization. Furthermore, the solution of the stochastic programming could be used to quantitatively describe the life‐cycle assessment uncertainties of the biomass supply chain performance, demonstrating seasonality and random variability.

Mechanical Behaviour of As-Manufactured and Repaired Aligned Discontinuous Basalt Fibre-Reinforced Vitrimer Composites.

The aim of this research is to investigate basalt as a natural mineral-based fibre together with a vitrimeric resin as a sustainable alternative to standard composite materials. Vitrimers combine the properties of thermoset and thermoplastic polymers, enabling the repair of specimens and hence prolonging the lifetime of the composite material. The micro-mechanical characteristics between the basalt fibres and the vitrimer resin are reported and shown to match those of a standard Skyflex K51 epoxy resin. Discontinuous (4 mm) basalt fibres were employed to produce aligned discontinuous fibre-reinforced composites (ADFRCs) using the high-performance discontinuous fibre (HiPerDiF) technology. The mechanical characteristics of the laminates were investigated through tensile testing and the fracture zones were analysed under a scanning electron microscope. By normalising the results by their respective fibre volume fraction, it was discovered that the vitrimer-basalt ADFRCs exhibited, on average, a 4% higher strength and a 25% higher stiffness compared to their basalt epoxy counterparts. The repair potential of the vitrimer ADFRC specimens was explored during low-temperature compression repair. Two approaches were tested using double-sided local- and full-patch repair. Both successfully recovered a significant amount of their prime strength. In conclusion, the potential of the sustainable vitrimer-basalt composite is shown by its competitive mechanical performance. Combining this with the manufacturing flexibility, repair potential, and recyclability of the material, the vitrimer-basalt composite seems to be a competitive alternative to standard glass epoxies.

The process‐structure–property relationship of 3D printed G‐Polymer using fused filament fabrication technique

AbstractThe ever‐increasing demand for additive manufacturing (AM) is driven by the technology's rapid prototyping and flexible manufacturing benefits. Among the various AM techniques, fused filament fabrication (FFF) is one of the most commonly used techniques, in which thermoplastic polymer filaments are deposited layer by layer to create the final part. This technique has been used extensively in various sectors. However, new materials have yet to be developed for use in FFF. This research aims to introduce a new biodegradable highly amorphous polyvinyl alcohol (HAVOH), commercially known as G‐Polymer (GP), into the scope of FFF, to propose a process window for the fabrication of parts with enhanced mechanical properties and to discover the process‐structure–property relationship for this material. This study investigates the effect of five key parameters in GP FFF, including raster angle, number of contours, nozzle temperature, build platform temperature, and print speed. The results of the chemical, thermal, and thermo‐mechanical analysis of the filaments before and after hot extrusion of the 3D printer nozzle showed a significant increase in the mechanical properties of the hot‐extruded filaments. In addition, the mechanical characterization of 3D printed parts showed that increasing the number of contours can improve the mechanical properties of parts, while the raster angle can have a complex effect. The mechanical properties of the parts are also improved by reducing the temperature of the nozzle and the build platform. Printing speed, as an essential parameter in AM, was related to the previously mentioned parameters, and the results showed that increasing the printing speed could improve the UTS and Young's modulus of the 3D printed part.Highlights A novel biodegradable polymer (HAVOH) was introduced into the scope of FFF. The hot extruded G‐Polymer filament from the nozzle showed improved mechanical properties. 180°C nozzle and room temperature platform resulted in improved mechanical properties. 3D printed parts with +45°/−45° raster angle show improved mechanical properties 110 mm/s print speed results in higher mechanical strength than 70 mm/s print speed.

Design methodology for the control logic of flexible production systems and related logistics

The ever-shorter product life cycle entails a shortening of the life cycle of the production site and production systems as well. One of the biggest advantages of flexible production systems is that new products can be introduced to them continuously, so that their life cycle can be significantly extended. Flexible manufacturing systems, on the other hand, also require flexible control, the logic of which must be defined already in the design phase. The main elements of the design methodology of the production and logistics layout that make up the research are the information relationship chart and the related decision tables, which are suitable for speeding up the design and later for facilitating the creation of a digital twin and control logic programming.

Graphene derivative-based ink advances inkjet printing technology for fabrication of electrochemical sensors and biosensors

The field of biosensing would significantly benefit from a disruptive technology enabling flexible manufacturing of uniform electrodes. Inkjet printing holds promise for this, although realizing full electrode manufacturing with this technology remains challenging. We introduce a nitrogen-doped carboxylated graphene ink (NGA-ink) compatible with commercially available printing technologies. The water-based and additive-free NGA-ink was utilized to produce fully inkjet-printed electrodes (IPEs), which demonstrated successful electrochemical detection of the important neurotransmitter dopamine. The cost-effectiveness of NGA-ink combined with a total cost per electrode of $0.10 renders it a practical solution for customized electrode manufacturing. Furthermore, the high carboxyl group content of NGA-ink (13 wt%) presents opportunities for biomolecule immobilization, paving the way for the development of advanced state-of-the-art biosensors. This study highlights the potential of NGA inkjet-printed electrodes in revolutionizing sensor technology, offering an affordable, scalable alternative to conventional electrochemical systems.

A machine learning framework for explainable knowledge mining and production, maintenance, and quality control optimization in flexible circular manufacturing systems

In the present study, we employed multiple decision tree algorithms to categorize cases and reflect the most efficient policies constructed by a reinforcement learning algorithm. These approaches treated a complex production, maintenance, and quality control optimization problem within a degrading manufacturing and remanufacturing system. The decision trees’ nodes represent the independent variables, while the trees’ leaves represent the set of function values. The reinforcement learning method revealed all optimization parameters and best policies, which were employed as the training sample for the tree algorithms. After constructing every decision tree, each resulting decision rule was used to solve the optimization problem, and its performance was assessed. Additionally, we performed a sensitivity analysis to determine if the pruning level impacts the objective function value and, generally, the effectiveness of the proposed approach.

Dynamic Job and Conveyor-Based Transport Joint Scheduling in Flexible Manufacturing Systems

Efficiently managing resource utilization is critical in manufacturing systems to optimize production efficiency, especially in dynamic environments where jobs continually enter the system and machine breakdowns are potential occurrences. In fully automated environments, co-ordinating the transport system with other resources is paramount for smooth operations. Despite extensive research exploring the impact of job characteristics, such as fixed or variable task-processing times and job arrival rates, the role of the transport system has been relatively underexplored. This paper specifically addresses the utilization of a conveyor belt as the primary mode of transportation among a set of production machines. In this configuration, no input or output buffers exist at the machines, and the transport times are contingent on machine availability. In order to tackle this challenge, we introduce a randomized heuristic approach designed to swiftly identify a near-optimal joint schedule for job processing and transfer. Our solution has undergone testing on both state-of-the-art benchmarks and real-world instances, showcasing its ability to accurately predict the overall processing time of a production line. With respect to our previous work, we specifically consider the case of the arrival of a dynamic job, which requires a different design approach since there is a need to keep track of partially processed jobs, jobs that are waiting, and newly arrived jobs. We adopt a total rescheduling strategy and, in order to show its performance, we consider a clairvoyant scheduling approach, in which job arrivals are known in advance. We show that the total rescheduling strategy yields a scheduling solution that is close to optimal.

Computational analysis of ligand design for Ru half-sandwich sensitizers in bulk heterojunction (BHJ) solar cells: Exploring the role of –NO2 group position and π-conjugation in optimizing efficiency

Bulk Heterojunction (BHJ) solar cells offer cost-effective photovoltaic solutions, flexible manufacturing, and improved efficiency with reduced environmental impact. Organometallic half-sandwich complexes, specifically a series of monometallic ruthenium complexes (Ru(1)–Ru(6)), have emerged as promising candidates for BHJ solar cells due to their outstanding charge transport properties. This study utilised Density Functional Theory (DFT) calculations with the B3LYP functional and SDD basis set augmented with 6-31 + G(d,p) to assess these Ru complexes with various N,O-chelating ligands, featuring distinct π-conjugated systems and positions of nitro groups, for BHJ solar cell applications. Our investigation focused on elucidating their electronic structures, identifying key electrophilic and nucleophilic sites, and exploring their optical properties. Our findings indicate that these complexes have maximum absorption wavelengths (λmax) ranging from 702 to 1083 nm, highlighting their strong photon absorption capabilities. Specifically, Ru(6), which includes the NO2 group, exhibits the narrowest energy gap but shows relatively lower values for oscillator strengths (f) and light harvesting efficiencies (LHE), indicating efficient light absorption but reduced energy conversion efficiency. In contrast, Ru(3), lacking the NO2 group but featuring enhanced π conjugation through cyclisation, demonstrates the widest energy gap yet boasts the highest values for f and LHE, suggesting superior efficiency in converting absorbed energy into electronic energy. These results emphasise the crucial role of ligand π-conjugation in enhancing energy conversion efficiency, while the strategic positioning of the NO2 group aids effective light absorption. Our study offers valuable insights into the design principles for optimizing organometallic ruthenium complexes in BHJ solar cell applications.

Digital Twin Portrait: A Fusion and Application Method of Multisource Twin Data for Flexible Manufacturing Line

Digital Twin Portrait: A Fusion and Application Method of Multisource Twin Data for Flexible Manufacturing Line

Unleashing the power of manufacturing flexibility: enhancing performance in Bangladesh's ready-made garment industry

PurposeThis research aims to investigate how manufacturing flexibility can address the challenges of an ever-changing and unpredictable business environment in Bangladesh’s labor-intensive ready-made garment (RMG) industry, which is underserved and situated in a developing country.Design/methodology/approachUsing Partial Least Square Structural Equation Modeling, this study empirically evaluated the relationships between manufacturing flexibility, environmental uncertainty and firm performance. The analysis utilized 320 survey responses from potential RMG experts, representing 95 organizations.FindingsThe study achieved a decision-making model for implementing manufacturing flexibility in the RMG industry of Bangladesh with acceptable model fit criterion. The research pinpointed that workforce flexibility plays the maximum mediating among different types of manufacturing in coping with demand and supply uncertainty in the RMG sector.Research limitations/implicationsThe study made valuable contributions to theoretical and practical knowledge in the context of manufacturing flexibility in Bangladesh’s RMG and other underserved labor-intensive sectors in developing economies. It suggests that managers should shift from defensive and risky business strategies to more aggressive and proactive approaches by utilizing workforce flexibility resources adaptively to enhance manufacturing capabilities and align with dynamic market demand. Additionally, the study offers recommendations for future research to build upon its findings.Originality/valueThis study is unique in its approach because it presents a decision model for implementing manufacturing flexibility in a labor-intensive industry in a developing economy, specifically the RMG industry in Bangladesh, whereas previous research has primarily focused on high-tech industries in developed economies.

A mortise-and-tenon structure inspired high strength-ductility 3D printed high-entropy alloys with mechanically interlocked network

Designing special heterogeneous structures has been proven to be an effective strategy for breaking the trade-off between strength and ductility. In this work, we drew inspiration from the architectural integrity and mechanical ingenuity of the mortise-and-tenon joint, a hallmark of traditional craftsmanship, to engineer a novel mechanically interlocked network (MIN) within a 3D printed high-entropy alloy (HEA). The sub-grains crosslink not only mimics the joint in structure, but also reproduces its function in modifying the mechanical properties of the MIN. The MIN structure provides the excellent structural stability and disperses the stress concentration at the grain boundary during the deformation process, which thus avoids the fracture failure caused by the crack propagation. The alloy's remarkable performance, characterized by a tensile strength of approximately 1152 MPa and an elongation of 28%, is attributed to a symphony of underlying mechanisms, including hetero-deformation induced strengthening and hardening, dislocation tangling, stacking faults, and the formation of Lomer-Cottrell locks. These findings demonstrate the feasibility of introducing MIN heterogeneous structures to enhance the mechanical properties of HEA and promote the development of high-performance materials with manufacturing flexibility, enabling customization of their multi-scale microstructure using additive manufacturing techniques.

Improved Multi-objective Genetic Algorithm Used to Optimizing Power Consumption of an Integrated System for Flexible Manufacturing

Improved Multi-objective Genetic Algorithm Used to Optimizing Power Consumption of an Integrated System for Flexible Manufacturing