Flexible Production Processes Research Articles

A biogeography-based optimization algorithm with local search for large-scale heterogeneous distributed scheduling with multiple process plans

Since modern production mode has shifted from a single factory to a multi-factory production network, distributed scheduling has been derived. Distributed scheduling problem (DSP) is characterized by many varieties, large scale, redundant production factories, flexible production processes, and high-value products. Each factory in the heterogeneous DSP can be considered an individual entity, and there may be several production process plans. To address the heterogeneous DSP with multiple process plans, we consider minimizing the global makespan over all factories containing the transportation time delivering tasks from factories to their destinations and propose a biogeography-based optimization algorithm combined with local search based on heuristic rules (BBO-LH) to find the optimal production plan and enhance productivity. First, a new encoding scheme with three-segment representation has been developed to avoid illegal solutions and realize the information sharing between solutions selecting different process plans. Then two efficient local search approaches have been proposed based on different heuristic rules on sequence and equipment allocation respectively, to improve the search efficiency. Besides, BBO-LH has adopted a cosine migration rate model to replace the linear one to strengthen the ability to jump out of local optima. BBO-LH is compared with a genetic algorithm (GA_X), using generated examples to test the performance of the proposed strategies, and simulation results show the effectiveness of the proposed BBO-LH on large-scale heterogeneous DSP with multiple process plans.

Nacre-like carbon fiber-reinforced biomimetic ceramic composites: Fabrication, microstructure, and mechanical performance

The unique structure of nacre is composed of 'bricks' and ‘mortar,’ which gives it excellent damage resistance. In this study, nacre-like bionic ceramic scaffolds were 3D-printed using vat photopolymerization (VPP) technology, filled with polymer, and reinforced with carbon fiber to create high-strength and high-toughness carbon fiber-reinforced bionic composites. This approach leverages additive manufacturing technology to easily overcome the challenges of fabricating complex bionic structures with a simple and flexible production process. It offers a practical method for developing other lightweight, high-strength, and tough bionic materials. The microstructure of the composites was observed using scanning electron microscopy and energy dispersive spectroscopy. Additionally, finite element method and molecular dynamics simulations were performed on the composites. The results indicate that the composite material increased the flexural strength by 293.52 % and the fracture toughness by 109.36 % compared to solid ceramic parts. The primary toughening mechanisms of carbon fiber include crack deflection, fiber “pull-out,” and “debonding” within the composite.

Development of a Two-Finger Haptic Robotic Hand with Novel Stiffness Detection and Impedance Control.

Haptic hands and grippers, designed to enable skillful object manipulation, are pivotal for high-precision interaction with environments. These technologies are particularly vital in fields such as minimally invasive surgery, where they enhance surgical accuracy and tactile feedback: in the development of advanced prosthetic limbs, offering users improved functionality and a more natural sense of touch, and within industrial automation and manufacturing, they contribute to more efficient, safe, and flexible production processes. This paper presents the development of a two-finger robotic hand that employs simple yet precise strategies to manipulate objects without damaging or dropping them. Our innovative approach fused force-sensitive resistor (FSR) sensors with the average current of servomotors to enhance both the speed and accuracy of grasping. Therefore, we aim to create a grasping mechanism that is more dexterous than grippers and less complex than robotic hands. To achieve this goal, we designed a two-finger robotic hand with two degrees of freedom on each finger; an FSR was integrated into each fingertip to enable object categorization and the detection of the initial contact. Subsequently, servomotor currents were monitored continuously to implement impedance control and maintain the grasp of objects in a wide range of stiffness. The proposed hand categorized objects' stiffness upon initial contact and exerted accurate force by fusing FSR and the motor currents. An experimental test was conducted using a Yale-CMU-Berkeley (YCB) object set consisted of a foam ball, an empty soda can, an apple, a glass cup, a plastic cup, and a small milk packet. The robotic hand successfully picked up these objects from a table and sat them down without inflicting any damage or dropping them midway. Our results represent a significant step forward in developing haptic robotic hands with advanced object perception and manipulation capabilities.

The intersection of mechatronics and precision engineering: Synergies and future directions

The convergence of mechatronics and precision engineering has sparked significant interest due to its potential to revolutionize various industries, ranging from manufacturing to healthcare. This review explores the synergies between these two domains and outlines potential future directions for research and application. Mechatronics, an interdisciplinary field combining mechanical engineering, electrical engineering, computer science, and control engineering, focuses on the design and development of intelligent systems with integrated sensing, actuation, and control capabilities. Precision engineering, on the other hand, emphasizes the fabrication of high-accuracy components and systems, often at micro or nanoscales, to meet stringent performance requirements. The intersection of mechatronics and precision engineering offers numerous synergistic opportunities. One such opportunity lies in the development of precision actuators and sensors with embedded intelligent control systems, enabling precise motion control and feedback mechanisms in complex systems. Furthermore, advancements in microfabrication techniques allow for the integration of sensors and actuators directly onto precision components, leading to miniaturized and efficient mechatronic systems. In manufacturing, this synergy facilitates the creation of highly automated and flexible production processes capable of producing intricate and high-quality products with minimal human intervention. In healthcare, it enables the development of precision medical devices for diagnostics, surgery, and drug delivery, enhancing patient outcomes and reducing the invasiveness of procedures. Future directions in this interdisciplinary field include exploring novel materials and manufacturing techniques to further improve the precision and performance of mechatronic systems. Additionally, advancements in artificial intelligence and machine learning are poised to enhance the autonomy and adaptability of mechatronic systems, enabling them to learn from data and adapt to changing environments in real-time. the intersection of mechatronics and precision engineering holds great promise for addressing complex engineering challenges and driving innovation across various industries. By leveraging their complementary strengths, researchers and engineers can unlock new opportunities to create smarter, more efficient, and more precise systems with transformative impacts on society.

Role of Flexible Operation of a Wastewater Treatment Plant in the Reduction of Its Indirect Carbon Dioxide Emissions—A Case Study

The increasing share of renewables in electricity grids comes with a challenge of energy surpluses and deficits, which needs be handled by demand side management (DSM) and storage options. Within this approach, wastewater treatment plants (WWTPs), with flexible energy consumption and production processes and storage units, can contribute to stabilizing the grids and integrating more renewables. In this study, the operation of a real WWTP was optimized by mixed integer linear programming (MILP) to minimize its indirect carbon dioxide (CO2) emissions. The operation of the WWTP was shown to be flexible in following the CO2 emission factor of the electricity grid, which was possible with the utilization of the WWTP’s storage units and flexible co-substrate feeding. As a result, by changing only the operational behavior of the WWTP, its indirect CO2 emissions decreased by 4.8% due to the higher share of renewables in the electricity grid. The CO2 emissions were shown to decrease further up to 6.9% by adding virtual storage units.

Digital twin architecture for assembly line performance monitoring

The forthcoming industrial requirements to ensure increasingly flexible and agile production processes will require an ever-increasing ability to recognise, predict and optimise the state of processes with respect to the planned state. Advanced tools such as Digital Twins (DT) and Cyber Physical Systems (CPS) can help manage and address these problems. This paper presents some results of the authors’ research on the adoption of a basic methodology for the design and implementation of Digital Twins aimed at manufacturing applications and subsequent integration into a Cyber Physical Production Systems (CPPS). Reference publications were selected based on the level of detail of DT and CPS architectures. The aim is also to combine methodological development with industrial tools and requirements. The architecture is potentially applicable to different production scenarios. Finally, the first results of application on a scale production line are shown.

Corrosion behavior of selective laser melted 6061 aluminum alloy electrodes for aluminum-air battery

6061 aluminum alloy can be used as an anode for aluminum-air batteries. Compared with cast one, three-dimensional (3D) printed aluminum alloy shows more flexible design and production process. Moreover, the self-corrosion behavior of 3D printed aluminum alloy without grinding its surface layer in alkaline solutions (electrolyte for aluminum-air batteries) is unclear. Here, the comparison between 3D-printed and cast 6061 aluminum alloy is investigated by self-corrosion testing, potentiodynamic polarization, electrochemical impedance spectroscopy, and hydrogen bubble visualization experimentation. The results show that although the corrosion resistance of the 3D-printed 6061 aluminum alloy decreases rapidly with time, it exhibits higher corrosion resistance at the initial stage of corrosion than that of cast 6061 aluminum alloy due to the “fish scale” layer and may be able to improve its discharge performance. The surface microscopy analysis results are in good agreement with those of the self-corrosion test and electrochemical characteristics.

Multi-objective evolutionary scheduling based on collaborative virtual workflow model and adaptive rules for flexible production process with operation reworking

Dynamic event disturbances during production such as reworking of defective operations more challenge scheduling optimization. Efficient response to operation reworking is crucial for collaborative optimization of production quality and scheduling performance. Given the complex constraints within production processes of jobs involving key operation reworking and uncertainty of dynamic disturbances, a two-level virtual workflow modeling method and a multi-objective evolutionary scheduling algorithm with adaptive rules (MOESA-AR) based on collaborative virtual workflow framework are proposed. Virtual workflow modeling fulfills linear abstraction of tasks to be scheduled and priority constraints. Through decision arrangement, task state tracking and information feedback, collaborative virtual workflow model constitutes framework for dynamic update of task workflows and real-time response to rescheduling triggered by event disturbances. MOESA-AR adopts two-dimensional chromosome evolution and heuristic rule training to adapt to the complexity of scheduling optimization. Adaptive strategy of dispatching rules enhances the dynamic adaptability of decision-making to variable scheduling scenarios, thereby improving the dominance level of multi-objective trade-off optimization under changing demand conditions. Experiments confirm the superiority of MOESA-AR in improving scheduling performance and the effectiveness of collaborative virtual workflow scheduling framework in responding to dynamic disturbances.

Triple Space-Time Yield in Discontinuous Antibody Biomanufacturing by Combination of Synergetic Process Intensification Strategies.

Monoclonal antibodies are the workhorse of the pharmaceutical industry due to their potential to treat a variety of different diseases while providing high specificity and efficiency. As a consequence, a variety of production processes have been established within the biomanufacturing industry. However, the rapidly increasing demand for therapeutic molecules amid the recent COVID-19 pandemic demonstrated that there still is a clear need to establish novel, highly productive, and flexible production processes. Within this work, we designed a novel discontinuous process by combining two intensification strategies, thus increasing inoculation density and media exchange via a fluidized bed centrifuge, to fulfill the need for a flexible and highly productive production process for therapeutic molecules. To establish this new process, firstly, a small-scale experiment was conducted to verify synergies between both intensification strategies, followed by a process transfer towards the proof-of-concept scale. The combination of these two-process intensification measures revealed overall synergies resulting in decreased process duration (-37%) and strongly enhanced product formation (+116%) in comparison to the not-intensified standard operation. This led to an impressive threefold increase in space-time yield, while only negligible differences in product quality could be observed. Overall, this novel process not only increases the ways to react to emergency situations thanks to its flexibility and possible short development times, but also represents a possible alternative to the current established processes due to high increases in productivity, in comparison to standard fed-batch operations.

Designing an adaptive and deep learning based control framework for modular production systems

AbstractIn today’s rapidly changing production landscape with increasingly complex manufacturing processes and shortening product life cycles, a company’s competitiveness depends on its ability to design flexible and resilient production processes. On the shop-floor, in particular, the production control plays a crucial role in coping with disruptions and maintaining system stability and resilience. To address challenges arising from volatile sales markets or other factors, deep learning algorithms have been increasingly applied in production to facilitate fast-paced operations. In particular deep reinforcement learning frequently surpassed conventional and intelligent approaches in terms of performance and computational efficiency and revealed high levels of control adaptability. However, existing approaches were often limited in scope and scenario-specific, which hinders a seamless transition to other control optimization problems. In this paper, we propose a flexible framework that integrates a deep learning based hyper-heuristic into modular production to optimize pre-defined performance indicators. The framework deploys a module recognition and agent experience sharing, enabling a fast initiation of multi-level production systems as well as resilient control strategies. To minimize computational and re-training efforts, a stack of trained policies is utilized to facilitate an efficient reuse of previously trained agents. Benchmark results reveal that our approach outperforms conventional rules in terms of multi-objective optimization. The simulation framework further encourages research in deep-learning-based control approaches to leverage explainability.

A biogeography-based optimization algorithm with modified migration operator for large-scale distributed scheduling with transportation time

To expand production scale and enhance enterprise competitiveness, modern supply chains and manufacturing systems have shifted from the single factory production to the multi-factory production network with the characteristics of large scale, diversification of varieties, redundant production factories, flexible production processes, and high-value products. This production mode is called distributed scheduling. The transportation time caused by geographically distributed factories in Distributed Scheduling Problem (DSP) with a series-parallel network plays an important role in the scheduling scheme. To minimize the global makespan over all factories, a two-segment representation has been taken as an encoding scheme while the decoding procedure applies the First Come First Served (FCFS) heuristic rule. A Biogeography-Based Optimization with Modified Migration Operator (BBO-MMO) is then proposed to address the heterogeneous DSP with transportation time between factories at adjacent processing stages, which improves the migration operator by modifying the immigration rate according to processing time and transportation time. A cosine migration model has been adopted to enhance the ability of BBO-MMO to break through local optimum. BBO-MMO is compared with the Improved Memetic Algorithm (IMA), and several groups of examples with various parameters have been generated to test the performance of BBO-MMO. The results show that the proposed BBO-MMO can effectively solve the large-scale heterogeneous DSP with transportation time.

The Impact of the Covid-19 Pandemic on Supply Chains (an Applied Study on the Industrial Sector in the Kingdom of Saudi Arabia - the Packaging Sector)

The COVID-19 pandemic has had a profound and ongoing impact on supply chains, affecting various aspects such as restrictions on movement, transportation methods, and the challenges of producing goods and services due to the virus's impact on human resources. Addressing these supply chain disruptions is crucial to ensure the smooth operation of global supply chains, supporting societies and commerce. Hence, the current study aims to examine the influence of the pandemic on raw materials in packaging plants in KSA and shed light on its impact on supply chains in those facilities, specifically the availability of spare parts. The study holds significance from both theoretical and practical perspectives. Theoretical importance lies in contributing to existing research by exploring the variables and their interrelationships, highlighting the significance of the selected sample, and paving the way for future related studies. On the practical side, the study's findings are valuable for developing strategies and solutions to prevent similar disruptions in the future. The study concludes that the COVID-19 pandemic has prompted companies to embrace a technology-driven work culture, optimize resource utilization, and expand the adoption of remote work practices. Additionally, the focus is on fostering mutually beneficial relationships with suppliers, ensuring flexible production and supply processes, incorporating local and international specifications into products, accommodating customer preferences, and striving to deliver high-quality and efficient products that meet customer demands.

Converting semantic web services into formal planning domain descriptions to enable manufacturing process planning and scheduling in industry 4.0

To build intelligent manufacturing systems that react flexibly in case of failures or unexpected circumstances, manufacturing capabilities of production systems must be utilized as much as possible. Artificial Intelligence (AI) and, in particular, automated planning can contribute to this by enabling flexible production processes. To efficiently leverage automated planning, an almost complete planning domain description of the real-world is necessary. However, creating such planning descriptions is a demanding and error-prone task that requires high manual efforts even for domain experts. In addition, maintaining the encoded knowledge is laborious and, thus, can lead to outdated domain descriptions. To reduce the high efforts, already existing knowledge can be reused and transformed automatically into planning descriptions to benefit from organization-wide knowledge engineering activities. This paper presents a novel approach that reduces the described efforts by reusing existing knowledge for planning and scheduling in Industry 4.0 (I4.0). For this purpose, requirements for developing a converter that transforms existing knowledge are derived from literature. Based on these requirements, the SWS2PDDL converter is developed that transforms the knowledge into formal Planning Domain Definition Language (PDDL) descriptions. The approach’s usefulness is verified by a practical evaluation with a near real-world application scenario by generating failures in a physical smart factory and evaluating the generated re-planned production processes. When comparing the resulting plan quality to those achieved by using a manually modeled planning domain by a domain expert, the automatic transformation by SWS2PDDL leads to comparable or even better results without requiring the otherwise high manual modeling efforts.

High-quality TbF3 coating prepared by suspension plasma spraying technique for improving the grain boundary diffusion efficiency of sintered Nd-Fe-B magnets

High-quality TbF3 coating used for the grain boundary diffusion process (GBDP) of sintered Nd-Fe-B was prepared by the suspension plasma spraying (SPS) technique. The obtained TbF3 coating with SPS process showed a dense and continuous structure with the uniform distribution as well as a good adhesion with the Nd-Fe-B substrate. Compared with the uncoated Nd-Fe-B magnet without heavy rare earth, the coercivity of TbF3 coated Nd-Fe-B magnet enhanced from 1160 kA/m to 1824 kA/m after the GBDP heat treatment. The microstructure and composition analysis indicated that the (Nd, Tb)2Fe14B shell formed at the periphery of Nd2Fe14B grains throughout the whole sample magnet. The shell with high magnetocrystalline anisotropy effectively suppressed the nucleation of reverse domain and repaired the defects on the grain surface of the sintered Nd-Fe-B magnets, thus enhancing the coercivity. As a consequence, the SPS technology has advantage of high depositing rate, flexible production process, good boundary adhesion and homogeneous-dense structure, which is expected to become a promising candidate way of depositing diffusion coating for the GBDP of sintered Nd-Fe-B in industrial production.

Examining the Health Experiences of Women Working in Flexible Production During the Pandemic Process: A Qualitative Study

Objective: This study examine the health status of women in the flexible production process during the pandemic period. 
 Material and Method: In this study, the qualitative research method was preferred. Maximum variation, sampling method, was used to determine the study group. The data were collected using a semi-structured interview form with the in-depth interview technique and the interviews lasted for 15-20 minutes. Content analysis was used in the research. The study was carried out with 15 people until data repetition occurred. 
 Results: In the research, the category was determined and the categories are as follows: Changes in women's working life before and after the pandemic, economic problems during remote working, housework during the pandemic, being affected by the care of the elderly and children, the barriers of being a woman in business life during the pandemic and gender equality in business life before and after the pandemic. 
 Conclusion and Recommendations: It has been determined that women pay attention to protection measures during the pandemic period. It was found that women were tired and stressed due to housework and childcare, lost income due to full closure during the pandemic period, exposed to double standards in their workplaces. It is recommended that future research, including men, compare perceptions during the pandemic.

Stochastic simulation uncertainty analysis to accelerate flexible biomanufacturing process development

Motivated by critical challenges and needs from biopharmaceuticals manufacturing, we propose a general metamodel-assisted stochastic simulation uncertainty analysis framework to accelerate the development of a simulation model with modular design for flexible production processes. There are often very limited process observations. Thus, there exist both simulation and model uncertainties in the system performance estimates. In biopharmaceutical manufacturing, model uncertainty often dominates. The proposed framework can produce a confidence interval that accounts for simulation and model uncertainties by using a metamodel-assisted bootstrapping approach. Furthermore, a variance decomposition is utilized to estimate the relative contributions from each source of model uncertainty, as well as simulation uncertainty. This information can be used to improve the system mean performance estimation. Asymptotic analysis provides theoretical support for our approach, while the empirical study demonstrates that it has good finite-sample performance.

Unlocking the Benefits of Mobile Manipulators for Small and Medium-Sized Enterprises: A Comprehensive Study

The mobile manipulator is a relatively recent development in the robotics domain. It is a type of robot system that extends the manipulation capabilities of a robotic arm by the mobility capabilities of a mobile platform. As a result, mobile manipulators promise to provide a wider usage of manipulators (for example, by extending their reach) and to enable automation of use cases that are still manually performed. They can, for instance, automate repetitive tasks, freeing human workers to focus on more valuable tasks. They can also be deployed to perform dangerous or difficult tasks, reducing the risk of workplace injuries. Additionally, using mobile manipulators can help meet the growing demand for fast and flexible production processes, enabling them to better compete in a rapidly changing market. However, integrating these systems bears complexity, high acquisition costs, and unsolved safety and security challenges. Therefore, this new approach is only beginning to be adopted in the manufacturing industry. Especially small and medium-sized companies (SMEs) suffer from these challenges, as they usually have fewer resources. However, mobile manipulators are a valuable tool to enhance their competitiveness and profitability in various industries. In order to unleash these benefits, this paper provides a comprehensive review of application cases of mobile manipulators within smart factory setups. It focuses on specific requirements and advantages for SMEs.

A Method for Dynamic Insertion Order Scheduling in Flexible Job Shops Based on Digital Twins

Various production disturbances occurring in the flexible job shop production process may affect the production of the workshop, some of which may lead to the prolongation of production completion time. Therefore, a flexible job shop dynamic scheduling method based on digital twins is proposed and a dynamic scheduling framework is constructed. Compared with the traditional workshop, the digital twin-based flexible job shop can upload the relevant production data of the physical workshop to the data management center in real time, and after fusion processing the data can work cooperatively with the upper application system. Taking the dynamic disturbance of rush order insertion as an example, the dynamic scheduling of insertion order is realized based on the dynamic scheduling framework, and then the production efficiency is improved. To achieve the shortest completion time, a mathematical model for dynamic scheduling optimization is established and a genetic algorithm (GA) is applied to solve the model. Finally, a practical case is applied to show that the completion time of this algorithm is reduced by 35%, which verifies the feasibility of the proposed dynamic scheduling method.

Flexible methanol production process using biomass/municipal solid waste and hydrogen produced by electrolysis and natural gas pyrolysis

Biomass-derived liquid fuels with low greenhouse gas emissions are an integral part of decarbonization plans. Three pathways for enhancing production of methanol from biomass and municipal solid waste (MSW), natural gas, and renewable electricity are explored using hydrogen produced from water electrolysis, natural gas pyrolysis, or combinations of these inputs. A combined electrolysis and natural gas pyrolysis process is designed to be flexible, changing operation modes depending on the cost of feedstocks (i.e. electricity and natural gas). A techno-economic analysis is performed to assess and compare the economic attractiveness of these processes. Hydrogen produced from natural gas pyrolysis could potentially be more economically attractive than electrolytic hydrogen using renewable electricity. Moreover, natural gas pyrolysis CO2 emissions could be substantially lower than emissions from conventional steam methane reforming (e.g., CO2 emissions which are 25 % or lower compared to CO2 emission from steam methane reforming). Hydrogen enhancement of the methanol production process results in increase of around a factor of two in carbon conversion efficiency (e.g. from 44% to 94 %). Methane pyrolysis shows high economic potential assuming the technical challenges to its commercialization are successfully addressed. Given an installed cost of electrolyzer of 1000 $/kW, electricity price of 50 $/MWh, natural gas price of 5 $/GJ and 100 $/ton selling price of carbon black, the natural gas pyrolysis design results in the lowest methanol production cost. Our analysis indicates that methanol could be produced in a price range of 300–1000$/ton depending on feedstock price (particularly electricity) and the chosen process.

Thermochemical recycling of plastics – Modeling the implications for the electricity system

To achieve a circular economy, we need to reinvent the ways in which plastic products are produced, used and recycled. This study investigates the cost-optimal design and operation of an electrified process for the production of plastics that employs thermochemical recycling of plastics and waste. In addition, the impact of this process on the north European electricity system is investigated. A techno-economic optimization model, with the objective of meeting the demand for electricity and plastic to the lowest cost, is developed. The model minimizes the investment and operating costs of electricity and plastic production units while meeting the demands for electricity and plastics without adding carbon-dioxide to the atmosphere. The model considers different flexibility options that can be applied in the plastics production process.A fully flexible plastics production process that has flexibility in relation to time, location and CO2 utilization shows the lowest cost for plastics production and the highest carbon circularity. At the same time, a fully flexible process has the lowest capacity utilization rate, i.e., there is an investment in overcapacity. The results show that a process with flexibility in time renders 100% carbon recovery beneficial, whereas inflexible operation of the plastics production process requires the development and scaling-up of carbon capture and storage facilities. Furthermore, the results show that for the thermochemical production of plastics, the availability of large volumes of waste and favorable conditions for generating electricity at low cost determine the location of the plastics production units. The additional electricity demand to produce plastics is mainly covered by increased generation from wind and nuclear power plants, while wind and solar power dominate in the modeled electricity system mix.