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Since much emphasis has been put on the eco-friendly manufacturing process in industries, and the complex changes in market demands, this paper focuses on a green scheduling problem of automated guided vehicles (AGVs) in a flexible manufacturing system (FMS). The studied FMS consists of multi-FMCs (flexible manufacturing cells) which have many material handling needs with time constraints. Distinguished from other AGV scheduling problems in FMS, this paper concentrates on the pickup and delivery operations or even bi-handling requirements of AGVs for FMCs, ignoring the inner production process within them. To solve this problem, a bi-objective mathematical model is built trying to minimize the total tardiness and energy consumption of AGVs simultaneously. Some properties of the problem and a no-collision algorithm are developed for the potential conflicts among AGVs. Due to the NP-hard nature of the proposed problem, a hyper-heuristic (HH) algorithm based on a double deep Q network (DDQN) is introduced, which benefits from the structures of double decision networks and multi-operator. To improve the performance of the proposed algorithm, the experience pool is used to increase the convergence speed and the crowding distance, and the non-dominated sorting strategies are presented to decide the acceptance of the new generation. Besides, in the DDQN, the states and rewards of agents are designed based on the characteristics of the scheduling problem. Finally, many experiments have been conducted and the computational results reveal that the proposed DDQN-HH algorithm outperforms the other two compared algorithms in both the convergence speed and quality of solutions. [ABSTRACT FROM AUTHOR]

Stem cell–based cell therapeutics and especially those based on human mesenchymal stem cells (hMSCs) and induced pluripotent stem cells (hiPSCs) are said to have enormous developmental potential in the coming years. Their applications range from the treatment of orthopedic disorders and cardiovascular diseases to autoimmune diseases and even cancer. However, while more than 27 hMSC-derived therapeutics are currently commercially available, hiPSC-based therapeutics have yet to complete the regulatory approval process. Based on a review of the current commercially available hMSC-derived therapeutic products and upcoming hiPSC-derived products in phase 2 and 3, this paper compares the cell therapy manufacturing process between these two cell types. Moreover, the similarities as well as differences are highlighted and the resulting impact on the production process discussed. Here, emphasis is placed on (i) hMSC and hiPSC characteristics, safety, and ethical aspects, (ii) their morphology and process requirements, as well as (iii) their 2- and 3-dimensional cultivations in dependence of the applied culture medium and process mode. In doing so, also downstream processing aspects are covered and the role of single-use technology is discussed. Key points: • Mesenchymal and induced pluripotent stem cells exhibit distinct behaviors during cultivation • Single-use stirred bioreactor systems are preferred for the cultivation of both cell types • Future research should adapt and modify downstream processes to available single-use devices [ABSTRACT FROM AUTHOR]

The trend of short-run production and personalized customization is more and more popular in the manufacturing industry. And the robots in these production lines must conduct task adjustment efficiently when learning new tasks. Thus, this paper developed the intelligent manufacturing cell based on the human–robot collaboration (HRC-IMC) which can enhance the learning ability of cobots by introducing the intelligence of human. The HRC-IMC was composed with four modules: the imitating learning module, the human–robot safety planning module, the task planning module and the visual inferring module. All of the four modules were designed to provide a set of systematic and effective methods. That was conductive to the efficiency improvement of the task adjustment for cobots' new task learning. The experimental results indicated that the efficiency of task adjustment can be increased by 42.8 % when the HRC-IMC was employed than that of Moveit. All in all, this study is of great significance for improving the efficiency of new task adjustment of cobots by imitating the manipulation experience of human via combining four algorithm modules. [ABSTRACT FROM AUTHOR]

One objective of Industry 4.0 is to reach a better system performance as well as to have a better consideration of humans. This would be done by benefiting from knowledge and experience of humans, and balancing in a reactive way some complex or complicated tasks with intelligent systems. Several studies already dealt with such an objective, but few are done at a methodological level, which forbids, for example, the correct evaluation of design choices in terms of human awareness of the situation or mental workload when designing intelligent manufacturing systems integrating the human. Indeed, increasing the intelligence and autonomy of industrial systems and their composing entities (resources, products, robots...), as fostered by Industry 4.0, increases their overall complexity. This modification reduces the ability to understand the behaviors of these systems, and leads to the difficulty for humans not only to elaborate alternative decisions when required, but also to make effective decisions and understand their consequences. This paper evaluates such a design methodology, the Cognitive Work Analysis (CWA), and its applicability when designing an assistance system to support Human in the control of Intelligent Manufacturing System in Industry 4.0. Among several functions identified through the application of CWA, the assistant system might have to integrate a digital twin of the intelligent manufacturing system. The evaluation of the methodology through the one of the designed assistant systems is done using a micro-world, which is an intelligent manufacturing cell composed of intelligent mobile ground robots, products, and static production robots interacting together and with a human supervisor in charge of the reaching of several time-based and energy-based performances indicators. The assistant system embeds a digital twin of the intelligent manufacturing system. Twenty-three participants took part in experiments to evaluate the designed assistance system. First results show that the assistance system enables participants to have a correct awareness of the situation and a correct evaluation of their alternative decisions, while their mental workload is managed and expected production performances are reached. This paper contains an analysis of these experiments and points out some limits of the CWA method in the context of Industry 4.0, especially the lack of tool enabling to specify clearly the cooperation processes between the supervisor and the intelligent manufacturing system. This paper concludes with potential research avenues, the main one being the potential benefits of coupling CWA with human–machine cooperation principles to fine tune and adapt the cooperation between the human and the intelligent manufacturing system. [ABSTRACT FROM AUTHOR]

The dual-arm cooperative manipulators system has more advantages in reconfigurability and flexibility compared with single manipulator manufacturing cell. However, random and time-varying Cartesian pose errors generated in dual-arm cooperative manipulators will be superimposed to increase the system synchronous error, which makes it difficult to complete the coordinate assembly task. To deal with the above-mentioned problems, in this paper, a novel cooperative mode-based dynamic cross-coupled sliding mode controller (DCSMC) scheme combined with position-based visual servoing (PBVS) is proposed to realize 3D dynamic tracking and positioning by correcting the movement of the dual-arm system in real time. Then, since the cross-coupled technology is incorporated into the proposed synchronous control scheme for the dual-arm system, both the Cartesian position tracking and synchronous errors of the end-effectors will simultaneously converge to zero. Moreover, the stability of the proposed DCSMC has been proved by using the Lyapunov theorem. Furthermore, to effectively demonstrate the control performance of the proposed synchronous control approach, a real-time experimental platform integrated with BECKHOFF industrial computer, C-track 780 photogrammetry sensor from Creaform, and two ESTUN industrial manipulators were developed for the implementation of the proposed DCSMC system. Finally, experimental results illustrate that the proposed scheme can improve the synchronous accuracy up to ± 0.13 mm for position accuracy and ± 62.6 × 10−5 rad for rotational accuracy respectively. [ABSTRACT FROM AUTHOR]

The goals for increased patient access and fast fulfillment have motivated considerable interest in autologous cell therapy manufacturing networks having multiple and geographically distributed manufacturing facilities. However, the cost of safety manufacturing capacity to mitigate supplier disruption risk—a significant risk in the emerging cell manufacturing industry—would be lower if manufacturing is centralized. In this paper, we analyze a decentralized network that has as its objective to minimize the cost of network resilience for mitigating supplier disruption by making use of the fact that bioreactors for autologous therapy manufacturing are small enough to be relocatable. We model this problem as a Markov decision process and develop efficient algorithms that are based on real-time demand data to minimize safety manufacturing capacity and determine how relocatable capacity should be distributed while satisfying resilience constraints. In case studies, based in part on data collected from a Chimeric antigen receptor T cell therapy manufacturing facility at the University of Pennsylvania, we compare decentralized network models with different heuristic algorithms. Results indicate that transshipment in a decentralized network can result in a significant reduction of required safety capacity, reducing the cost of network resilience. [ABSTRACT FROM AUTHOR]

In response to the policy development, the manufacturing industry is undergoing intelligent technology transformation. Online orders are characterized by multiple varieties and small batches. Therefore, in order to meet the personalized needs of more customers, it is necessary to transform the traditional production mode into the intelligent factory mode. Intelligent factories can realize green and sustainable development. Using intelligent robot technology to complete programming to design and process is an important research direction in related fields. In this context, this study strives to design a unitary production scheduling algorithm, which is implemented based on artificial intelligence technology. After testing, this algorithm has the best performance, the shortest running time, relatively low power consumption and short product processing cycle. The system design framework includes three parts: the communication design between physical control equipment and PC, the interactive control software design of PC, and the virtual controlled object model design. From the research results, it can be concluded that the realization of production scheduling algorithm design for intelligent manufacturing cells can help enterprises to make rational allocation of order size and resources, so as to improve production efficiency while taking into account the low-carbon production concept widely promoted by the international community. In this paper, a kind of effective production scheduling algorithm is studied by introducing AI technology into the field of intelligent manufacturing cell. [ABSTRACT FROM AUTHOR]

The growing demand for individualized products is becoming more and more significant and leads to a reduction in batch sizes. In particular, the production of multi-material components for lightweight design presents new challenges to the manufacturing process. This is evident when it comes to the production of individual parts, as today's processes are characterized by high tool costs and manual operations. The described challenge can be overcome by a robot-based manufacturing cell allowing the use of a novel, modular process chain in which metal parts are mechanically pre-treated, subsequently completed by additive plastic application, and afterwards finalized in a machining step to achieve the required surface qualities and geometries. In order to realize the novel process chain, robot-based solutions for free-form metal sheet processing, increased interlayer bonding strength of plastic, and multi-material machining with integrated chip extraction have to be found. Therefore, this paper presents the first approach of a robot-guided surface structuring end-effector and a concept for a direct extraction hood, which is able to be adapted specifically to the movement of the robot and the part surface, so free-form surfaces can be machined. Based on this, first experimental studies for increasing the interlayer bonding strength of plastic were carried out using an extruder set up to applicate thermoplastics onto metal at high deposition rates. To define the positioning accuracy for a robot-guided structuring process, different point to point movements have been investigated. [ABSTRACT FROM AUTHOR]

Reliability analysis of the flexible manufacturing cell (FMC) can help enterprises master the system state to formulate maintenance strategies better. However, little seems to have been done to access the reliability of the FMC. The current research on the reliability of the manufacturing system mainly focus on the components, which is not completely in line with the actual production. In this paper, we proposed a multi-state mission reliability analysis method of the FMC considering component reliability, task demand, and product quality. Firstly, the multi-state component reliability is expressed by the universal generating function (UGF) constructed by the performance states and the corresponding probabilities. Secondly, the extended state task network (ESTN) is used to integrate component performance, product quality, and task demand. We study the ESTN of the FMC with different system structures. After the above two steps, the component mission reliability can be expressed by the UGF with fewer states. Finally, the multi-state mission reliability of the FMC can be determined by the fusion rules of the UGF. An actual production case is used to illustrate the proposed method. [ABSTRACT FROM AUTHOR]

Thus far in the available literature, capability-based distributed layout (CBDL) design approaches were only developed under certain environments. Indeed, uncertainties embedded in the machine unavailability (or random machine breakdowns), product demands, and process flow data were not considered by the previous studies to achieve a robust CBDL design. However, many real-life facility layout design applications may involve different types of uncertainties simultaneously, like fuzziness and stochasticity. Based on this motivation, for the first time in the literature, this paper introduces a novel robust capability-based distributed layout (R-CBDL) design problem under a mixed fuzzy-stochastic environment. First, a new fuzzy-stochastic optimization model of the R-CBDL design problem is developed by considering the random machine breakdowns and fuzzy demand/process flow data. Then, a hybrid solution approach based on a chance-constrained stochastic programming technique with a well-known interactive fuzzy resolution method is proposed. Thus, the random machine breakdowns and fuzzy part flow rates among different machining capabilities could be easily handled via the proposed approach. Fortunately, the proposed approach can also generate various risky and risk-free robust layout design alternatives under different probabilistic scenarios and uncertainty levels (α-cuts) according to the facility designer's risk attitude. To show the validity and applicability of the proposed R-CBDL problem and hybrid solution approach, an extensive computational study with comparative analysis is first presented based on an illustrative numerical example under different machine capability overlap cases and probability distributions. Then, the performance of the proposed approach is also tested on a real-life cellular manufacturing system of a company. The computational experiments have shown that the proposed approach can accomplish more efficient robust layout design alternatives with on average 24.5% better total expected layout score when compared to the existing cellular layout of the manufacturing company. [ABSTRACT FROM AUTHOR]

Within cellular manufacturing systems (CMSs), families of parts are assigned to manufacturing cells, composed by homogeneous sets of machines. In conventional CMSs, each cell is devoted to the production of a specific part family, reducing material handling and work-in-process. Despite their flexibility, such systems still suffer from coping with the present market challenges asking for dynamic part mix and the need of agility in manufacturing. To meet these challenges, the recent literature explores the idea of including elements of the emerging reconfigurable manufacturing paradigm in the design and management of CMSs, leading to the cellular reconfigurable manufacturing system (CRMS) concept. The aim of this paper is to propose an original linear programming optimization model for the design of CRMSs with alternative part routing and multiple time periods. The production environment consists of multiple cells equipped with reconfigurable machine tools (RMTs) made of basic and auxiliary custom modules. By changing the auxiliary modules, different operations become available on the same RMT. The proposed approach determines the part routing mix and the auxiliary module allocation best balancing the part flows among RMTs and the effort to install the modules on the machines. The approach discussion is supported by a literature case study, while a multi-scenario analysis is performed to assess the impact of different CMS configurations on the system performances, varying both the number of cells and the RMT assignment to each of them. A benchmarking concludes the paper comparing the proposed CRMS against a conventional CMS configuration. The analysis shows relevant benefits in terms of reduction of the intercellular travel time (− 58.6%) getting a global time saving of about 53.3%. Results prove that reconfigurability is an opportunity for industries to face the dynamics of global markets. [ABSTRACT FROM AUTHOR]

Energy-awareness in the industrial sectors has become a global consensus in recent decades. Green scheduling is acknowledged as an effective weapon to reduce energy consumption in the industrial sectors. Therefore, this paper is devoted to the green scheduling of flexible manufacturing cells (FMC) with auto-guided vehicle transportation, where conflict-free routing of the vehicles is considered. To deal with this problem, a bi-objective optimization model is proposed to achieve the minimization of the maximum completion time and the total energy consumption in an FMC. The studied problem is an extension of flexible job shop problem which is NP-hard. Thus, an improved bi-objective salp swarm algorithm based on decomposition (IMOSSA/D) is proposed and applied to the problem. The approach is based on the decomposition of the bi-objective problem. Salp swarm intelligence along with three stochastic-distribution-based operators are incorporated into the approach, to enhance and balance its exploring and exploiting ability. Computational experiments are performed to compare the proposed approach with two state-of-the-art algorithms. This study allows the decision makers to better trade-off between energy savings and production efficiency in flexible manufacturing cellular environment. [ABSTRACT FROM AUTHOR]

A robust optimal scheduling method driven by multi-objects is proposed for the collaborative optimization problem between dynamic scheduling, preventive maintenance of equipment, and robustness of scheduling schemes in a complex manufacturing system. Firstly, the equipment maintenance task is mapped to the process level, and composite dispatching rules with weight parameters are designed, which flexibly consider equipment maintenance and system processing status. Secondly, the performance-driven ideology is analyzed through two models based on the IWOA-MLP algorithm. Thirdly, the feedback mechanism ideology facilitates adaptive closed-loop optimizations. Finally, a series of experiments were carried out on the simulation platform of a semiconductor manufacturing enterprise in Shanghai. The experimental results show that the proposed robust optimal scheduling system can effectively deal with mixed uncertainty, improve production performances, and maintain highly robust measures. [ABSTRACT FROM AUTHOR]

This paper proposes a distributed approach for smart production management in a cellular manufacturing system offering mass-customized products. This approach is based on three decision levels: factory-level (master planning module), shop floor level (bidding system) dealing with unexpected events, and cell level. The approach integrates planning, scheduling, and material handling allocation while considering real-time data from the supply chain. A mathematical model for factory-level planning is proposed with two sequence-based resolution approaches implemented on two metaheuristics, NSGAII and SMPSO. These were tested on a numerical example after optimizing their parameters tuning. [ABSTRACT FROM AUTHOR]

Battery cells are central components of electric vehicles. It is important for automotive original equipment manufacturer (OEM) to utilize high-quality battery cells to ensure high performance and safety of their vehicles. This results in the high demand for quality control measures and inspection methods in battery cell manufacturing. Particular relevant features of battery cells are welds for the internal electrical contact. Failures of these welds are often the cause for battery defects in the field and scrap during production. Consequently, there is a strong need to evaluate all welds during manufacturing. However, there is no established method that allows a quick, comprehensive, and cheap inline measurement of the weld quality. This paper presents a new eddy current-based method for nondestructive testing of seam welds as well as a machine learning approach for its validation. A deep learning model has been trained on eddy current measurements to predict results from a reference inspection method, in this case computer tomography. The results prove that eddy current measurements can be used to replicate data acquired by computer tomography, which means that eddy current measurements could be a suitable candidate for nondestructive 100 % inline inspection. More general, this study demonstrates how machine learning may help to get deeper insights into measurement results and to validate new nondestructive testing techniques whose detailed features are yet unknown. The presented evaluation method enables understanding the capabilities and the limits of a new technique and to extract hidden features from the data. In terms of defect segmentation, the trained model applied to an eddy current test data set achieves an accuracy of 93.7 %. Furthermore, the usage of machine learning allows to perform evaluations on artificial product samples with specific defects and features, which avoids the costly production physical samples. [ABSTRACT FROM AUTHOR]

So far, a great bulk of research has been dedicated to demand fluctuations in the make-to-order (MTO) environment, and several solutions have been proposed. In contrast, cellular layouts have received little attention from researchers. Also, in studies on dynamic cell production systems, the concept of dynamics is limited to the possibility of making changes between periods. This paper presents a new mathematical model to create an integrated system of order prioritizing, capacity measurement, and scheduling for dealing with new orders in MTO environments arranged by a cellular manufacturing layout. In this model, it is possible to negotiate the price and delivery time. The periods are also considered to be connected, and unlike the existing dynamic cellular manufacturing systems (DCMSs), it is possible to receive an order at any moment. Moreover, machine relocation is allowed with regard to the time of relocation during the periods. Another notable feature is the alternative routing for parts and cell formation (CF) at the same time as scheduling. The proposed model has two objectives including a) maximizing the profit and b) maximizing the number of orders accepted based on their priorities. The bi-objective model for small sizes has been solved by GAMS software using the augmented epsilon-constraint method. The effects of key parameters as well as some important features of the model, such as the possibility of checking the acceptance/rejection of a new order during the program, are addressed. Generally, the proposed model is able to develop a DCMS in an online cellular manufacturing system (OCMS). The results of sensitivity analysis show that the integration of CF, GS, order acceptance, pricing, and delivery time in a mathematical model simultaneously can significantly improve system performance and increase system profits. Finally, an NSGA-II algorithm is developed to solve the problem in larger sizes. To verify the computational effectiveness of the employed NSGA-II in comparison with a CPLEX solver, its performance is evaluated using two methods. [ABSTRACT FROM AUTHOR]

Lean manufacturing is a systematic method to improve productivity and reduce costs for each industrial. One of the tools for lean is the cellular manufacturing that divides the production system into smaller subsystems. The objective is to reduce inventory, setup times, and material handling costs. The manufacturing cell formation problem presents the first issue faced in designing a cellular manufacturing system. In this paper, the presence of alternative process routings and machine reliability in addition to production volume, operation sequence and production time makes the problem more complex but also more realistic and appealing to real-world applications. Most authors solve this kind of problems by mathematical programming approaches that require large amounts of computational efforts. Therefore, a meta-heuristic algorithm, entitled Hybrid cuckoo search for the generalized cell formation problem, is considered in this paper. A set of benchmark problems with various scales is used to further evaluate the performance of the proposed method. Comparison of the results obtained with those obtained by LINGO and by the simulated annealing method reveals the better effectiveness and efficiency of our method. [ABSTRACT FROM AUTHOR]

The objective of this paper is to investigate the potential added value of being able to relocate production capacity, relative to fixed production capacity, in a network of multiple, geographically distributed manufacturing sites. There is a growing number of examples of production capacity that can be geographically relocated with a modest amount of effort; e.g., 3D printers, bioreactors for cell and gene manufacturing, and modular units for pharmaceutical intermediates. Such a capability shows promise for enabling the fast fulfillment of a distributed network with a reduction in the total inventory and total production capacity of a distributed network with fixed production capacity without sacrificing customer service levels or total system resilience. Allowing also for transshipment, we model a production-inventory system with L production sites and Y units of relocatable production capacity, develop efficient and effective heuristic solution methods for dynamic relocation and multi-location inventory control, and analyze the potential added value and implementation challenges of being able to relocate production capacity. We describe the (L, Y) problem as a problem of sequential decision making under uncertainty to determine transshipment, mobile production capacity relocation, and replenishment decisions at each decision epoch. To enhance model realism, we use a partially observed stochastic process, the modulation process, to model the exogenous and partially observable forces (e.g., the macro-economy) that affect demand. We then model the (L, Y) problem as a partially observed Markov decision process. Due to the considerable computational challenges of solving this model exactly, we propose two efficient, high quality heuristics. We show for an instance set with five locations that production capacity mobility and transshipment, relative to the fixed production capacity case, can improve systems performance by as much as 41% on average over the no-flexibility case and that production capacity mobility can yield as much as 10% more savings compared to when only transshipment is permitted. [ABSTRACT FROM AUTHOR]

This paper presents a nonlinear multi-objective mathematical model to obtain quality solutions for design problems of cellular manufacturing systems. The objectives of the multi-objective model are, simultaneously, (1) to minimize the number of exceptional elements among manufacturing cells, (2) to minimize the number of voids in a cell, and (3) to minimize cell load variation. In this paper, a new multi-objective genetic algorithm (GA) approach has been proposed to solve the multi-objective problem. In contrast to existing GA approaches, this GA approach contains some revised genetic operators and uses a conic scalarization method to convert the mathematical model's objectives in a single objective function. This approach has been tested and compared with two test problems and some source models collected from the literature. The results have shown that the problem-solving performance of the proposed multi-objective approach is at least as good as the existing approaches in designing the cellular system, and in many cases better than them. [ABSTRACT FROM AUTHOR]

Reducing gear vibration as well as its weight is a challenging field latterly. This research deals with the manufacturing of a gear having cellular lattice body structure by using Selective Laser Melting (SLM) technology. A cellular lattice design defines a complex structure which typically cannot be fabricated by conventional manufacturing technologies. On the other hand, the SLM technology enables the production of such complex cellular lattice structures directly from their computer-aided designed (CAD) models. The cellular gear body structure was designed and optimized by employing topology optimization software ProTOp and fabricated by SLM using Ti-6Al-4V alloy. The problems faced by the researchers and the solution which enabled the manufacturing of this gear by SLM are described in this paper. To estimate the performance of the gear during operation, the sound pressure was measured and compared to the results obtained with the gear having a solid body. The experimental results show that the investigated cellular lattice structure of the gear body is well capable to reduce the mass and vibration of the gear. [ABSTRACT FROM AUTHOR]

Cellular manufacturing design is apprehensive about the conception and activity of cells to take the benefits of adaptability, effective flow, and high creation rate. The way toward forming manufacturing cells with the greatest efficiency is the most critical strides in cellular manufacturing. In this paper, a new monarch butterfly optimization (MBO) and firefly (FF)-based meta-heuristic is proposed to solve a multi-objective cell formation problem (CFP). This hybridized MBO–FF acquires optimal arrangements in a worthy measure of time, particularly for big size problems also focused to enhance the working of CFP. This algorithm is competent to investigate the search space viably and recognize the global optimal within a short measure of time. Here, percentage of exceptional elements, machine utilization, grouping efficacy and cell efficiency are measured for the performance enhancement. Computational outcome of the presented MBO–FF herein demonstrates superior or equivalent to the benchmark instance collected from the literature. [ABSTRACT FROM AUTHOR]

In order to increase the flexibility, space utilization, product quality and safety, transferring the semi-manufactured parts between machines is performed by automated guided vehicles (AGVs). AGVs should be properly managed to maintain their efficiency as a material handling equipment in production systems. This AGVs' management will be more important where the circumstances will be dynamic (i.e., where the volume and variety of demands are different one period to another). To determine one or several homes for idle AGVs (AGVs' dwell point), the assignment of AGVs to manufacturing cell and to prevent the AGVs collisions are three basic aspects in their management. This paper proposes a new nonlinear mixed-integer bi-objective mathematical model of dynamic cell formation and dwell point location problem (DCFDPLP) for AGVs on unidirectional single loop in which AGVs assignment, location of dwell points for AGVs and transportation time of AGVs are considered besides cell reconfiguration, part families and machine groups formation in a dynamic environment. The first objective is to minimize the related costs, and the second one is to minimize the maximum response time for all AGVs. The nonlinear model is transformed to linear one. Due to the NP-hardness of DCFDPLP, a non-dominated sorting genetic algorithm (NSGA-II) is developed to solve the problem. Finally, randomly generated test problems are generated to demonstrate the performance of NSGA-II as a solution procedure. [ABSTRACT FROM AUTHOR]

Human tissue plasminogen activator was the first recombinant therapy protein that successfully produced in Chinese hamster ovary cells in 1986 and approved for clinical use. Since then, more and more therapeutic proteins are being manufactured in mammalian cells, and the technologies for recombinant protein production in this expression system have developed rapidly, with the optimization of both upstream and downstream processes. One of the most promising strategies is expression vector cassette optimization based on the expression vector cassette. In this review paper, these approaches and developments are summarized, and the future strategy on the utilizing of expression cassettes for the production of recombinant therapeutic proteins in mammalian cells is discussed. [ABSTRACT FROM AUTHOR]

Because the data generated in the complex industrial manufacturing processes is multi-sourced and heterogeneous, it brings a challenge for addressing decision-making optimization problems embedded in the whole manufacturing processes. Especially, for inconsistent lithium-ion cell screening as such a special problem, it is a tough issue to fuse data from multiple sources in a lithium-ion cell manufacturing process to screen cells for relieving the inconsistency among cells in a battery pack with multiple cells configured in series, parallel, and series-parallel. This paper proposes a data-driven decision-making optimization approach (DDDMO) for inconsistent lithium-ion cell screening, which takes into account three dynamic characteristic curves of cells, thus ensuring that the screened cells have consistent electrochemical characteristics. The DDDMO method uses the convolutional auto-encoder to extract features from different characteristics curves of lithium-ion cells through multi-channels and then the features in different channels are combined into fusion features to build a feature base. It also proposes an effective sample generation approach for imbalanced learning using the conditional generative adversarial networks to enhance the feature base, thereby efficiently training a classifier for inconsistent lithium-ion cell screening. Finally, industrial applications verify the effectiveness of the proposed approach. The results show that the missing rate of inconsistent lithium-ion cells drops by an average of 93.74% compared to the screening performance in the single dynamic characteristic of cells, and the DDDMO approach has greater accuracy for screening cells at lower time costs than the existing methods. [ABSTRACT FROM AUTHOR]

This paper presents a systematic methodology to enable environmental sustainability and productivity performance assessment for integrated process and operation plans at the machine cell level of a manufacturing system. This approach determines optimal process and operation plans from a range of possible alternatives that satisfy the objectives and constraints. The methodology provides a systematic procedure to highlight parameters that have significant impact on both sustainability and productivity performance metrics. We developed models and applied them to analyze manufacturing life cycle scenarios for collecting and categorizing key concepts towards building a material information model for sustainability. Integration of process and operation plans allows globalized assessment of sustainability and productivity, while development of a multi-criteria decision-making method leads to optimization of process planning activities based on the impact of conflicting sustainability and productivity metrics. A case study is detailed to demonstrate the sustainability-focused methodology, wherein integrated simulation and optimization techniques are used to support analysis of candidate scenarios and selection of preferred alternatives from a finite set of alternate process and operation plans. A discrete event simulation tool is used to model evolution of sustainability metrics (e.g., energy consumption) and productivity metrics (e.g., production time, cost) of a shop floor. The outcomes of this work include determination of optimized feature sequence plans which optimize various key performance indicators depending on stakeholder interest based on time, sustainability and production cost. [ABSTRACT FROM AUTHOR]

Establishing how to effectively manufacture cell therapies is an industry-level problem. Decentralised manufacturing is of increasing importance, and its challenges are recognised by healthcare regulators with deviations and comparability issues receiving specific attention from them. This paper is the first to report the deviations and other risks encountered when implementing the expansion of human pluripotent stem cells (hPSCs) in an automated three international site–decentralised manufacturing setting. An experimental demonstrator project expanded a human embryonal carcinoma cell line (2102Ep) at three development sites in France, Germany and the UK using the CompacT SelecT (Sartorius Stedim, Royston, UK) automated cell culture platform. Anticipated variations between sites spanned material input, features of the process itself and production system details including different quality management systems and personnel. Where possible, these were pre-addressed by implementing strategies including standardisation, cell bank mycoplasma testing and specific engineering and process improvements. However, despite such measures, unexpected deviations occurred between sites including software incompatibility and machine/process errors together with uncharacteristic contaminations. Many only became apparent during process proving or during the process run. Further, parameters including growth rate and viability discrepancies could only be determined post-run, preventing 'live' corrective measures. The work confirms the critical nature of approaches usually taken in Good Manufacturing Practice (GMP) manufacturing settings and especially emphasises the requirement for monitoring steps to be included within the production system. Real-time process monitoring coupled with carefully structured quality systems is essential for multiple site working including clarity of decision-making roles. Additionally, an over-reliance upon post-process visual microscopic comparisons has major limitations; it is difficult for non-experts to detect deleterious culture changes and such detection is slow. [ABSTRACT FROM AUTHOR]

Cellular structures are made up of an interconnected network of plates, struts, or small unit cells and acquire many unique benefits such as, high strength-to-weight ratio, excellent energy absorption, and minimizing material requirements. When compared with the complicated conventional processes, additive manufacturing (AM) technology is capable of fabricating geometries in almost all types of shapes, even with the small cellular structures inside, by adding material layer-by-layer directly from the digital data file. All major industries have been exploiting the benefits of cellular structures due to their prevalence over a wide range of research fields. To date, there are a few state-of-the-art reviews compiled focusing on a specific area of lattice structures, but many aspects still need to be reviewed. Therefore, this paper aims to provide a comprehensive review of the various lattice morphologies, design, and the AM of the cellular structures. Furthermore, the superior properties of the additively fabricated structure, as well as the applications and challenges, are presented. The conducted review has identified the significant limitations and gaps in the existing literature and has highlighted the areas that need further research in the design, optimization, characteristics, and applications, and the AM of the cellular structures. This review would provide a more precise understanding and the state-of-the-art of AM with the cellular structures for engineers and researchers in both academia and industrial applications. [ABSTRACT FROM AUTHOR]

The cellular manufacturing technology, an application of group technology in manufacturing, has been a widely studied combinatorial optimization problem where the entire production system is divided into many cells and part families. In this paper, a novel clonal selection algorithm (CSA) that uses a new affinity function and part assignment heuristic for solving a multi-objective cell formation problem is studied. The proposed CSA has been hybridized with genetic algorithm for generating feasible cell sequences that fulfill both mutual exhaustivity and exclusion properties of machine cells prior to the initial population generation. Additionally, a new part assignment heuristic function that maps parts to machine cells and a novel basic affinity function have been built into the proposed CSA so that it can act as the utility function to solve the multi-objective cell formation problem. This hybrid CSA (HCSA) has been presented and computational results have been obtained for the proposed scheme with a set of 52 benchmark instances collected from literature. The results presented herein demonstrate that overall proposed HCSA is much more promising in comparison with existing approaches available in recent literatures. Extensive statistical and convergence tests have been carried out to ratify the superiority of the proposed HCSA. The improvements can be attributed to the collaborative interactions in the CSA mechanism, the proposed hybridization for initial population generation and so forth. [ABSTRACT FROM AUTHOR]

In this paper, an integrated mathematical model of multi-period cell formation and part operation tradeoff in a dynamic cellular manufacturing system is proposed in consideration with multiple part process route. This paper puts emphasize on the production flexibility (production/subcontracting part operation) to satisfy the product demand requirement in different period segments of planning horizon considering production capacity shortage and/or sudden machine breakdown. The proposed model simultaneously generates machine cells and part families and selects the optimum process route instead of the user specifying predetermined routes. Conventional optimization method for the optimal cell formation problem requires substantial amount of time and memory space. Hence a simulated annealing based genetic algorithm is proposed to explore the solution regions efficiently and to expedite the solution search space. To evaluate the computability of the proposed algorithm, different problem scenarios are adopted from literature. The results approve the effectiveness of the proposed approach in designing the manufacturing cell and minimization of the overall cost, considering various manufacturing aspects such as production volume, multiple process route, production capacity, machine duplication, system reconfiguration, material handling and subcontracting part operation. [ABSTRACT FROM AUTHOR]

Intercell transfers are inevitable for the manufacturing of complicated products, which disrupts the philosophy of cellular manufacturing and leads new challenges to the field of production scheduling. The issue of intercell scheduling is analyzed in the context of a cellular manufacturing system consisting of multiple single-processing machines and one batch-processing machine, which is derived from the actual manufacturing of complicated assemblies in the equipment manufacturing industry. Since the two types of machines are different from, even contrary to, each other in some constraint conditions, a combinational ant colony optimization (CACO) approach is developed in this paper, which designs two structures for the single-processing machines and the batch-processing machine, respectively. By updating pheromone trails integratedly and scheduling the single-processing operations and the batch-processing operations simultaneously, cooperative optimization for the two types of machines is achieved in the CACO. Minimizing the maximum completion time is taken as the scheduling objectives. Computational results show that the CACO has significant advantages comparing with other approaches and the CPLEX, and is especially suitable for the large dimension problems. [ABSTRACT FROM AUTHOR]

In this paper, a visual, data-driven operational level lean maturity model is developed. The model can be used to assess level of lean maturity and to compare it to performance results in different axes of manufacturing cells in order to evaluate lean effectiveness. As demonstrated in this paper, to measure effectiveness of lean manufacturing, both inputs (tools and processes) and outputs (performance) are measured separately and analyzed together. A case study is carried out for gathering data, analysis, and explanatory study of results. Qualitative and quantitative data on lean capability and performance of two manufacturing cells is collected using historical data and audit. A scoring system based on the major and minor non-conformances is suggested to quantify the indicators of leanness. Minimum of fuzzy membership values is selected to calculate overall performance. Then, the results of leanness are compared with performance to highlight the gaps of lean effectiveness. Results of the study show that the developed model can be used to measure both leanness and lean effectiveness through assessment of lean performance. The model can be applied by practitioners as a framework to design and develop a company-specific lean maturity model. [ABSTRACT FROM AUTHOR]

Quantum-dot cellular automata (QCA) emerged as a viable alternative to CMOS technology in nanotechnology. Despite the potential advantages of QCA compared with CMOS technology, QCA circuits often suffer from various manufacturing defects such as cell omission, cell displacement, and extra-cell deposition, which make them unreliable and fault-prone. Hence, it is essential in QCA technology to design a fault-tolerant circuit. The design of an optimized, high-speed RAM memory has received increasing attention of researchers as the memory unit is an essential component in the digital systems. In this paper, a new design of a fault-tolerant RAM cell is introduced using two proposed fault-tolerant three- and five-input majority gates. The proposed fault-tolerant majority gates have been evaluated in the presence of variety of defects such as cell omission, cell displacement and extra-cell deposition. The QCADesigner 2.0.3 and QCAPro tools are used for our simulations. The results indicate that the proposed majority gates have less energy consumption and more fault tolerance in comparison with the previous works. Furthermore, some physical calculations are presented to assess the functionality of the proposed gates. [ABSTRACT FROM AUTHOR]

The immense design freedom offered by additive manufacturing yields tremendous benefits, but it also raises the question of how to best go about designing components to exploit this design freedom. In early design phases, design tools need to utilize this freedom, but do so with low computational cost and with a good estimate of the final weight to correctly assess the part's influence on the overall product design. This is especially important for aerospace designs. In this paper, a design methodology is devised for cellular structures to be manufactured using additive manufacturing techniques, specifically for such early design phases. This design methodology uses adaptive meshing techniques to design the topology of the cellular structure, after which the struts of that cellular structure are optimized separately to reduce the dimensionality of the problem. The method is demonstrated for a small generic bracket and an aircraft bracket design from the literature. For these problems, our method is fast enough to evaluate many thousands of design options. This results in identifying promising candidate designs for further detailed design work. [ABSTRACT FROM AUTHOR]

The formation of manufacturing cells forms the backbone of designing a cellular manufacturing system. In this paper, we present a novel intelligent particle swarm optimization algorithm for the cell formation problem. The proposed solution method benefits from the advantages of particle swarm optimization algorithm (PSO) and self-organization map neural networks by combining artificial individual intelligence and swarm intelligence. Numerical examples demonstrate that the proposed intelligent particle swarm optimization algorithm significantly outperforms PSO and yields better solutions than the best solutions existed in the literature of cell formation. The application of the proposed approach is examined in a case problem where real data is utilized for cell reconfiguration of an actual company involved in agricultural manufacturing sector. [ABSTRACT FROM AUTHOR]

Cellular manufacturing is an important application of group technology and the cell formation process is one of the important steps in designing cellular manufacturing system. In recent years, researchers have noticed potential benefits when the layout problem is considered within the cell formation process. Nevertheless, there are not sufficient studies about consideration of real-life features in the cell formation and layout design process. In this paper, a new approach is presented to integrate the cell formation and its layout design. The proposed approach has three important design features not found in other papers. These design features are multi-row intra-cell layout (layout of machines within the cells), continuous inter-cell layout (layout of rectangular shape cells on the planar area), and aisle distance. The objective of the proposed approach is to form machine cells, find the layout of machine cells, and obtain the arrangement of machines within the cells such that the total material handling cost is minimized. In order to have a more accurate layout design, the material handling cost is calculated in terms of the actual position of machines within the cell. Due to the computational complexity of the proposed problem, a heuristic method is proposed to solve medium- and large-scale problems in a reasonable computational time. Three lower bounds are developed for the proposed integrated problem in which the tightest of them is chosen for evaluating the solution of the heuristic method. Finally, numerical examples adopted from the literature are solved to verify the performance of the proposed heuristic method and illustrate the advantages of the proposed integrated approach. The results indicated that the heuristic method is both effective and efficient in solving real-sized problems. The results also demonstrated that the proposed layout approach gives better layout design in comparison with the existing approaches. [ABSTRACT FROM AUTHOR]

This paper addresses designing a dynamic cellular manufacturing system which considers optimizing batch sizes for inter-cell moves in order to pursue fundamentals of Just-In-Time production philosophy. To do so, a bi-objective mixed-integer nonlinear programming model is proposed with two conflicting objective functions: minimizing sum of machine purchasing, operating, inter-cell moves, machine relocation, and machine transferring cost, and minimizing work-in-process (WIP) with regard to inter-cell batch sizes. Also, the best time to sell unused machines is obtained by calculating their salvage values. Finally, the proposed model is validated using numerical experiments and hence, the resulted WIP inventories are assessed through the conducted numerical experiments, and sensitivity analysis is provided with respect to three major parameters (machine purchasing cost, inter-cell material handling cost, and part demand) applied in the proposed model. [ABSTRACT FROM AUTHOR]

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The intriguing biomineralization process in nature endows the mineralized biological materials with intricate microarchitected structures in a facile and orderly way, which provides an inspiration for processing ceramics. Here, we propose a simple and efficient manufacturing process to fabricate cellular ceramics in programmed cell-based 3D configurations, inspired by the biomineralization process of the diatom frustule. Our approach separates the ingredient synthesis from architecture building, enabling the programmable manufacturing of cellular ceramics with various cell sizes, geometries, densities, metastructures, and constituent elements. Our approach exploits surface tension to capture precursor solutions in the architected cellular lattices, allowing us to control the liquid geometry and manufacture cellular ceramics with high precision. We investigate the geometry parameters for the architected lattices assembled by unit cells and unit columns, both theoretically and experimentally, to guide the 3D fluid interface creation in arranged configurations. We manufacture a series of globally cellular and locally compact piezoceramics, obtaining an enhanced piezoelectric constant and a designed piezoelectric anisotropy. This bioinspired, surface tension-assisted approach has the potential to revolutionize the design and processing of multifarious ceramic materials for structural and functional applications in energy, electronics and biomedicine. Three-dimensional architecture engineering allows for higher performance ceramics with more diverse applications. Here, the authors report a simple and efficient manufacturing process for cellular ceramics in programmed cell-based 3D configurations. [ABSTRACT FROM AUTHOR]

In a virtual cell formation system, machine types are formed for one period during which the machine types of a cluster process the corresponding operations of part types. However, there is one major difference between a virtual cellular manufacturing and a cellular manufacturing and that is the fact that machine types of the same cluster are not necessarily brought to a physical proximity in virtual cellular manufacturing, contrary to a cellular manufacturing. Depending on the variations in the demand for part types, the virtual cells are clustered periodically and merged according to the demand for new part types in a planning horizon. On the other hand, optimal raw material quantity to purchase from qualified suppliers has gained importance recently; this phenomenon is greatly influenced by the significant portion of raw materials costs in a finished product. Consequently, most firms have to pay out most of their revenues on transportation and purchasing. In this paper, the dynamic virtual cellular manufacturing is formulated through a mathematical model developed for this purpose; it is also of utmost importance to consider machine layout and quantity of raw material purchased from qualified suppliers. To solve the real-sized problems of proposed model, a hybrid metaheuristic algorithm is extended. The results demonstrate that the proposed hybrid genetic algorithm is promising. [ABSTRACT FROM AUTHOR]

In this paper, the flow-shop sequence-dependent group scheduling (FSDGS) problem is addressed with reference to the makespan minimization objective. In order to effectively cope with the issue at hand, a hybrid metaheuristic procedure integrating features from genetic algorithms and random sampling search methods has been developed. The proposed technique makes use of a matrix encoding able to simultaneously manage the sequence of jobs within each group and the sequence of groups to be processed along the flow-shop manufacturing system. A well-known problem benchmark arisen from literature, made by two, three and six-machine instances has been taken as reference for both tuning the relevant parameters of the proposed procedure and assessing performances of such approach against the two most recent algorithms presented in the body of literature addressing the FSDGS issue. The obtained results, also supported by a properly developed ANOVA analysis, demonstrate the superiority of the proposed hybrid metaheuristic in tackling the FSDGS problem under investigation. [ABSTRACT FROM AUTHOR]

This paper presents three main contributions: (i) an experimental analysis of variables, using well-defined statistical patterns applied to the main parameters of the welding process. (ii) An on-line/off-line learning and testing method, showing that robots can acquire a useful knowledge base without human intervention to learn and reproduce bead geometries. And finally, (iii) an on-line testing analysis including penetration of the bead, that is used to train an artificial neural network (ANN). For the experiments, an optic camera was used in order to measure bead geometry (width and height). Also real-time computer vision algorithms were implemented to extract training patterns. The proposal was carried out using an industrial KUKA robot and a GMAW type machine inside a manufacturing cell. We present expermental analysis that show different issues and solutions to build an industrial adaptive system for the robotics welding process. [ABSTRACT FROM AUTHOR]

The formation of machine cells to process subsequent part families is not the only goal of the designing of an efficient cellular manufacturing system (CMS). A competent layout of the newly acquired cells is also essential to restrict the total inter-cell material handling cost which is primarily significant with large production volume. Furthermore, in realistic industrial scenario, the uncertainty of product demand can influence the layout configuration to be altered from period to period. Albeit there are numerous articles exist in the domain of CMS research considering cell formation problems, layout issues have not been addressed significantly. Therefore, the aim of our paper is to portray a reformed mathematical model of the inter-cell layout design problem in dynamic production situation considering material handling cost and a modified proximity relationship of manufacturing cells. The proposed Quadratic Assignment Programming (QAP) model is combinatorial in nature and is difficult to solve using traditional exact solution methods. The state-of-the-art soft-computing techniques are extremely advantageous for such QAP paradigms. Thus, we developed an improved genetic algorithm (IGA) and a simulated annealing heuristic (SAH) to sort out the abovementioned problem. Due to the inadequacy of datasets, we formed small to large size test problems (6 × 6 × 2 to 24 × 24 × 10) in logical way to cater the purpose. The proposed algorithms are successfully employed to attain near-optimal solutions to the test problems. Computational results demonstrate the proficiency of the IGA over SAH for all the test problems in terms of solution quality and computational time. In addition, we conducted a statistical data analysis to validate the test results. [ABSTRACT FROM AUTHOR]

In this paper we consider a job shop scheduling problem with blocking (BJSS) constraints. Blocking constraints model the absence of buffers (zero buffer), whereas in the traditional job shop scheduling model buffers have infinite capacity. There are two known variants of this problem, namely the blocking job shop scheduling with swap allowed (BWS) and the one with no swap allowed (BNS). This scheduling problem is receiving an increasing interest in the recent literature, and we propose an Iterated Greedy (IG) algorithm to solve both variants of the problem. IG is a metaheuristic based on the repetition of a destruction phase, which removes part of the solution, and a construction phase, in which a new solution is obtained by applying an underlying greedy algorithm starting from the partial solution. A comparison with recent published results shows that the iterated greedy algorithm outperforms other state-of-the-art algorithms on benchmark instances. Moreover it is conceptually easy to implement and has a broad applicability to other constrained scheduling problems. [ABSTRACT FROM AUTHOR]

This research develops a new methodology for a cell formation problem in the cellular manufacturing system. The methodology includes two phases. In the first phase, an improved similarity coefficient method, which considers the operation sequence and the number of repeated operations firstly in related researches, is proposed to identify part families. A new decomposed mathematical model is presented in the second phase, which considers some crucial operational aspects such as alternative routing, machine capacity, part demand, operation time, and lot splitting, to assign machines into part families for minimum machine cost, operation cost, and inter-cell movement cost. The model puts emphasis on the effect of trade-off between machine duplication and material inter-cell movement on performance of the cell formation to optimize machine utilization and workload balance. This paper also provides a concrete production schedule with optimum system utilization for cell formation. Test problems and sensitivity analyses are carried out to reveal the effectiveness and feasibility of the proposed methodology. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of scheduling operations in cellular manufacturing systems. A concurrent approach is proposed for job shop cell scheduling while the objective is to minimize the makespan. An integer linear programming model is developed by considering exceptional elements, intercellular moves, intercellular transportation times, and sequence-dependent family setup times. A modification of the model with reduced number of functional constraints is then presented to enhance the efficiency of the model in terms of computational time. In order to efficiently solve real-size problems in a reasonable amount of time, a heuristic approach based on the genetic algorithm (GA) is also developed. The proposed GA is enhanced with problem-specific knowledge of the search space by using a specialized repair strategy, a decoding procedure based on the concept of active schedules and a problem-specific mutation operator which prevents the algorithm from searching redundant solutions. Results reveal that the modified mixed integer linear programming model significantly reduces the computational time and the proposed GA is capable of providing promising solutions to large-scale problems. [ABSTRACT FROM AUTHOR]

This paper presents a dynamic multi-objective mixed integer mathematical model for cell formation problem with probabilistic demand and machine reliability analysis, where the total system costs, machine underutilization cost, and maximum system failure rate over the planning time periods are to be minimized simultaneously. The total system cost objective function calculates machine operating, internal part production, intercellular material handling, and subcontracting costs. Since in this type of problems, objectives are in conflict with each other, so finding an ideal solution (a solution that satisfies all objectives simultaneously) is not possible. Therefore, this study uses the augmented ε-constraint method (to solve small size problems) and a nondominated sorting genetic algorithm (NSGAII) (to solve large size problems) to find the Pareto optimal frontier that decision makers can select her/his preferred solution. Numerical examples will be solved to demonstrate the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Allocation of operators is one of the most important factors determining the performance of a cellular manufacturing system (CMS). This paper presents a new integrated method which combines genetic algorithm (GA), simulation, and data envelopment analysis (DEA) in order to select the best operator allocation scenario. The proposed method first generates, by GA, a feasible set of operator allocation scenarios, after which it evaluates the efficiency of those scenarios using DEA with multiple objectives that are extracted from a simulation. The proposed method has two main advantages over the previous DEA-based operator allocation methods. First, whereas the previous methods require the decision maker to predefine all feasible scenarios, the proposed method does not. Second, whereas the previous methods determine only the number of operators assigned to each machine, the proposed method provides not only the number of operators but also additional knowledge on who is assigned to which machine. The results obtained from a practical case study are presented herein to illustrate the effectiveness and superiority of the proposed methodology. [ABSTRACT FROM AUTHOR]

Repairing large tissue defects often represents a great challenge in clinics due to issues regarding lack of donors, mismatched sizes, irregular shapes, and immune rejection. Three-dimensional (3D) printed scaffolds are promising for growing cells and manufacturing tissue constructs because of the intricate control over pore size, porosity, and geometric shape, but the lack of biomimetic surface nanotopography and restricted biomolecule presenting capacity render them less effective in regulating cell responses. Besides, electrospinning is a rapid and simple manner to fabrication of different forms of nanofibrous scaffolds and cause the novel and complex scaffold constructs for many tissue engineering applications. In this review article, we begin with a thorough discussion on the method of producing 1D, 2D, and 3D electrospun nanofiber materials. In particular, we insisted on the techniques which are developed to fabricate 3D scaffolds. We categorized these techniques into four groups, direct fabrication, post-processing method, the combination of textile method and electrospinning, and a combination of electrospinning and 3D printing. Then, we tried to explain their application in many clinical scopes, especially in drug delivery, regenerative medicine, and cancer treatment. Ultimately, we discussed future actions which could be done to improve nanomedicine and nanotechnology researches. [ABSTRACT FROM AUTHOR]

To address the problem of how to identify the best time to implement reconfiguration for the reconfigurable manufacturing system (RMS), a dynamic complexity-based RMS reconfiguration point decision method is proposed. This method first identifies factors that affect RMS dynamic complexity (including both positive and negative complexity) at the machine tool and manufacturing cell levels. Next, based on information entropy theory, a quantitative model for RMS dynamic complexity is created, which is solved via state probability analysis for processing capability and the processing function. This model is combined with cusp catastrophe theory to establish an RMS reconfiguration decision model. Both positive and negative complexity are control variables for cusp catastrophe. Cusp catastrophe’s state condition is used to identify RMS state catastrophe at the final stage of production. This catastrophe point is the RMS reconfiguration point. Finally, the case study result shows that this method can effectively identify the RMS state catastrophe moment so that system reconfiguration is implemented promptly to improve RMS’s responsiveness to the market. [ABSTRACT FROM AUTHOR]