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This white paper was developed to provide leukapheresis guidance for the collection of mononuclear cells from adult and pediatric patients who are destined for immune effector cell (IEC) therapies for commercial and research applications. Currently, there is considerable variability in leukapheresis processes and limited published information regarding best practices relevant to new cellular therapies, especially IECs. Herein the authors address critical leukapheresis questions in five domains to help guide consistent collection processes and ensure high-quality products. The first four domains are onboarding, pre-collection, collection and post-collection, with protocol feasibility, preparation, care and follow-up of the patient/donor at each step, respectively, and technical considerations during collection. The fifth domain of quality assurance focuses on ensuring product potency, purity, safety and auditing. The American Society for Apheresis (ASFA) Clinical Applications Committee (IEC Therapy Subcommittee) was charged by the society's board of directors with working collaboratively with other ASFA committees and organizations, including the Foundation for the Accreditation of Cellular Therapy, Association for the Advancement of Blood and Biotherapies, American Society for Transplantation and Cellular Therapy, National Marrow Donor Program and International Society for Cell & Gene Therapy, to develop guidelines regarding leukapheresis collection of cells destined for the manufacture of IEC therapies. After a review of the literature and discussion with members of the involved committees and various institutions, a draft guidance was created and circulated for comment and revision. Critical aspects of apheresis that could affect the quality and quantity of the leukapheresis product were identified. These areas were then discussed and reviewed. After consensus, the best practice guidelines were proposed and accepted. In the current era of rapid growth of IEC therapies, it is important to address critical leukapheresis steps to provide high-quality products and more consistent practices and to eliminate redundant efforts. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this article is to conduct a bibliometric analysis of the cellular manufacturing system (CMS) literature published from 1982–2021 to identify key issues and trends for the future. Design/methodology/approach: A six-stage methodology is used to conduct a literature review, which includes: (1) article collection; (2) inclusion/exclusion criteria; (3) reviewing the articles; (4) analyzing the articles; (5) framework development; and (6) future research directions. A total of 936 CMS-specific articles are reviewed. This paper made use of three software tools: the R package, VOSviewer and SciMAT. Findings: According to the findings, the majority of CM researchers focused on cell formation and design. The USA, Iran and India are the top three leading publishers. Additionally, the gap and future direction of CM are discussed. Originality/value: To the best of the authors' knowledge, the current study is the first attempt to investigate CMS evaluation through bibliometric and thematic analysis and provides a decisional framework as well as steps for CMS adoption. For individuals who are interested in understanding more about CMS and its evolution, this paper offers a starting point. [ABSTRACT FROM AUTHOR]

Cellular manufacturing represents a production system arrangement in which machines, tools, workers, and devices are grouped to produce a single product or a group of products with similar production requirements. By implementing cellular manufacturing, it is possible to significantly influence the elimination of the seven types of Lean waste: transport, inventory, unnecessary motion, waiting, overproduction, overprocessing, and defects. There are various models of a cellular manufacturing organization, and some of the most widely used and studied include the Toyota Sewing System, Bucket Brigades, Working Balance, and Rabbit Chase. This paper aims to present different types of lean cellular manufacturing organizations. By reviewing the literature, the advantages and disadvantages of individual types of cellular manufacturing will be systematized. In the practical part of the paper, the theoretical assumptions will be confirmed, and the impact and robustness of individual types of cellular manufacturing will be explored in different situations. Simulations were performed in real conditions on real products to demonstrate the efficiency of individual types of cellular manufacturing organizations depending on the duration of technological operations. The goal was also to examine the robustness of individual types of cellular organization in case of the absence of certain operators or insufficiently trained operators. The criteria used to compare different cellular models were productivity, non-conformance, WIP inventory, time to deliver the first correct piece, and flow time. Simulations were performed for the Toyota Sewing System, Bucket Brigade, Working Balance, and Rabbit Chase cellular manufacturing concepts. The simulation results indicate significant differences in the performance of individual concepts, where the difference in some criteria can reach up to 100%. [ABSTRACT FROM AUTHOR]

This paper deals with an overview of flowshop group scheduling problems in the manufacturing environment. The aim of this paper is twofold: (i) making a comprehensive survey of research on flowshop group scheduling problems in manufacturing systems, and (ii) presenting a bibliometric analysis. We address the general definition of flowshop group scheduling problems and provide a taxonomy of methodologies used in previous literature. The papers are presented from several perspectives, including the utilised objective functions, a transformation of problem structure, benchmarks in existing literature, and solution approaches. Additionally, bibliometric analysis, including keyword and journal analyses, is conducted for articles published between 1986 and 2022. Finally, suggestions for future developments are listed to further consolidate this area. [ABSTRACT FROM AUTHOR]

Feasibility checking is a step in manufacturing system engineering for verifying the normalization and effectiveness of a manufacturing system associated with a specific configuration of resources and processes. It enables factory operators to predict problems before operational time, thus preventing equipment and machinery accidents and reducing labor waste in physically organizing the shop floor. In Industry 4.0, feasibility checking becomes even more critical since emerging challenges, such as mass personalization, require reconfiguring work cells quickly and flexibly on demand. Regarding this need, digital twin technologies have emerged as an ideal candidate for practicing feasibility checking. Indeed, they are tools used to implement digital representations of manufacturing entities that can constitute a digital environment and context. Factory operators can test a manufacturing process within a digital environment in different contexts before the execution with physical resources. This approach currently receives significant attention from the manufacturing community; however, there is still a lack of sharing experiences to implement it. Thus, this paper contributes a methodology to engineer a digital environment and context for a manufacturing work cell using AAS digital twins and physics-based 3D digital twins technologies. Technically, this methodology is a specific case of N-DTs, a general methodology for engineering heterogeneous digital twins. The product assembly line case study, also presented in this paper, is a successful experiment applying the above contributions. The two methodologies and the case study can be helpful references for both public and private sectors to deploy their feasibility-checking frameworks and deal with heterogeneous digital twins in general. [ABSTRACT FROM AUTHOR]

The printing company's process performance depends on the possibility of providing requested products and managing the existing constraints of fixed machine layouts and high setup times between different products. Process inefficiencies caused by these factors reflect on throughput, production times, and resource utilization. The changes that improve one part of the production system usually affect other parts, needing additional optimization, and it is very useful to test the feasibility of proposed solutions with simulation before implementation. This paper presents a new algorithm for continuous improvement of enterprises performance, combining the lean approach with cellular manufacturing, and simulation. The performance is observed in a way that a certain setup influences the system in its entirety, rather than on a specific part of that system. The results are presented through models developed within the production optimization phase, representing various ways in which the continuous improvement algorithm can unfold. Each of them comes with its advantages and disadvantages, all intending to create more efficient production processes that generate less production waste. [ABSTRACT FROM AUTHOR]

This paper provides a literature review and an analysis of the studies related to workforce reconfiguration strategies as a part of workforce planning for various production environments. The survey demonstrates that these strategies play a crucial role in the resilience and flexibility of manufacturing systems since they help industrial companies to quickly adapt to frequent changes in demand both in terms of volume and product mix. Five strategies are considered: the use of utility, temporary, walking, cross-trained workers, and bucket brigades. They are analysed in the context of mixed and multi-model manual assembly lines, dedicated, cellular, flexible, and reconfigurable manufacturing systems. The review shows that most of the researches on these reconfiguration strategies focus on multi- or mixed-model assembly lines. At the same time, few studies consider workers team reconfiguration in flexible and reconfigurable manufacturing systems. Finally, this paper reveals several promising research directions in workforce reconfiguration planning, namely, the use of both machine and workforce reconfigurations, consideration of the ergonomic aspects, the combination of multiple workforce reconfiguration strategies, the study of workforce reconfiguration in human-robot collaborative systems, and the use of new technologies in human-machine industrial environments. [ABSTRACT FROM AUTHOR]

Various software environments have been developed in the past to create digital twins of single cells or a digital twin of a factory. Each environment has its own strengths and weaknesses and has been designed with a specific focus. The environments that are able to holistically simulate complete factories are limited in terms of the modelling details required for the analysis of single manufacturing cells (e.g., manufacturer-independence of the individual digital twins) and their ability for virtual commissioning. This paper presents three options for realising a virtual commissioning of linked cells using a 3D integration platform with NVIDIA Omniverse, consisting of two different digital models fused into a combined model, also representing material flow. First, with a source/sink solution and unidirectional connector controlled by OPC UA; secondly, with a bidirectional connector, developed in the course of this elaboration, and an extension of the 3D integration platform controlled by Apache Kafka; thirdly, with a bidirectional connector and using only an extension of the 3D integration platform. The research demonstrates that virtually commissioning multiple linked digital twins from different manufacturers in a 3D platform with material flow makes a significant contribution to the industrial metaverse. [ABSTRACT FROM AUTHOR]

This paper presents a collaborative manufacturing cell implemented in a laboratory setting, focusing on developing learning-based interaction abilities to enhance versatility and ease of use. The key components of the system include 3D real-time volumetric monitoring for safety, visual recognition of hand gestures for human-to-robot communication, classification of physical-contact-based interaction primitives during handover operations, and detection of hand–object interactions to anticipate human intentions. Due to the nature and complexity of perception, deep-learning-based techniques were used to enhance robustness and adaptability. The main components are integrated in a system containing multiple functionalities, coordinated through a dedicated state machine. This ensures appropriate actions and reactions based on events, enabling the execution of specific modules to complete a given multi-step task. An ROS-based architecture supports the software infrastructure among sensor interfacing, data processing, and robot and gripper controllers nodes. The result is demonstrated by a functional use case that involves multiple tasks and behaviors, paving the way for the deployment of more advanced collaborative cells in manufacturing contexts. [ABSTRACT FROM AUTHOR]

The expected large growth in electric mobility presents challenges, such as requiring a very large amount of critical raw materials like nickel, cobalt, and lithium. Due to this expected growth significant amounts of production scrap from cell and battery manufacturing will be generated. Over the next decade, increasingly larger amounts of Li-ion batteries from electric vehicles will also reach their end-of-life. Hence, in order to close the loop, the development and industrialization of sustainable battery recycling flowsheets are key so that both production scrap and end-of-life batteries can be recycled back to their 'battery grade' building blocks. Battery recycling flowsheets are typically categorized into two categories: (1) 'Pyro-Hydro', a combination of battery smelting in a pyrometallurgical process, followed by the further refining of the alloy via hydrometallurgy; and (2) '(Thermo)mechanical-Hydro', a combination of (thermo)mechanical pretreatment and further hydrometallurgical refining of the resulting black mass. In this paper, a carbon footprint analysis is presented comparing these two battery recycling approaches: 'Pyro-Hydro' and 'Thermomechanical-Hydro', taking into account the impact of the latest evolutions in process technology and efficiency. To facilitate this comparison, a prospective LCA was carried out for the respective flowsheets. The quantitative analysis shows that 'Pyro-Hydro' leads to the lowest overall carbon footprint but also that both 'Pyro-Hydro' and 'Thermomechanical-Hydro' flowsheets have their challenges and opportunities for decarbonization. The inclusion of the fate of side streams such as graphite and electrolyte in the analysis is shown to be critically important in order to gain an objective and complete view. [ABSTRACT FROM AUTHOR]

In exploring the challenges of bone repair and regeneration, this review evaluates the potential of bone tissue engineering (BTE) as a viable alternative to traditional methods, such as autografts and allografts. Key developments in biomaterials and scaffold fabrication techniques, such as additive manufacturing and cell and bioactive molecule-laden scaffolds, are discussed, along with the integration of bio-responsive scaffolds, which can respond to physical and chemical stimuli. These advancements collectively aim to mimic the natural microenvironment of bone, thereby enhancing osteogenesis and facilitating the formation of new tissue. Through a comprehensive combination of in vitro and in vivo studies, we scrutinize the biocompatibility, osteoinductivity, and osteoconductivity of these engineered scaffolds, as well as their interactions with critical cellular players in bone healing processes. Findings from scaffold fabrication techniques and bio-responsive scaffolds indicate that incorporating nanostructured materials and bioactive compounds is particularly effective in promoting the recruitment and differentiation of osteoprogenitor cells. The therapeutic potential of these advanced biomaterials in clinical settings is widely recognized and the paper advocates continued research into multi-responsive scaffold systems. [ABSTRACT FROM AUTHOR]

In this paper, a novel bi-objective mathematical model is proposed to design a four-dimensional (i.e., part, machine, operator, and tool) Cellular Manufacturing System (CMS) in a dynamic environment. The main objectives of this model are to: (1) Minimize total costs including tools processing cost, costs of transporting cells between various cells, machine setup cost, and operators' educational costs and (2) Maximize the skill level of operators. The developed model is strictly NP-hard and exact algorithms cannot find globally optimal solutions in a reasonably computational amount of time. Thus, a Multi-Objective Vibration Damping Optimization (MOVDO) algorithm with a new solution structure that satisfies all the constraints and generates feasible solutions is proposed to find near-optimal solutions in a reasonably computational amount of time. Since there is no benchmark available in the literature, three other meta-heuristic algorithms (i.e., Non-dominated Sorting Genetic Algorithm-II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Invasive Weeds Optimization (MOIWO)) with a similar solution structure are developed to validate the performance of the proposed MOVDO algorithm for solving various instances of the developed model. The result of comparing their performances based on statistical tests and different measuring metrics reveals that the proposed MOVDO algorithm remarkably outperforms other meta-heuristics used in this paper. [ABSTRACT FROM AUTHOR]

Rapid advances in new generation information technologies, such as big data analytics, internet of things (IoT), edge computing and artificial intelligence, have nowadays driven traditional manufacturing all the way to intelligent manufacturing. Intelligent manufacturing is characterised by autonomy and self-optimisation, which proposes new demands such as learning and cognitive capacities for manufacturing cell, known as the minimum implementation unit for intelligent manufacturing. Consequently, this paper proposes a general framework for knowledge-driven digital twin manufacturing cell (KDTMC) towards intelligent manufacturing, which could support autonomous manufacturing by an intelligent perceiving, simulating, understanding, predicting, optimising and controlling strategy. Three key enabling technologies including digital twin model, dynamic knowledge bases and knowledge-based intelligent skills for supporting the above strategy are analysed, which equip KDTMC with the capacities of self-thinking, self-decision-making, self-execution and self-improving. The implementing methods of KDTMC are also introduced by a thus constructed test bed. Three application examples about intelligent process planning, intelligent production scheduling and production process analysis and dynamic regulation demonstrate the feasibility of KDTMC, which provides a practical insight into the intelligent manufacturing paradigm. [ABSTRACT FROM AUTHOR]

One of the main concepts in group technology (GT) is the cellular manufacturing system (CMS) with three main problems of cell formation (CF), cell layout (CL), and cell scheduling (CS). This paper studies the cell layout problem (CLP), aiming to find the optimal layout of machines within each cell (intracellular layout) and the optimal layout of cells in each workshop (intercellular layout). To adapt to reality, the dimensions of the cells and machines (inside each cell) were considered unequal, and also the cells and machines could rotate. We believe that a cellular layout that assumes unequal dimensions of the cells and machines can be used for batch production. This kind of production has a wide variety of low to medium demand. Furthermore, a cellular layout can be applied in CMSs and also in noncontinuous industries that have a job shop layout. Our main contribution is considering the possibility of rotating the cells and machines inside the cells. For this purpose, a mixed nonlinear programming model was developed to solve the CLP with the minimum cost of intracellular and intercellular material flows. The proposed nonlinear model was first converted into a linear model, and then a problem was generated and solved with GAMS software to validate the resulting linear model. This model finds the best layout of cells within the workshop and the best layout of machines inside each cell. Then, because of the NP-hardness of the CLP and the fact that even exact methods cannot solve large-scale examples in an acceptable computational time, an imperialist competitive algorithm (ICA) was designed and used to solve the problem. To evaluate the efficiency of the proposed algorithm, its numerical results in small dimensions were compared with the results of GAMS software. In large dimensions, 30 random problems were created, and the results of ICA were compared with the results of the particle swarm optimization (PSO) algorithm and genetic algorithm (GA). Finally, the parameters of the three meta-heuristic algorithms were set by the Taguchi method. Numerical results indicated that ICA was superior to both the PSO algorithm and GA. It could also achieve efficient solutions in a shorter computational time. [ABSTRACT FROM AUTHOR]

Natural killer (NK) cells have recently shown renewed promise as therapeutic cells for use in treating hematologic cancer indications. Despite this promise, NK cell manufacturing workflows remain largely manual, open, and disconnected, and depend on feeders, as well as outdated unit operations or processes, often utilizing research-grade reagents. Successful scale-up of NK cells critically depends on the availability and performance of nutrient-rich expansion media and cryopreservation conditions that are conducive to high cell viability and recovery post-thaw. In this paper we used Cytiva hardware and media to expand the NK92 cell line in a model process that is suitable for GMP and clinical manufacturing of NK cells. We tested a range of cryopreservation factors including cooling rate, a range of DMSO-containing and DMSO-free cryoprotectants, ice nucleation, and cell density. Higher post-thaw recovery was seen in cryobags over cryovials cooled in identical conditions, and cooling rates of 1°C/min or 2°C/min optimal for cryopreservation in DMSO-containing and DMSO-free cryoprotectants respectively. Higher cell densities of 5x107 cells/ml gave higher post-thaw viability than those cryopreserved at either 1x106 or 5x106 cells/ml. This enabled us to automate, close and connect unit operations within the workflow while demonstrating superior expansion and cryopreservation of NK92 cells. Cellular outputs and performance were conducive to clinical dosing regimens, serving as a proof-of-concept for future clinical and commercial manufacturing. [ABSTRACT FROM AUTHOR]

Population-based metaheuristics can be seen as a set of agents that smartly explore the space of solutions of a given optimization problem. These agents are commonly governed by movement operators that decide how the exploration is driven. Although metaheuristics have successfully been used for more than 20 years, performing rapid and high-quality parameter control is still a main concern. For instance, deciding the proper population size yielding a good balance between quality of results and computing time is constantly a hard task, even more so in the presence of an unexplored optimization problem. In this paper, we propose a self-adaptive strategy based on the on-line population balance, which aims for improvements in the performance and search process on population-based algorithms. The design behind the proposed approach relies on three different components. Firstly, an optimization-based component which defines all metaheuristic tasks related to carry out the resolution of the optimization problems. Secondly, a learning-based component focused on transforming dynamic data into knowledge in order to influence the search in the solution space. Thirdly, a probabilistic-based selector component is designed to dynamically adjust the population. We illustrate an extensive experimental process on large instance sets from three well-known discrete optimization problems: Manufacturing Cell Design Problem, Set covering Problem, and Multidimensional Knapsack Problem. The proposed approach is able to compete against classic, autonomous, as well as IRace-tuned metaheuristics, yielding interesting results and potential future work regarding dynamically adjusting the number of solutions interacting on different times within the search process. [ABSTRACT FROM AUTHOR]

Cell formation (CF) and machine cell layout are two critical issues in the design of a cellular manufacturing system (CMS). The complexity of the problem has an exponential impact on the time required to compute a solution, making it an NP-hard (complex and non-deterministic polynomial-time hard) problem. Therefore, it has been widely solved using effective meta-heuristics. The paper introduces a novel meta-heuristic strategy that utilizes the Genetic Algorithm (GA) and the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) to identify the most favorable solution for both flexible CF and machine layout within each cell. GA is employed to identify machine cells and part families based on Grouping Efficiency (GE) as a fitness function. In contrast to previous research, which considered grouping efficiency with a weight factor (q = 0.5), this study utilizes various weight factor values (0.1, 0.3, 0.7, 0.5, and 0.9). The proposed solution suggests using the TOPSIS technique to determine the most suitable value for the weighting factor. This factor is critical in enabling CMS to design the necessary flexibility to control the cell size. The proposed approach aims to arrange machines to enhance GE, System Utilization (SU), and System Flexibility (SF) while minimizing the cost of material handling between machines as well as inter- and intracellular movements (TC). The results of the proposed approach presented here show either better or comparable performance to the benchmark instances collected from existing literature. [ABSTRACT FROM AUTHOR]

The paper exposes testing results corresponding to the process of manufacture cellular glass from consumed drink glass packaging, a vegetable expanding agent (beech leaves) used for the world premiere, and borax as a flux agent. The process of sintering/expanding the powder material mixture was performed by microwave irradiation at 825-834 °C, as a process improved by the authors of this paper and applied in previous experiments, unlike the conventional procedures used in processes of the same type. The nonconventional heating has allowed a higher energy efficiency of the process and reduced energy consumption (0.78 kWh/kg). The optimal version of the manufacturing process with 30 % beech leaves, 65 % consumed drink glass packaging, and 5 % borax led to obtaining a cellular glass with features adequate for its use as heat insulating material in civil engineering having density of 0.60 g/cm3, heat conductivity of 0.124 W/mK, compression resistance of 5.0 MPa, absorbing the water of 1.5 vol. %, and cells dimension between 0.7-1.0 mm. [ABSTRACT FROM AUTHOR]

This paper constructs an integrated virtual reconfiguration model that can simultaneously group workstations, schedule virtual cells, and select energy consumption levels. If managers prefer the physical proximity of machines in a certain virtual cell, the material handling cost in it can be reduced. However, the distances among machines in other virtual cells are probably large, which may cause greater material handling cost. If a virtual cell for a certain product type has priority to be created, the backorder cost of that type can be decreased or even avoided. Nevertheless, the creation of virtual cells for other product types may be delayed, perhaps leading to the higher backorder cost of other product types. In addition, managers can choose a high energy consumption level of a machine to expedite its production to reduce backorder cost at the expense of more energy consumption cost. To minimize the total operational cost, we develop a novel Discrete Imperialist Competitive Algorithm with a Priority rule-based heuristic (DICAP). It includes a colony movement strategy, a competition strategy, a collapse mechanism, a development strategy, and a sufficient convergence policy. Numerical experiments and t-test are conducted to validate that the proposed DICAP outperforms genetic algorithm and simulated annealing. Note to Practitioners—Virtual cellular manufacturing systems can create an expectation of improving machine utilization and productivity, reducing reconfiguration cost, and adaptation to product specification changes in reality. However, managers often feel difficult to make appropriate decisions for three interrelated issues, i.e., workstation grouping, virtual cell creation and release, and energy consumption options. Workstation grouping depends on the availability of machines and workers, and the availability may be related to the creation and release time of some virtual cells. Whether a virtual cell is created in time has an influence on the selection of energy consumption levels, because managers need to consider both backorder cost and energy consumption cost. In addition, there often exists a bottleneck workstation in each virtual cell. Therefore, it is essential and worthwhile to select appropriate energy consumption levels of machines to smooth the production efficiency of grouped workstations. To effectively address the issues, this paper presents an integrated virtual reconfiguration model. A novel Discrete Imperialist Competitive Algorithm with a Priority rule-based heuristic is developed to minimize the total operational cost. Numerical experiments and t-test results indicate that the proposed algorithm outperforms two commonly used ones, i.e., genetic algorithm and simulated annealing in solution quality. It is suitable for virtual reconfiguration problem with industrial size in practice. [ABSTRACT FROM AUTHOR]

Manufacturing industries are in a constant state of competition to attract customers in a variety of methods. Group Technology (GT) is a term used in the field of manufacturing for grouping similar elements based on their similarities in production and design. Cellular manufacturing (CM) is an application of Group Technology (GT) that has gained widespread traction in Small- and Medium-Sized Enterprises (SMEs) during the recent years in order to increase the production floor's efficiency and output. A Cell Formation consists of grouping identical machinery and assigning them on similar functions. There are three main decisions involved in designing the Cellular Manufacturing System (CMS): Group Scheduling (GS), Group Layout (GL), and Cell Formation (CF). In this study, the primary challenge associated with the CMS is not only the formation of cells but also the optimal placement of machinery within each cell. This paper's objectives are therefore twofold: the formation of cells and the optimal placement of machinery within cells. For the purpose of Cell Formation and the position of machinery within the cell, a Genetic Algorithm (GA) and Encoding Scheme are employed. In this study, a Genetic Algorithm is used to classify machines and parts, while MATLAB is used for the simulation and encoding scheme. To evaluate the developed objective function and GA, a layout problem of medium size is solved. Results indicate that the proposed strategy is effective for resolving CMS issues and increasing productivity by 8.85%. [ABSTRACT FROM AUTHOR]

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]

Cancer is a worldwide health problem. Nevertheless, new technologies in the immunotherapy field have emerged. Chimeric antigen receptor (CAR) technology is a novel biological form to treat cancer; CAR-T cell genetic engineering has positively revolutionized cancer immunotherapy. In this paper, we review the latest developments in CAR-T in cancer treatment. We present the structure of the different generations and variants of CAR-T cells including TRUCK (T cells redirected for universal cytokine killing. We explain the approaches of the CAR-T cells manufactured ex vivo and in vivo. Moreover, we describe the limitations and areas of opportunity for this immunotherapy and the current challenges of treating hematological and solid cancer using CAR-T technology as well as its constraints and engineering approaches. We summarize other immune cells that have been using CAR technology, such as natural killer (NK), macrophages (M), and dendritic cells (DC). We conclude that CAR-T cells have the potential to treat not only cancer but other chronic diseases. [ABSTRACT FROM AUTHOR]

This paper presents the concept and methodology for the designing of a "tree-shaped" production line. The concept is a result of the search for production unit organization that meets the Lean Production assumptions, i.e. focusing on lead time (throughput time) shortening with simultaneous ability of use in conditions of varied product range. The varied product range characterized by lower technological-organizational similarity when compared to "Ushaped" units typical for Lean Production. The paper presents an algorithm for the designing of a "tree-shaped" production line and examples of its application. The designed unit underwent evaluation according to the criteria preferred by Lean Manufacturing experts. The designed production unit achieved results confirming the effectiveness of the proposed concept for the analysed sets of input data on the product range and production capacities. [ABSTRACT FROM AUTHOR]

Modeling and simulation of manufacturing systems is part of the industry 4.0 concept. The paper presents how a model with timed Petri nets was developed for the evaluation of the performance of a robotic manufacturing cell for the execution of the Disc limiter product. The manufacturing process involves a first cutting operation, an assembly operation and a final assembly processing operation. The need to satisfy the client's requirements presupposed the design of a manufacturing structure composed of four CNC machining centers and two industrial robots. The hardware and software integration of the cell components required the realization of the model with timed Petri nets. Through simulation, the existence of collisions can be highlighted and the production achieved in a given time interval can be evaluated. [ABSTRACT FROM AUTHOR]

Abstract\nKEY POINTSHigh-purity quartz (HPQ) is the only naturally occurring and economically viable source for the production of silicon. Silicon is a critical mineral, and a key component in modern technologies such as semiconductors and photovoltaic cells. Critical minerals support the move towards a greater reliance on electrification, renewable energy sources and economic security. The global transition to net zero carbon emissions means there is a growing need for new discoveries of HPQ to supply the silicon production chain. HPQ deposits are identified in a multitude of geological settings, including pegmatites, hydrothermal veins, sedimentary accumulations and quartzite; however, deposits of sufficient volume and quality are rare. Quartz is abundant throughout Australia, but the exploration and discovery of HPQ occurrences are notably under-reported, making assessment of the HPQ potential in Australia extremely difficult. This paper presents a much-needed summary of the state of the HPQ industry, exploration and deposit styles in Australia.High-purity quartz (HPQ) is a key material for the manufacture of photovoltaic cells, semiconductors and other high-technology applications.HPQ can be recovered from a variety of different source rocks in a range of geological settings.Currently, the HPQ industry in Australia is under-utilised for high-technology applications, and historical exploration and mining records are under-reported and opaque.This review presents an outline of the characteristics, processing requirements and end uses of HPQ, and a summary of the operations, deposits, exploration targets and known occurrences of HPQ in Australia. [ABSTRACT FROM AUTHOR]

Cell formation is one of the oldest problems in cellular manufacturing systems (CMS) including assigning parts, machines and operators to cells. Cell manufacturing contains a number of cells where each cell is responsible for processing the family of similar parts. Another important aspect of cell formation is worker assignment to cells. Since operators work together in long periods, it is suggested to consider operators’ personal characteristics to increase their satisfaction and the productivity of system. This paper considers decision-making styles of operators (as an index of operator’s personal characteristics) and presents a new mathematical programming model for clustering parts, machines and workers simultaneously. The model includes two objectives; (1) minimization of intracellular movements and cell establishment costs, (2) minimization of decision-making style inconsistency among operators in each cell. The paper appliesε-constraint method for solving the problem and gathering non-dominated solutions such as Pareto optimal solutions. Furthermore, this paper uses common weighted multicriteria decision analysis (MCDA)-data envelopment analysis method to choose the best solution from the candidate Pareto optimal solutions that have been achieved by solving the mathematical model. A real case study is investigated to show the capability of the proposed model to design CMS in the assembly unit. The proposed design assists decision-makers to develop cellular systems with more operators’ satisfaction and productivity. [ABSTRACT FROM AUTHOR]

Graphs are powerful tools to model manufacturing systems and scheduling problems. The complexity of these systems and their scheduling problems has been substantially increased by the ongoing technological development. Thus, it is essential to generate sustainable graph-based modeling approaches to deal with these excessive complexities. Graphs employ nodes and edges to represent the relationships between jobs, machines, operations, etc. Despite the significant volume of publications applying graphs to shop scheduling problems, the literature lacks a comprehensive survey study. We proposed the first comprehensive review paper which (1) systematically studies the overview and the perspective of this field, (2) highlights the gaps and potential hotspots of the literature, and (3) suggests future research directions towards sustainable graphs modeling the new intelligent/complex systems. We carefully examined 143 peer-reviewed journal papers published from 2015 to 2020. About 70% of our dataset were published in top-ranked journals which confirms the validity of our data and can imply the importance of this field. After discussing our generic data collection methodology, we proposed categorizations over the properties of the scheduling problems and their solutions. Then, we discussed our novel categorization over the variety of graphs modeling scheduling problems. Finally, as the most important contribution, we generated a creative graph-based model from scratch to represent the gaps and hotspots of the literature accompanied with statistical analysis on our dataset. Our analysis showed a significant attention towards job shop systems (56%) and Un/Directed Graphs (52%) where edges can be either directed, or undirected, or both, Whereas 14% of our dataset applied only Undirected Graphs and 11% targeted hybrid systems, e.g., mixed shop, flexible, and cellular manufacturing systems, which shows potential future research directions. [ABSTRACT FROM AUTHOR]

The fourth industrial revolution requires higher capabilities of changeability and reconfigurability (C-R) of the future factories (FoF), as well as a higher focus on business models that are based on total-cost-of-ownership (TCO) paradigm. Up to date, there are little scientific contributions to deploy C-R into TCO models, as well as to systematic plan and design manufacturing resources such as to facilitate FoF ecosystem. In order to address this issue, this paper introduces research results that show how to deploy C-R, connectivity, smartness and TCO requirements into the technical solutions of manufacturing resources of FoF. Contributions emerging from this research include an index to measure C-R capability of manufacturing resources, a model to assess economic feasibility of a FoF over its lifecycle, as well as a methodology and related tools to design smart connected manufacturing resources with embedded features to facilitate changeability and reconfigurability in a FoF. Theoretical contributions are explained through a case study of a fast reconfigurable robotic manufacturing cell. Preliminary results demonstrate that it is possible to rapid design smart connected manufacturing resources and integrate them into FoF architectures that support convertibility, integrability, modifiability, adaptability, serviceability, scalability, integration of resources from various producers, service clustering and cloud-based services. [ABSTRACT FROM AUTHOR]

The paper reviews the design of Cellular Manufacturing System (CMS) and production requirements. The conventional methodologies for the cell formation problems consider the single period cell design, ignoring the introduction of new part mix and dynamic production in the planning horizon. However, in today's business world, the companies are under relentless pressure to reduce production costs while meeting the high quality expectations of customers. Contemporary researchers have been addressing the various modeling techniques used for the CMS design ranging from simple to sophisticated ones to deal with confinements on cell size, number of cells, part mix variation in the periodic production and setup time. A comprehensive review has been made in this paper to present the CMS design on distinguished production criteria. Finally, the review concludes by highlighting the cell formation problems and future directions for practitioners. [ABSTRACT FROM AUTHOR]

Intercell transfers in cellular manufacturing systems disrupt the philosophy of creating independent cells, but are essential for enterprises to reduce production costs. The problem of intercell scheduling with single processing machines and batch processing machines is considered in this paper, which involves an assignment subproblem, a sequencing subproblem, and a batch formation subproblem. An ant colony optimization (ACO)-based hyperheuristic (ABH) is developed in this paper, searching assignment rules for parts, sequencing rules for single processing machines, and batch formation rules for batch processing machines, simultaneously, and then using the obtained combinatorial rules to generate scheduling solutions. Computational results show that ABH is an effective and significantly efficient approach to provide near-optimum solutions even when CPLEX shows poor performance, and as compared to genetic algorithm that is widely used in hyperheuristics, ABH has better performance with respect to the problem addressed in this paper. [ABSTRACT FROM AUTHOR]

The paper is a partial synthesis of the work that a team within the Faculty of Managerial and Technological Engineering has in the field of modeling and simulation of systems with Petri nets. The paper deals with three issues. The first part is Modeling and simulation of manufacturing systems with timed Petri nets. The first part presents the way in which the performances of some manufacturing systems are evaluated using timed Petri nets. The first example is a manufacturing system specific to the textile industry. The system has three production lines. In this case, the modeling with timed Petri nets allows the analysis of the operation of the flow lines and highlights the bottleneck. Another modeled manufacturing system is specific to a series production, more precisely it refers to the manufacture of gas cookers within a company from Oradea. The model with Petri nets also describes the supply sequences of the stocks of parts and subassemblies. The next modeled system is an automated manufacturing system for the packaging of detergents. The complexity of the Detergent Packing System and the high level of automation, require the use of efficient modeling and simulation methods to verify the validity of the solutions adopted. In this chapter is present the model of a robotic welding cell. The second part of the paper, the transition from timed Petri nets to hierarchical colored Petri nets, shows how Petri nets were developed for a robotic cell for metallographic analysis. The third part is dedicated to modeling with colored Petri nets timed with complex colors of the flexible manufacturing cell A007 within the Department of Industrial Engineering. [ABSTRACT FROM AUTHOR]

The filling of the electrolyte and the subsequent wetting of the electrodes is a quality-critical and time-intensive process in manufacturing of lithium-ion batteries. The exact influencing factors are the subject of research through experiments and simulation tools. Previous studies have demonstrated that wetting occurs mainly in the transition between the materials but leads to gas entrapments. Therefore, this paper investigates the influence of the electrode surface structures, situated between anode and separator, on the wetting progress, through experimental capillary wetting and simulated with a lattice Boltzmann simulation. The results show that the simulations can identify the exact pore size distribution and determine the wetting rates of the entire materials. Furthermore, the experiments reveal a negative correlation between fast wetting and rougher surface properties. This enables a more precise determination of the wetting phenomena in lithium-ion cell manufacturing. [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]

Additive manufacturing technology which started as a prototyping application in the infant stage for new product design and development is gaining importance by transforming into a realistic part manufacturing innovative opportunity. Basically additive manufacturing for cellular lattice structure manufacturing is a latest addition for benefitting from strength to weight ratio for better functional performance of parts. In this paper various types of lattice structure, computer aided design for AM, different methods to create cellular structures, functionally graded cellular structures in AM, stress based structural optimization method for light weight designing, and manufacturing and testing of lattice structure were considered as basis for further research in this area. [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]

With increasing the energy costs and aiming for fossil-free Europe, cellular structures could provide a cost-effective tool for saving fuel consumption in aircraft. To achieve this goal, a cellular structure topology is a rapidly growing area of research facilitated by developments in additive layer manufacturing. These low-density structures are particularly promising for their aerospace applications. In this paper, four cellular structure topologies are developed to serve as a vibration damper in small electric aircraft motor, we have compared their performance with the original motor holder in the aircraft. This paper introduces the roadmap of scaffolding concept design and provides a novel concept in vibration damping. Based on the FEA simulation, aluminium 6061T spiderweb-inspired lattices (weight 0.3473 g and porosity 84%) have proven to have the lowest natural resonance and highest yield strength to weight ratio compared to other scaffolding concepts. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to document the development of cellular manufacturing at Scania-Vabis, thereby contributing to the history of an organizational idea. Design/methodology/approach: The authors draw on published sources and interviews to reconstruct the development of cellular manufacturing at Scania-Vabis and its traces. Findings: Cellular manufacturing was applied and further developed at Scania-Vabis in the 1940s and 1950s. Nevertheless, it seems to have fallen into oblivion. The key idea resurfaced in the 1970s. Practical implications: The authors argue that such "proven technology" should be considered a classical insight in organization design rather than old and thus outdated. Originality/value: The authors demonstrate that this form of flow-based organizing is much older than commonly assumed and point to barriers in accumulating knowledge on organizing. [ABSTRACT FROM AUTHOR]

In cellular manufacturing systems (CMSs), an operator plays an important role. Because operators work for long-time periods in a production area, an increase in job satisfaction and system productivity occurs if the consistency of operators’ personal characteristics are considered in the design of CMSs. In a CMS, a cell formation problem (CFP) focuses on grouping and allocating machines, part families and operators to manufacturing cells. This paper considers a decision-making style (DMS) as an operator’s personal characteristic index in a CFP for designing a psychologically consistent CMS. DMS influences not only the interaction between two operators, but also the work that operator does on a machine. Hence, this paper develops a novel multi-objective mathematical model for the CFP considering consistency between each two operators in each cell and consistency between operator and his/her assigned machine(s). Because of possibility of a change in the primary DMS of a person to the backup one, this paper tackles this issue by applying a probabilistic procedure. Two hybrid meta-heuristic algorithms are developed for the large-sized test problems. In addition, the PROMETHEE-II method is applied to select the best Pareto solution. Finally, a real case study is presented to show the applicability of the developed approach. [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 layout of fixed-position assembly islands (FPAI) is widely used for producing fragile or bulky products. With the increasing customised demand and unique operation patterns, manufacturing practitioners are facing challenges on flexible and efficient production arrangement to meet customer demand, which lead to inappropriate assembly islands configuration, frequent setups and long waiting times in FPAI. Industry 4.0 comes with the promise of improved flexibility and efficiency in manufacturing. In the context of Industry 4.0, this paper proposes a 5-layer APICS (assembly layer, perception layer, interaction layer, cognition layer, and service layer) roadmap for transformation and implementation of Assembly 4.0. Following the 5-layer APICS roadmap, a Graduation Intelligent Manufacturing System (GiMS) is presented as the pioneering implementation in FPAI. A graduation-inspired assembly system is designed for FPAI at assembly layer. Internet of Things (IoT) and industrial wearable technologies are deployed for perception, connection, and collaboration among various manufacturing resources at perception and interaction layer. A self-configuration model is proposed at cognition layer for autonomously configuring optimal assembly islands and corresponding production activities to meet customer demand. Cloud-based services are developed for managers and onsite operators to facilitate their decision-making and daily operations at service layer. Finally, a demonstrative case is conducted to verify the feasibility of the proposed methods. [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]

Failure of resource in automated manufacturing systems could cause a complete system shutdown. This paper addresses the issue of unreliable resource failure in manufacturing cells through the use of load-sharing redundant resources (LSRRs). The aim is to use more than one type of a failure-prone resource to share tasks between a failure-prone resource, called a target resource, and reliable ones called load-sharing redundant resources (LSRRs). Both an unreliable resource and its LSRR perform the same tasks, and there is normally a system that assigns tasks to them. If the target resource fails, all the tasks will be performed by the LSRRs. After the faulty target resource is fixed and restored, its assigned tasks are automatically returned to it. This way the system can continue to produce or process parts. Thus, a total system shutdown due to unreliable resource failures is eliminated. The proposed method is tested using real examples. The results, compared with those obtained by the studies in the literature, show that the proposed method has an outstanding performance and outperforms some of the existing studies. [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]

This paper presents an approach for the design and derivation for establishing a digital product twin for battery cells. A digital product twin is a virtual replica of a physical battery cell and can be used to predict and optimize quality properties and performance in real-time. The study focuses on pouch cell manufacturing and aims to map the large amount and variety of process information down to purchased parts and interim products. The approach for this study was to collect and analyze data from the physical production process and use this information to structure a digital battery product twin based on its product architecture. The main findings of this study indicate that a digital product twin can be effectively structured and implemented in a digital interface based on its product architecture in combination with data from the physical production process. The results of this study show the potential of digital product twins, in which statements about material, design, and behavior can be made using real information from production. Further research will focus on the practical application and implementation of digital product twins in a battery cell pilot production. [ABSTRACT FROM AUTHOR]