The production environment accounts for a significant portion of the battery manufacturing costs. Most manufacturing processes in battery production require a dry environment. Otherwise, reactions between moisture and battery materials, especially active materials and electrolytes, can adversely affect battery life. With fully automated, digitized production processes, more inline measurement tools are needed to ensure quality and efficiency. Residual moisture measurement during the battery production process is, therefore, crucial. In this work, a contactless, non-destructive method for determining residual moisture was investigated. Numerous experiments were conducted to examine potential influencing factors of electrode moisture. Near-infrared (NIR) spectroscopy was used to detect the residual moisture of electrodes. Artificial intelligence (AI) models were trained to predict residual moisture using a non-destructive, inline-capable hyperspectral imaging (HSI) camera. The true labels of the residual moisture content were determined based on Karl Fischer titration (KFT) results. For data preprocessing, multivariate image analysis techniques were required to handle the large data sets generated by the HSI camera. In the test data set, 84 % of the residual moisture predictions fell within the ± 1 σ uncertainty band. The robustness of the predictive AI model was further tested by moving samples to align with the battery manufacturing environment.

Electromobility as a key factor of the energy transition places high demands on its core technology: the lithium-ion cell. In particular, fast charging is one of the critical prerequisites and is associated with major challenges, as improving the fast-charging capability often comes at the cost of reduced energy density. Electrode structuring can enhance the fast-charging capability of lithium-ion cells without compromising energy density. Simple and meaningful characterization methods are essential for rapid development of such processes. However, conventional charge rate tests can lead to misleading results when testing transport-limited electrodes. This study demonstrates that, for transport-limited electrodes, conventional rate tests may yield similar results despite significantly different transport parameters, such as tortuosity. Conventional graphite anodes are compared with electrodes processed via structure calendering-a novel method that combines structuring and calendering within a single roller process step. Using rate tests and half-cell measurements, it is shown how lithium plating contributes to this effect and the underlying electrochemical phenomena are explained. Furthermore, an adapted charge rate test procedure is proposed, which effectively demonstrates the advantage of structured electrodes in the charging direction. The novel method can be implemented using standard cell testers enabling widespread application.

Process optimization (PO) in manufacturing is guided by ISO 9000 quality management principles and implemented through methodologies such as Six Sigma's DMAIC cycle. Yet the extent to which data science frameworks (DSFs) align with QM standards, and how AI agents can extend data-driven PO, remains insufficiently understood. This systematic mapping study (SMS) examines 96 publications on: (i) how DSFs reflect ISO 9000 principles, (ii) how CRISP-DM compares with DMAIC, (iii) how data-driven methods support PO tasks, and (iv) the functional roles of AI agents in supporting PO. The SMS reveals that none of the evaluated DSFs fully satisfy ISO 9000 requirements, with customer focus and relationship management most consistently unaddressed. A phase-by-phase comparison reveals that CRISP-DM and DMAIC are complementary but structurally distinct: CRISP-DM is data-centric and exploratory, while DMAIC is process-centric and controloriented. Across publications, data-driven methods yield cycle-time reductions of up to 5.5%, makespan reductions of 8-11%, predictive accuracies above 90% for quality tasks, and fault detection improvements of up to 30% over baseline methods. Five recurring challenges constrain industrial deployment, including data quality, process complexity, and organizational barriers. AI agents operate across three functional roles: decision-making, knowledge-reasoning, and orchestration, extending DSFs toward autonomous analytics. Building on these findings, this paper develops an agentic process optimization framework (APOF) comprising a business governance layer, an AI agent system, a data system, and a tool system. Unlike prior frameworks, APOF connects both process- and data-centric paradigms to support human experts while enabling continuous data integration, automated model development, and operationalization, with auditable decision trails.

Manufacturing companies face increasing volatility, shorter product life cycles, and rising cost pressure. These challenges demand faster and more adaptive production system planning. Artificial Intelligence (AI) holds strong potential to enhance this process but remains rarely applied in manufacturing, particularly in tasks that require spatial understanding. VisionLanguage Models (VLMs), which combine visual perception with language-based reasoning, mark a promising step toward AI systems capable of interpreting complex production scenes. This paper introduces a framework for evaluating VLMs on spatial tasks in production system planning. The framework connects large-scale 3D production system scenes with multimodal reasoning components through a modular architecture. Controlled rendering, structured reasoning, and traceable evidence handling ensure reproducible and transparent evaluation. Validation with two representative models, one state-of-the-art and one openaccess, was conducted within an automotive planning scenario at the BMW Group. A corresponding benchmarking dataset is made available to support further research. The results demonstrate the framework's value as a practical foundation for benchmarking VLMs and for tracking their progress toward industrial readiness.

Nowadays, wiring in control cabinets is a manual, time-consuming, and error-prone assembly task, therefore automation offers great potential for improvement. However, the wiring is a highly complex assembly task involving hundreds of deformable linear objects (DLOs). Currently, existing fully automated approaches to solving this task commonly involve complex system setups and still have technical shortcomings in terms of handling resource capabilities and in-parallel execution. Hence, human-robot collaboration (HRC) can be a cost-effective approach if both the sub-tasks for the human and the robot are optimally allocated. In this paper, a task allocator is presented towards optimizing one human-robot collaborative system setup within control cabinet assembly. Based on a series of experiments, the potential for improvement in the cabling of control cabinets using an HRC paradigm is investigated. To this end, the solution is being validated against industry-related key performance indicators (KPIs).

Skill-based programming of robots provides a flexible approach for automation. Existing solutions neglect the optimization of motion sequences, leading to inefficiencies in execution. This work introduces a planning method that enhances skill-based robot programming by integrating motion sequence optimization. This optimization leads to a new MoveContinuousSkill. The software for executing the MoveContinuousSkill is implemented on a Programmable Logic Controller and applied across multiple robotic systems. Experimental results demonstrate a significant improvement in execution time through optimized motion sequence.

Cognitive assistance systems (CAS) used in manual assembly aim to improve worker’s productivity and accuracy by providing guidance and process control. Various CAS in manual assembly can be used simultaneously through integration into the existing IT-infrastructure. The integration of CAS is often enabled by custom software solutions that enable seamless synchronization between CAS. To increase productivity and accuracy, continuous reconfiguration of CAS is required to align it with specific tasks and individual preferences. This can be achieved by directly obtaining workers’ opinions and thus allowing workers to decide what CAS are necessary to optimize individual performance and efficiency. This paper presents a dialog system based on Natural Language Processing (NLP) that leverages voice and text inputs to improve the reconfiguration of CAS.

The global shift towards electromobility drives the demand for high-energy-density lithium-ion batteries. Gaining an in-depth knowledge of battery production steps is crucial to meet this demand. Electrodes, a key component in lithium-ion batteries, consist of a multi-material system coated on a metallic current collector foil. Calendering affects mechanical and electrochemical properties by compacting the electrodes and defining the volumetric energy density. The strive for higher energy densities and the associated high compaction rates during calendering lead to an increase in electrode deformations. The coated particles are pressed into the current collector foil during compaction, influencing the electrode's electrical and mechanical properties at the interface. A holistic understanding of particle behavior during compaction is essential for producing high-quality electrodes. In this work, an automated, model-based approach is adapted for the process analysis to determine the calendering-induced particle indentations and the resulting contact area for lithium-ion battery cathodes. The methodology presented in this study enables the quantification of the contact area and the associated calendering-induced microstructural deformations of the current collector foil. A strong correlation was identified between the contact area and the characteristic values for the electrical resistance and adhesion strength at the interface between the coating and the current collector foil. The particle indentations are dependent on the mechanical specification of the current collector foil used, as a softer aluminum foil leads to a larger contact area.

High energy densities are vital to satisfy the increasing demand for battery storage systems for electric vehicles. One innovative battery type of the next generation is the solid-state battery, which is characterized by the high expected energy density. The polymer-based solid-state battery is notable for its high machinability in production and, therefore, offers great potential for industrial scale. One component of the polymer-based solid-state battery is the composite cathode, which faces particular challenges in the individual production processes. The calendering process is essential, as it can increase the ionic conductivity through a reduction of the composite cathode porosity. For this reason, the calendering process for polymer-based composite cathodes with different compositions of active material and solid electrolyte has been analyzed in depth in this work. This enabled extensive analysis of the calendering process with different material compositions of polymer-based composite cathodes to provide a profound understanding of the causal-effect relationships.

Lack of digital information continuity hinders factory planning efficiency, and current methods fall short in industry practice. This work validates the industrial applicability of a VDI-5200-based approach in a rubber industry greenfield project. The study assesses the direct information transfer shares, the out-of-the-box applicability of modules, and the automation benefits. Results show feasibility under real conditions, with future plans to extend continuity into construction and operation phases.

The demand for lithium-ion batteries continues to rise as industries pursue electrification and emission-free production. However, battery manufacturing remains a complex and resource-intensive process involving an iterative optimization of both processes and products. Due to material costs accounting for a large proportion of the total cost of battery cell production, scrap generated during process ramp-up represents a key economic challenge. Calendering, a crucial step during electrode manufacturing, significantly impacts the mechanical and electrochemical characteristics of lithium-ion battery cells. The configuration of the calendering process remains predominantly guided by empirical tuning and trial-and-error approaches, resulting in inefficiencies during ramp-up. This highlights the need for efficient parameterization and process control strategies in electrode production, enabling faster process stabilization and minimizing scrap during the initial production phases. To address these challenges, this study proposes a material-guided process control approach for calendering. Using an in-line thickness measurement and a stepwise compaction strategy, a control model and an automated, material-dependent parameterization routine are derived. To precisely control the thickness of the electrode, the presented approach utilizes a predictive feedforward control to adjust the roll gap, complemented by a closed-loop correction during calendering. The proposed approach is demonstrated on graphite anodes at various web speeds and transferred to cathode calendering. The findings show that the developed methodology allows for a streamlined ramp-up process for novel electrode formulations, thereby reducing process setup time, and material scrap. This contributes to the enhancement of cost efficiency, overall productivity, and sustainability in battery manufacturing.

Calendering is a critical process step in the production of lithium-ion battery electrodes, significantly influencing both the mechanical integrity and the electrochemical performance. While high line loads reduce the electrode porosity and increase the volumetric energy density, they also induce substantial mechanical stress on cathode particles. This can result in particle indentations in the current collector foil that improve adhesion and conductivity, but may also lead to particle fracture, negatively impacting cell performance. In this study, a novel experimental methodology is presented for investigating the indentation and fracture behavior of lithium nickel manganese cobalt oxide (NMC622) secondary particles using a single-particle layer approach. A preparation technique was developed to fabricate a defined single-particle layer, enabling controlled nanoindentation and fracture testing. Force-displacement curves obtained from nanoindentation provide quantitative information on the mechanical response of individual particle beds, including the load required for fracture and the extent of particle indentation into the metallic current collector. The developed method allows for a correlation between local mechanical stress and structural features of individual particle beds. The insights gained contribute to a better understanding of particle fracture mechanisms and interfacial indentation behavior during electrode calendering.

Solid-state batteries (SSBs) are regarded as a successor to current lithium-ion battery technology due to potential gains in energy density and safety. However, most SSB designs require external stack pressure to maintain contact at the solid-to-solid interfaces. While insufficient pressure increases interfacial resistance and diminishes usable capacity, excessive pressure necessitates heavy, costly mechanical constraints that compromise system-level energy density. This work presents a method leveraging a universal testing machine coupled with a potentiostat in a primary-secondary control architecture to systematically explore the pressure-performance relationship in pouch-type sulfide-based SSBs. Pressure sweeps reveal two distinct operating regimes: an initial exponential improvement in cell resistance and discharge capacity at low pressures, followed by a linear saturation phase with increasing pressure. Based on these findings, a quantitative methodology is established to define an optimal operating pressure $(p_{99})$ that balances electrochemical performance with mechanical load. The results provide insight into the trade-off between the cell performance and the operating pressure. This approach facilitates standardized pressure optimization, thereby supporting the transition of SSB technology from laboratory-scale research to industrial application.

The battery cell production, a cornerstone of the net-zero vision, is a multifaceted process chain involving diverse processes, spanning from batch to continuous to single-unit steps. The quality of the battery cell as the final product is affected by various product and process parameters along this process chain. In the era of Industry 4.0, data-driven approaches have emerged as a promising solution to navigate these complexities and derive effective quality management practices. A key prerequisite for the successful implementation is the availability of accurate data. A tracking and tracing system in battery cell production provides the foundation to acquire such data. It supports the development of a digital twin of the product, enabling real-time monitoring of key performance indicators, in-line quality control, resource optimization, and compliance fulfillment, among others. This article presents an implementation methodology and discusses the key aspects to consider for upscaling such a system focusing on data management, including relevant parameters, data acquisition, and storage, as well as data structuring and mapping. It highlights the advantages of using ontology-based data descriptions, enabling semantically mapped production environments. Lastly, this article explores potential use cases facilitated by a traceability system, emphasizing its potential to realize intelligent, data-driven production.

Industrializing solid-state batteries is crucial for advancing energy storage technologies, as solid-state batteries offer a promising alternative to conventional lithium-ion batteries with increased energy densities and improved safety. Reducing the porosity and enhancing the particle-to-particle contact between particles of the solid-electrolyte separator has been shown to improve the performance of solid-state batteries. This study investigates calendering as a scalable process to achieve a homogeneous densification of solid-electrolyte separators. The influence of key process parameters on the solid-electrolyte separator’s properties is examined using a pilot-scale calender for the processing of both sheet-based and coil-based materials. Interactions between the parameters and the resulting microstructural, mechanical, and electrochemical properties of the solid-electrolyte separators are analyzed. The findings of this work demonstrate that calendering significantly reduces the porosity of solid-electrolyte separators, creating mechanically robust, flexible free-standing separators. This enables roll-to-roll processing and thus provides a scalable alternative to the predominantly used uniaxial pressing technique for producing solid-electrolyte separators.

Solvent-reduced, water-based electrode production offers great opportunities in reducing the energy demand and enables a sustainable electrode production process. This study explores the semidry, water-based production of NMC622 cathodes using an innovative production route. The high solid content of the suspension required an extrusion process for the production of granules, further used in the film formation process. Various screw configurations and screw speeds were examined to analyze the extrusion parameters. The resulting electrodes were produced with a two-roller calender, and resistance measurements, along with scanning electron microscope analysis, revealed that a feeding screw configuration produced the best results with the lowest electrical resistance and no particle breakage. In the second part of the study the film formation process was investigated, in which the areal loading, the roller speed, and the porosity were varied. Electrical resistance measurements showed that electrodes produced at lower roller speed exhibited increased interface resistance and cracked NMC particles, leading to decreased cell performance in full cells. Cell testing revealed that electrodes manufactured at higher roller speed had lower first cycle coulombic efficiency, indicating increased moisture degradation. The best-performing electrode had the lowest areal loading and demonstrated excellent capacity retention of approximately 93% after 330 cycles.

Water inside a Li-ion battery (LiB) can be detrimental to its performance and even degrade cathode active materials (CAM) in combination with CO2 during production. This degradation may originate from sorption of water and CO2 in CAM. Moisture management aims to control this sorption process, minimize degradation, and reduce water carried into the cell during production. Controlling these sorption processes requires understanding the kinetics that restrict the mass uptake of water and CO2 in CAM. This manuscript provides time-resolved sorption data for Li[Ni0.6Co0.2Mn0.2]O-2 (NMC-622) and Li[Ni0.8Co0.1Mn0.1]O-2 (NMC-811) cathodes as electrodes and NMC-622 CAM as powder in various humid atmospheres. The exposed material was processed to full coin cells and electrochemically tested. The sorption behavior of cathodes in air at 72%-RH is restricted by kinetics depending on material chemistry. Experiments showed the difference in sorption behavior of NMC-622 in humid atmospheres with and without CO2. The exposure of CAM to humid atmospheres with various CO2 concentrations showed that cell performance decreases with increasing CO2 concentration in the atmosphere - linking a defined mass uptake of NMC-622 from water and CO2 to a capacity loss. The overall mass uptake detected at industrially relevant conditions in NMC-622 in four days is similar to 250 wt-ppm, which has little but noticeable effect on cell performance.

The increasing variety of products has resulted in a rise in production errors, primarily due to more complexity in manufacturing processes. This paper proposes a data-driven inline problem-solving approach to mitigate the response times associated with these errors. Problem-solving is initiated by detecting anomalies within processes by an autoencoder model. Upon identifying these anomalies, the proposed approach employs causal inference using a Failure Mode and Effects Analysis (FMEA)-based Bayesian Network (BN) to determine potential root causes. The inferred causes, along with the user's problem description, are processed within a hybrid Retrieval-Augmented Generation (RAG) framework. The RAG produces two sets of retrievals: one by querying a Knowledge Graph (KG) containing historic eight discipline-based (8D) problem-solving data to extract failure information and relationships; the other through keyword similarity and vector search techniques. The combined retrievals, along with the results from the BN, are then input for a relatively small-scale Large Language Model (LLM) from Mistral. The findings indicate that this approach achieves accurate information retrieval and provides reliable outputs, even when problem descriptions are vague.

The transition to renewable energy sources necessitates the development of environmentally friendly and cost-efficient lithium-ion battery production methods. Traditional cathode production is energy-demanding, primarily due to solvent recovery and drying. This study investigates the water-based semidry production processes for NMC622 cathodes, aiming to reduce the energy-intensive drying phase and eliminate toxic solvents like N-methyl-pyrrolidone (NMP). By substituting NMP with water and employing a semidry approach, the solvent content during production can be significantly reduced. This research compares semidry-coated electrodes with NMP-based and water-based wet-coated electrodes, focusing on the electrode properties and the electrochemical characteristics in full cells. Utilizing an extrusion and calendering method, the semidry process demonstrated a five-fold reduction in solvent use compared to the NMP-based method. However, the semidry-produced electrodes exhibited higher resistance, probably due to the fine distribution of binder and carbon black, which lacked long electron transport pathways compared to the wet-coated electrodes. The water-based wet-coated electrode, with lower initial coulombic efficiency, displayed better performance at higher discharge rates and lower capacity loss over cyclization than the NMP-based reference. This study highlights the potential of water-based semidry production in creating more sustainable and efficient lithium-ion batteries.

Although studies have demonstrated the potential of holistic digital factory models, their application in industry remains limited. There is a research gap as to why this is the case. In particular, the starting point of the factory operators and their specific requirements for such models are still unclear. This paper presents a mixed-methods study that addresses the existing research gap regarding the implementation of holistic digital factory models. The study investigates the evolving understanding of factory planning, emphasizing the transition from one-time projects to continuous tasks. The key findings reveal a significant need for cross-life-cycle information continuity, highlighting the importance of collaboration and data integration among stakeholders. The research identifies obstacles to achieving holistic digital factory planning, including knowledge management and data availability challenges. Furthermore, the applicability of existing technologies for holistic digital factory models, such as Building Information Modeling and Digital Twins, is examined, demonstrating their relevance in factory planning. Additionally, it is shown that while there is an apparent demand for standardized methods and tools, many existing methodologies are underutilized. The paper concludes with recommendations to further investigate the contrasts between literature and industry practices, as well as the implementation of shared data environments to enhance the efficiency of factory planning processes.