Machine Learning Driven Design Of Experiments For Predictive Models In Production Systems

Machine learning (ML) describes the ability of algorithms to structure and interpret data independently or to learn correlations. The use of ML is steadily increasing in companies of all sizes. However, insufficient market readiness of many ML solutions inhibits their application, especially in production systems. Predictive models apply ML to understand the complex behavior of a system through regression from operational data. This enables determining the relationship between factors and target variables. Accurate predictions of these models for production systems are essential for their application, as even minor variations can significantly affect the process. This accuracy depends on the available data to train the ML model. Production data usually shows a high epistemic uncertainty, leading to inaccurate predictions unfit for real-world applications. This paper presents ML-driven, data-centric Design of Experiments (DoE) to create a process-specific dataset with low epistemic uncertainty. This leads to improved accuracy of the predictive models, ultimately making them feasible for production systems. Our approach focuses on determining epistemic uncertainty in historical data of a production system to find data points of high value to the ML model in the factor space. To identify an efficient set of experiments, we cluster these data points weighted by feature importance. We evaluate the model by running these experiments and using the collected data for further training of a prediction model. Our approach achieves a significantly higher increase in accuracy compared to continuing the training of the prediction model with the same amount of regular operating data

Enabling Quality-oriented Process Development for sulfidic All-Solid-State Battery Cathodes

After major advances in material research throughout recent years, the industrialization of all-solid-state batteries now depends on the development of cost-effective production technology for novel materials and components. To enable a fast production scale-up and complex process interdependency handling, production engineering needs a quantitative evaluation and comparison approach for manufacturing strategies and process parameter settings. To address this challenge, we derive microstructural quality criteria from specifications at the product-level such as driving range and charging speed of battery electric vehicles. These range from porosity and agglomerate density on a macroscopic level to microscopic properties such as pore size distribution and particle contacts. By listing comprehensive characterization methods, the work enables engineers to efficiently evaluate these criteria. Experimentally applying the proposed approach, the influence of different mixing process parameters is analyzed. Thereby, sulfidic composite cathodes manufactured in a scalable procedure are used as samples

Material Flow Simulation in Lithium-Ion Battery Cell Manufacturing as a Planning Tool for Cost and Energy Optimization

Lithium-ion batteries are seen as a key technology for powering electric vehicles and energy storage. Still, their high cost and energy-intensive manufacturing process remain a significant barrier to wider adoption. Due to the high moisture sensitivity of certain processed materials, the operation of dry rooms is required, constituting a critical contributor to cost and energy consumption in lithium-ion battery production. As the operating costs for these dry rooms strongly depend on the volume and adjusted humidity of the air, it is vital to choose an appropriate operation strategy already in the planning and designing phase of the factories. In this regard, simulation tools can effectively support the planning process by providing predictive information on the production system. The simulation model presented in this paper offers an approach to optimize the material and energy consumption associated with the production of lithium-ion batteries while also considering current material-related production challenges regarding moisture. By calculating a time-resolved material flow, the model enables to identify individual process times and storage durations depending on the chosen production layout. This allows for a material-specific dimensioning of the buffers and supports the dry room design. Hence, the data generated by the model can serve as a basis for planning more cost- and energy-efficient production environments

Efficient Intralogistics Planning Based on an Innovative Intralogistics Tool using the Example of a Flexible Battery Cell Factory

In the course of increasingly volatile markets, globalization as well as shorter product life cycles, factories and thus also the logistics system as a central component of a factory have to be designed in a more flexible way. Battery cell production faces a special challenge in this aspect. Due to the trend towards a sustainable and environmentally friendly energy supply and mobility, the demand is expected to increase significantly. New battery cell factories have to manage rising product volumes and simultaneously react versatile regarding new research findings. Thus, the market for battery cells, the product itself, and the corresponding manufacturing processes are constantly changing. New materials, manufacturing methods, variations of cell formats as well as the possibility of scalability and the associated changes in the requirements for the factory must be taken into account as early as possible in the planning stage. The logistics system as one of the core elements of a factory is always affected by changes in the product, manufacturing processes or input materials. If, for instance, other materials are used, the storage and transport of these goods with different dimensions, weight or even environmental requirements must still be guaranteed. In order to consider the required flexibility already in the planning process, simulation can provide a decisive benefit. It enables the planner to analyse the production and iteratively adapt logistics planning. Since there are many possibilities and combinations, especially in the design of warehouse and transport systems, a reduction of these should take place at an early stage. However, the preselection of suitable logistics systems that provide the necessary flexibility is currently often based on empirical knowledge and extensive market research. Therefore, this paper presents an efficient, holistic approach to logistics planning and an intralogistics tool in detail, which is based on established data. As a result, an optimal logistics system can be defined through an iterative optimization of the flexibility corridor, taking into account the factory goals

Framework For The Rapid Development And Deployment Of Customized Industrial Robotic Applications

Automation and industrial robots enable today's enterprises to increase productivity. Due to current challenges, such as a shortage of skilled workers, the trend is toward using industrial robots more and more in high-mix low-volume production. For this, enterprises must be able to develop and deploy robotic applications for various products, variants, and tasks easily and quickly. In previous works, we demonstrated the increased flexibility and efficiency of robot programming via a skills-based software framework. In this paper, we expand this framework by considering the overall development and deployment procedure of robotic applications. In addition to modular programming, we address the development of the necessary hardware for the robotic application. Here, we focus on the design of the gripper system. As an exemplary use case we present the handling and testing of variant-rich electronic products. Finally, based on the introduced framework, we show our first implementation results to realize this use case

Adaption of the Level of Development to the Factory Layout Planning and Introduction of a Quality Assurance Process

Current developments and trends are causing an increasingly turbulent environment for manufacturing companies. In order to respond to these dynamic market conditions, products and thus also production systems have to be adapted more frequently and much faster. However, time and cost targets are often missed by classic factory planning approaches due to poor communication, inadequate tools, and lack of interfaces. Therefore, new ways have to be found in factory planning to overcome these problems. Building Information Modeling, which is already used in the construction industry, provides a promising method for the collaboration of stakeholders based on digital models. This would allow communication to be structured, new tools to be used, and interfaces to be stabilized to improve the target achievement in factory planning projects. However, which information should be provided in which level of detail in which phase of a factory planning project and how the quality of this information can be ensured has not yet been answered. A possible solution to these questions is addressed in this article. First, the concept of the so-called Level of Development, i.e. the geometric and non-geometric definition of the model contents, is transferred to factory layout planning. Then, based on two use cases, the process of quality assurance is defined

Towards Enabling Human-Robot Collaboration in Industry: Identification of Current Implementation Barriers

Human-robot collaboration (HRC) is designed to combine the repeatability and precision of robots with the flexibility and adaptability of human workers. However, despite being researched for several years, HRC applications are still not broadly adopted in the industry. This study aims to identify current barriers to HRC adoption in the industry from a practical perspective. Therefore, a qualitative explorative approach based on semi-structured interviews with knowledgeable industry experts was chosen. The study was conducted in cooperation between IMT Nord Europe and the Technical University of Munich in France and Germany. Thereby, several experts from various backgrounds in areas such as robot manufacturing, system integration, and robot application in manufacturing were interviewed. These interviews are inductively analysed, and the findings are compared to the state-of-the-art in scientific HRC research. The study offers insights into the practical barriers to HRC adoption resulting from the technical, economic, social, and normative dimensions as well as the trade-offs between them. Based on these insights, opportunities for future research are identified

Machine learning in lithium‐ion battery cell production : a comprehensive mapping study

With the global quest for improved sustainability, partially realized through the electrification of the transport and energy sectors, battery cell production has gained ever-increasing attention. An in-depth understanding of battery production processes and their interdependence is crucial for accelerating the commercialization of material developments, for example, at the volume predicted to underpin future electric vehicle production. Over the last five years, machine learning approaches have shown significant promise in understanding and optimizing the battery production processes. Based on a systematic mapping study, this comprehensive review details the state-of-the-art applications of machine learning within the domain of lithium-ion battery cell production and highlights the fundamental aspects, such as product and process parameters and adopted algorithms. The compiled findings derived from multi-perspective comparisons demonstrate the current capabilities and reveal future research opportunities in this field to further accelerate sustainable battery production