Michael F. Zaeh’s research while affiliated with Technical University of Munich and other places

The residual stress state in the sub-surface of machined components can significantly impact the properties and performance of parts, such as tribological properties, fatigue life, and dimensional distortion. The evolution of residual stresses depends on the selected process parameters and tool geometry, which affect the thermo-mechanical load imposed on the workpiece. The cutting-edge radius is among the most important factors influencing the resulting residual stresses. In this paper, a semi-analytical model for the determination of residual stresses after peripheral milling of Ti–6Al–4V is presented. It considers the physical principles of orthogonal machining, including the complex geometric and kinematic relationships during milling, to enable the determination of the residual stress variation after three-dimensional milling. Model input parameters, such as the equivalent Hertzian width of contact, the contact pressure, and the friction coefficients, were determined using a dedicated in-situ experimental setup. In addition, digital image correlation (DIC) was used to characterize the displacement fields in the sub-surface during machining. After the peripheral milling of Ti–6Al–4V, the calculated residual stresses were compared with experimentally measured values for various process parameters and cutting-edge radii. The residual stress model reproduced the 3D residual stress depth profiles with acceptable accuracy. The maximum Root Mean Squared Error (RMSE) was 29.87 MPa .

In today's manufacturing industry, companies operate in a complex and rapidly changing environment. This environment results in a high number and variety of Manufacturing Changes (MCs). To ensure long-term competitiveness, companies must respond quickly and effectively to various types of MCs. Although most companies have a Manufacturing Change Management (MCM) with an established process for managing MCs, the process often lacks the flexibility to handle the wide variety of MCs efficiently. Furthermore, MCM processes have a deficit in digital and methodological support, reducing the process's effectiveness. This contribution describes a requirements engineering (RE) approach applied to elicit the requirements for a flexible, digital, and methodological MCM. For this, a comprehensive literature review and semi-structured expert interviews were conducted. A total of 18 experts from eight different industries participated in the interviews. After introducing the necessary theoretical foundations and the current state of research in the MCM literature, the RE process is described in detail. The requirements are derived separately from the literature and the expert interviews and are then consolidated. The result is a catalog of requirements for a methodology for a flexible, digital, and methodological MCM. The paper concludes with an outlook on future research perspectives based on the identified requirements.

Manufacturing companies are challenged by various influencing factors in the volatile environment, which requires frequent adjustments to their factories, known as Manufacturing Changes (MCs). The wide variety of changes and the company-individual implementation of Manufacturing Change Management (MCM) processes in practice represent a significant challenge for research, as general approaches are often too rigid and cannot be adequately integrated into the individual structures of companies. Existing MCM processes in the industry often lack flexibility, methodological, and digital support. This contribution proposes a change-specific and company-individual approach to address these challenges. The four-step methodology includes steps for describing MCM processes, creating a systematic change description model, identifying and structuring methods and digital tools, and correlating attributes through a Delphi study. An overall application methodology supports companies in using the methodology and serves as a basis for the final validation.

Manufacturing companies face a volatile environment with multiple sustainability challenges threatening their competitiveness. To meet these challenges, they need to adapt quickly to the changing demands and environment to improve their sustainability in production. Therefore, this contribution presents a holistic and multidimensional model for sustainable production systems based on three levels: elements, overarching elements, and dimensions. First, a systematic literature review (SLR) was conducted to capture the state of research. Second, two studies were conducted to identify and cluster the relevant sustainability elements. One of the studies is based on the SLR findings, which were verified by an expert survey. The other is based on a document analysis of sustainability reports. Finally, both studies are compared, and the results are presented. In total, 40 elements, such as energy efficiency, and eight overarching elements, such as efficiency, can be identified in addition to the three dimensions of economic, social, and environmental sustainability.

Die Ineffizienz und Unsicherheit der Demontage sind Hindernisse für das Skalieren der industriellen Kreislaufwirtschaft. Eine datengetriebene, reaktive Demontageplanung kann die Hindernisse reduzieren, indem sie Demontagepläne während der Demontage flexibel an neue Informationen über Produkte, Prozesse und Ressourcen anpasst. Sie erfordert einen Digitalen Zwilling des Demontagesystems. Der Beitrag leitet die notwendige Abstraktionsebene und die Systemgrenze eines solchen Digitalen Zwillings her.

An increasing number of renewable energy sources driven by ambitious sustainability efforts is both changing and challenging electricity systems worldwide. Successfully dealing with a variable and highly decentralized electricity supply associated with the rising share of renewable energy sources will be crucial in the near future. This paper investigates the potential of information technology (IT) for regional marketing of energy flexibility from the machine level in factories to the electricity-grid level. In this context, energy flexibility describes the capability to react quickly and cost-efficiently to alternating electricity availability. Based on a literature review of regional marketing mechanisms, existing concepts of IT platforms, and current real-world model regions, this paper aims to apply a holistic research approach to better understand the challenges regarding the energy transition on a regional level and corresponding requirements for IT platforms used for flexibility marketing. The approach allows to identify relevant key challenges and flexibility marketing use cases, attributing core importance to underlying production processes. In particular, target processes for respective use cases are derived based on a description of current regional marketing of demand flexibility. Successful implementation of target processes is required to provide regional energy flexibility via IT platforms. A comparison between current processes and target processes finally allows defining the need for change and development, e.g., of new intelligent interfaces, already during the conceptual test of new IT platforms. The introduced research approach was applied to one exemplary use case within the energy-flexible model region Augsburg of the Kopernikus project SynErgie , Germany. The paper especially illustrates that service-oriented IT platforms simplify the communication process between the relevant players in energy flexibility marketing.

In recent years, there has been an increased demand for elaborate monitoring techniques in laser material processing. This has been driven by the need for fast and cost-efficient quality assurance processes. At the same time, ultrashort-pulsed (USP) laser radiation has emerged as a promising technology for creating intricate microstructures in lithium-ion battery graphite anodes due to its high precision and negligible thermal impact. However, the integration of process monitoring in USP laser applications for graphite anode structuring is still unexplored. There is a lack of clarity on suitable sensors, observable parameters, and extractable process-relevant insights. The presented study addressed this gap by demonstrating the capability of state-of-the-art photodiode-based monitoring systems in collecting process-relevant data and deriving valuable insights. A sensor equipped with three photodiodes was employed to address these challenges. Exploratory data analysis and machine learning methodologies were leveraged to develop a data pipeline for processing the acquired information. The data were used to train convolutional neural networks that could accurately predict the focal position. At the same time, the limitations of traditional regression approaches could be shown. The findings advanced the understanding of the possibilities of process monitoring in USP laser applications and emphasized the significance of data-driven approaches in optimizing manufacturing processes.

International industrial companies operate complex value streams within production networks. Therefore, strategic network design aims to identify an efficient value stream from several value stream scenarios. For this purpose, Value Stream Mapping (VSM) is a well-established methodology from Lean Management. However, the complexity and variety of value streams in production networks can lead to high manual effort when using pen-and-paper-based VSM. Therefore, data-driven VSM based on process mining has to be applied. To create a comprehensive data-driven VSM, it is necessary to transparently understand the correlations between different dimensions, such as the material flow, the information flow, and the inventory, which requires a multidimensional process mining approach. Simulation experiments can generate the necessary data for each value stream scenario using a data farming based planning approach to conduct a data-driven VSM in strategic network design. However, no data model currently supports storing comprehensive datasets for multiple scenarios to enable multidimensional process mining. To overcome this shortcoming, this article presents a data model for applying multidimensional process mining that is scalable to multiple dimensions and scenarios. The data model is constructed based on the theoretical principles of data cubes and multidimensional process mining. The applicability is demonstrated by a case study of a production network from the automotive industry.