Structured surfaces at the micro- and nanoscale enable precise control of cell–material interactions in tissue engineering and regenerative medicine (TERM). This keynote examines scalable manufacturing methods for 2D substrates, 3D scaffolds, and implants, analyzing relationships between topography, biological performance, and process efficiency. Key technologies include nanoimprint lithography, micro injection molding, high-resolution additive manufacturing, and ultrafast laser texturing. Strategies for combining biological efficacy with cost-effective production are presented, along with multifunctional designs for osseointegration and antibacterial activity. Future perspectives address biomimetic and patient-specific structures, process–function modeling, and smart materials to support advanced TERM applications and clinical translation.
Many critical manufacturing processes in sheet metal forming are characterized by shear-dominated mechanisms. By clearly defining how shear-dominated processes can be classified based on stress triaxiality and the Lode parameter, this paper provides an overview of their characteristics and challenges in sheet metal forming. Based on the distinction into in-plane and out-of-plane shear-dominated processes and an introduction to the mechanical fundamentals of shearing mechanics, the topics of testing and measurement methods, simulation, and tooling are discussed. Finally, concepts and principles are illustrated with specific application examples. The paper concludes with open topics and upcoming challenges.
Industrial maintenance plays a critical role in enabling complex assets such as production equipment and more widely factories to be available for use in a sustainable manner. With the emergence of digital technologies such as digital twins, IoT, virtual and augmented reality, and machine learning, significant opportunities are observed to transform the way in which maintenance is delivered. It is anticipated that maintenance will become more affordable, proactive, predictive, outcome driven and sustainable in the future. This paper presents the state of the art and industrial perspectives on digitally enhanced maintenance covering current approaches and challenges. Furthermore, a comprehensive framework is presented to structure future research directions by considering the concept of a maintenance ecosystem. Finally, a strategic vision is set out to convey the future of maintenance.
4H-SiC wafers require damage-free, ultra-smooth surfaces for power devices manufacturing, whereas conventional chemical mechanical polishing (CMP) often suffers from limited throughput and high cost. Slurry-less electrochemical mechanical polishing (ECMP) enables efficient smoothing with low cost, but surface scratches still limit surface integrity and yield. This study systematically observed and classified surface morphologies formed on 4H-SiC under different ECMP conditions, including conventional groove-type scratches and ECMP-specific ridge-type traces, and elucidated the mechanisms underlying each type. Based on these insights, practical process-optimization strategies were proposed to suppress trace formation and improve slurry-less ECMP stability and applicability.
Hardness is a critical surface integrity metric governing the performance of machined components. Due to experimental constraints, hardness characterization typically relies on sparse measurements, limiting spatial resolution and generalization across machining conditions. This work addresses this limitation by introducing a physics-informed graph transformer for hardness prediction in milling, using process parameters and in-situ power signals. To mitigate data scarcity, power signals are synthesized, and a graph representation is constructed to embed machining physics and measurement relationships. Data analysis demonstrates robust and generalizable prediction under limited reference data, establishing a soft sensing framework for scalable characterization of hardness from milling operations.
Advanced high-strength steels (AHSS) often exhibit a trade-off between global and local formability. This study investigates five AHSS grades to elucidate microstructural factors governing local formability and proposes a quantitative metric for damage tolerance. Shear-induced edge damage is correlated with the hole-expansion ratio (HER). An integrated approach combining high-throughput nanoindentation mapping with the effective heterogeneity index (EHI) captures hardness distribution disorder and mechanical contrast. EHI correlates strongly with the HER, effectively predicting local formability and damage tolerance of multiphase steels. This proposed approach offers a new tool for designing AHSS with an optimal balance between strength, ductility, and damage tolerance.
The manufacturing of integrated circuits (ICs) is an enabler of technological innovation and is critical to the resilience of industry and national security. Four levels of excellence are necessary as the foundation for semiconductor manufacturing: ecosystem; fab profitability; IC design for manufacturing and research & development; culture and customer trust. In this paper, the culture, semiconductor manufacturing processes, innovations of equipment, removal processes, in-line metrology for process control, and data analytics are discussed. Future topics, including advanced packaging, sustainability, fab operation optimization, and in-space semiconductor manufacturing, as well as workforce policy and ethics, are elaborated.
Climate change, driven largely by greenhouse gas (GHG) emissions from anthropogenic activity, demands urgent global decarbonisation. Industry contributes 30–35% of global GHG emissions, with manufacturing value chains playing a pivotal role. This paper explores decarbonisation measures within manufacturing, focusing on both material and energy-related measures. Emphasizing efficiency and substitution, it outlines actionable strategies at process and system levels towards net zero carbon emissions. Supported by literature analysis and case studies, a structured framework is presented enabling to assess and prioritize decarbonisation efforts, aligned with planetary boundaries. Based on this, future research demands are derived and discussed.
This study investigates the influence of depth-dependent microstructural differences in case-hardened workpieces on process loads and subsurface characteristics. Specimen preparation using EDM and polishing enables analysis of hardness depth profiles and microstructural modifications after single-stage grinding for different depths within the case-hardened layer. Results show reduced modification depths for deeper levels. To understand underlying mechanisms, phase fractions (martensite, retained austenite, cementite) and hardness depth profiles were calculated analytically. Carbon-dependent softening of martensite is the main factor influencing hardness modification depths. In two-stage grinding, measurements and simulations reveal depth-dependent increased modification depths due to roughing and reduced loads in finishing.
The number of publications on digital twins went from 3.000 in 2017 to over 70.000 in 2024. This paper analyses the use and status of digital twins for machine tools as well as the necessary modelling and identification for enabling optimization, process planning, process control and predictive maintenance on the machine tool level and the fleet level. Recent research on digital twins for machine tools leveraging AI or greybox models is also presented. This paper gives an overview of the application and the technology behind digital twins used for machine tool design, commissioning, and operations.
Realizing a circular economy in manufacturing requires coordination across value-chain stakeholders and understanding how local design and operational decisions affect system performance. Existing life-cycle engineering approaches rely on isolated indicators and provide limited support for modelling stakeholder interdependencies. This paper proposes a stakeholder-driven assessment method that links actionable circularity indicators to system-level life-cycle performance. The method integrates Weighted Intuitionistic Fuzzy Delphi and DEMATEL to quantify causal dependencies between stakeholder-controlled indicators and value-chain variables embedded into life-cycle sustainability formulas. A ten-year Product-as-a-Service case study shows that stakeholder weighting moderates systemic and cross-value-chain effects that are difficult to capture with conventional lifecycle assessment (LCA) or lifecycle cost (LCC) analyses.
Hydrogen embrittlement reduces the low‑cycle fatigue life of 316L stainless steel in hydrogen environments. This study evaluates electropolishing as a surface treatment to improve fatigue performance under electrochemical hydrogen charging. Strain‑controlled low-cycle fatigue (LCF) tests (0.8–1.6%) were performed on as‑received and electropolished specimens with and without hydrogen. Electropolishing reduced surface roughness, produced a Cr‑enriched passive layer, and mitigated brittle fracture and irregular striations. The fatigue life of hydrogen‑charged electropolished specimens improved by about 30% at high strain, demonstrating that electropolishing is an effective strategy to enhance hydrogen fatigue resistance.
Reconfiguration of manufacturing systems is a frequent and labor-intensive activity driven by evolving products, volumes, and operational constraints. While existing methods based on optimization, digital twins, and expert modeling provide rigorous analysis, they require explicitly specified models and substantial manual effort. This paper presents a multi-agent AI framework for generating geometry-feasible reconfigurable system layouts from updated production requirements. Leveraging 3D machine models, multiple large language model–based agents collaboratively generate and refine shopfloor layout candidates with Unity3D visualization. These high-quality solutions can be optionally optimized further using established reconfiguration methods.
The incremental profile forming process enables the flexible forming of profiles with axially varying cross-sections. The localized complex deformation field induces large shape deviations. It is hypothesized that the shape accuracy can be improved by stress superposition. For the fundamental case of axial groove forming, several stress superposition modes are examined. Axial stress superposition is shown to be the most effective one and has been analyzed further. An axial tensile stress of the order of the initial flow stress improves the shape error from 47% to 2%. At the same time the steady-state indentation and axial forces are reduced by approx. 30-55%.
There is an increasing need to develop sustainable circular value chains for spent lithium-ion batteries (LiBs) in response to the diffusion of electric vehicles. This paper aims to develop a simulation-based methodology for circular battery value chains by integrating scenario analysis, a market model, and a product life-cycle model. The methodology enables dynamic simulation that accounts for temporal changes and balances LiB demand and supply. A case study of Japan and Germany indicates that combining product-as-a-service models (e.g., leasing) with life-cycle management is promising for reducing CO2 emissions over the battery life cycles during 2021–2050.
This study formulates a condition-based battery remanufacturing scheduling (CBRS) problem to address uncertainties inherent in electric-vehicle battery remanufacturing, where internal module conditions are unknown prior to disassembly. Such uncertainties create challenges in determining the interdependent routing, machine assignment, and operational decisions. To address these challenges, we develop risk management methods to minimize makespan and maximize recovered value while explicitly controlling the risk of route nonviability. The proposed methods improve the robustness of decision-making and consistently outperform heuristics, including Shortest Processing Time and the proposed Smallest Module Requirement, achieving up to 14.5% higher recovered value while reducing makespan by approximately 50%.
Additive manufacturing (AM) enables the production of complex, near-net-shape components, but as-built components exhibit high roughness and defects that compromise performance. Abrasive finishing processes are essential to achieve the required surface integrity, dimensional accuracy, and functional properties of components. This review examines state-of-the-art abrasive post-processing for AM, including grinding, abrasive fine-finishing, and mass finishing approaches. Interactions within AM process chains, effects on surface topography and mechanical properties, and industrial case studies are discussed. Current challenges and future research needs are identified to guide the integration of abrasive finishing into efficient AM process chains.
A surrogate analytical model was constructed and validated for deep rolling in train axle production. Two independent, distinct finite element models were developed to simulate both the time evolution and the stationary solution of this cold forming process. To reduce computational time, an efficient surrogate model was fitted that captures key features observed in the finite element modelling, such as the hardening relay and a maximum in the forming force characteristics as a function of feed rate. A measurement setup was developed for validation with pull-off roller ditch depth measurement.
Semidiscretization is a well-known method for stability prediction in milling which provides a quantitative measure of stability very convenient for optimization purposes. Existing approaches have extended it to 5-axis cases or varying thin wall dynamics, but none of them have provided a robust workflow to simulate and optimize real industrial scenarios. This work presents a 5-axis milling stability framework combining a tri-dexel engagement model, an automatic finite element model of the workpiece, and semidiscretization, enabling process optimization. The approach is applied to an industrial case, where a mark caused by surface location error is mitigated while keeping the process stable.
Designing products that embody circular economy principles is central to enabling macro-economic transition away from linear production practices and mitigating their associated environmental impacts. However, circular product design (CPD) is inherently complex, requiring trade-offs among functionality, cost, and numerous product circularity-driven requirements. While comprehensive product circularity metrics enable systematic assessment of such requirements, existing CPD optimization efforts are often limited by incomplete metric integration and insufficient consideration of interdependencies and trade-offs. Moreover, optimization alone does not guarantee robust or fail-safe CPD outcomes. This paper presents a novel axiomatic design-leveraged, product circularity metrics-driven approach to address these limitations.
