In-situ measurement of cutting tool wear is essential for understanding wear mechanisms and for improving machining reliability and process efficiency. However, most established measurement methods require periodic interruption of machining operations and tool removal, which prevents continuous observation of wear progression and may introduce measurement inconsistencies. This work presents a robot-assisted optical approach for the in-situ measurement of tool wear development directly on a CNC (Computer Numerical Control) machine. The system combines a chromatic confocal point sensor with a six-axis industrial robot and two precision linear stages. A tool coordinate system derived from the unworn tool geometry is introduced to compensate for robot positioning uncertainties and to ensure repeatable measurements of identical tool regions. The width of flank wear land is determined automatically from line-scan profiles using a segmentation-based algorithm. For flank wear land width determination, a standard deviation between 2.2 μm and 12.5 μm are obtained, which is mostly below the target uncertainty threshold of 10 μm (3
Industry 4.0 revolutionizes manufacturing by integrating Cyber-Physical Systems (CPS), the Industrial Internet of Things (IoT), and Artificial Intelligence (AI) to enable Smart Manufacturing by focusing on reducing downtime, enhancing efficiency, and so forth. On the one hand, this requires a critical focus on scheduling jobs to optimize resource utilization; on the other hand, it requires a suitable maintenance strategy, including the optimization of spare parts inventory management. Traditional scheduling models often overlook Preventive Maintenance (PM) activities and rely on periodic or reactive maintenance, leading to avoidable consequences. In many cases, these functionalities are treated in isolation, i.e., lacking a unified framework for a flow shop production environment. As part of the current work, an attempt is made to integrate job and PM scheduling, and subsequently, a novel optimization approach is proposed to jointly schedule jobs and PMs to enhance machine utilization while ensuring machine reliability. A mathematical formulation and a Genetic Algorithm (GA) approach are presented to address integrated job and PM scheduling under numerous constraints. Additionally, the GA approach is benchmarked against other meta-heuristic approaches, such as Differential Evolution (DE), Tabu Search (TS), and Simulated Annealing (SA). The joint scheduling of jobs and PM is validated in an automotive electronics manufacturing industry. The proposed models were tested and validated against available flow-shop benchmark data, as industrial data was not permitted to be published. The data was appended to include PM data, job priorities, and so on. The computational experiments demonstrate the effectiveness of these meta-heuristic approaches in balancing production efficiency with adherence to maintenance requirements by solving large-scale instances. The results highlight the benefits of integrated job and PM scheduling, which help develop resilient, data-driven smart manufacturing systems. The proposed approach is the first step in the integrated scheduling of jobs and PM. Maintenance activities inherently require spare parts, which, in turn, drive substantial inventory levels and associated costs. Subsequently, the next steps would be to predict PM plans based on historical data and thereby optimize the spare parts inventory level. This would integrate the job scheduling, PM scheduling, and spare parts inventory optimization.
Production equipment is often replaced or disposed of before its end of life, leading to an increase in waste and resource demand and increased cost for the procurement of new machinery. Transitioning to a circular economy is a possible solution to reduce the environmental impact by reusing and reselling production equipment. The adoption of reuse practices, however, requires a common information model documenting environmental data, terms, and descriptions related to the production equipment to assess the environmental impact compared to newly built machines. Existing ontologies, such as the Manufacturing Resource Capability Ontology (MaRCO), provide a structured representation of technical parameters of production equipment, but lack the integration of environmental information. To close this gap, this paper presents a selection of key performance indicators relevant to the environmental assessment of reused and new production equipment, along with an extension of an existing production equipment ontology to include these indicators. The extension is implemented and demonstrated in a use case study. The resulting ontology provides a baseline to reinforce the circularity of production equipment by enabling the assessment of environmental benefits.
Machining high-strength alloys frequently leads to segmented chip formation due to severe thermo-mechanical loading in the primary shear zone. The resulting process vibrations increase tool wear and degrade surface quality. This study investigates an approach for stabilization of machining processes through suppression of segmented chip formation under production-relevant cutting parameters by locally limiting chip space using a counter body, the Constraint tool. Therefore, orthogonal cutting experiments are conducted to characterize unconstrained chip formation process and associated tool loads as well as deflections due to chip segmentation during machining of titanium alloy Ti6Al4V. In addition, finite element simulations that reliably predict segmented chip formation are used for a systematic variation of edge shape and positioning of the Constraint. Optimal configurations that suppress macroscopic chip segmentation are identified and transferred into physical Constraint designs. These are manufactured using wet abrasive jet machining and validated in orthogonal cutting experiments using a custom clamping and positioning setup. A final comparison between constrained and unconstrained cutting conditions quantifies the degree of segmentation suppression. Subsequently, the results obtained are used to check the transferability to steel alloy 51CrV4.
Modern production systems are increasingly challenged by volatile and complex market conditions. To maintain their competitiveness, the application of Reinforcement Learning (RL) in production planning and control has shown considerable promise. However, the state space in RL approaches is often excessively large and complex, which triggers the curse of dimensionality and hinders learning efficiency. A significant research gap exists regarding systematic methodologies for defining, evaluating, and iteratively optimizing the state space to improve both the transparency and learning performance. This paper addresses this gap by proposing a novel methodology consisting of two steps. First, an initial state space is systematically derived from corporate planning goals with the help of a feature map. Second, Shapley Additive Explanations (SHAP) values are utilized to quantify the influence of each state feature on the RL agent’s action selection and to iteratively improve the state space accordingly. The proposed methodology is validated using a real-world industrial use case from the machinery industry. The results indicate that the methodology successfully identifies dominant state features, leading to an improved learning behavior and enhanced transparency in the RL agent’s decision-making process.
Modern manufacturing requires scheduling methods that adapt to changing order arrivals, machine disruptions, customer priorities, stakeholder preferences, and time-varying energy conditions. This paper proposes a preference-conditioned deep reinforcement learning (DRL) approach for dynamic scheduling in sustainable and robust manufacturing. The approach is embedded in a cyber-physical production system (CPPS)-oriented framework that links production states, machine availability, energy-related background data, simulation-based learning, performance monitoring, and decision support. Within this framework, a Double Deep Q-Network (DDQN) scheduler is developed for joint job sequencing, machine assignment, and start-time adjustment. The scheduler uses a candidate-based state representation for dynamic order arrivals, vector-valued Q-output for objective-specific value estimation, and a priority- and preference-aware reward design. Customer priorities are treated as order-level attributes, while stakeholder preferences are encoded as system-level objective weightings. This enables one policy to consider energy-related cost, carbon emissions, energy demand, and tardiness while adapting to different preference profiles. The concept is demonstrated in an on-demand manufacturing (ODM)-oriented parallel CNC machining case with heterogeneous orders, product-specific setup and processing requirements, hourly electricity prices, carbon-intensity signals, and curriculum-adaptive machine breakdowns. DDQN is compared with three dispatching rules and two DRL baselines under shared training and testing scenarios. The results show that DDQN achieves the lowest energy-related cost and carbon emissions in training and unseen testing while maintaining acceptable delivery performance. Overall, the study demonstrates the potential of CPPS-oriented and preference-conditioned DRL for adaptive, energy-aware, and robust scheduling in smart manufacturing systems.
Accurate prediction of flat pattern lengths is essential for achieving high dimensional precision in sheet metal air bending. Conventional approaches, such as the DIN 6935-based calculation implemented in CAD systems, often result in significant deviations due to their limited consideration of material behavior, bending angle, and tool geometry. This study presents a universal methodology for determining bend deduction based on controlled experimentation, dedicated measurement technology and systematic validation. A dedicated measurement fixture was developed and evaluated through Type-1 and Type-2 measurement system analyses to ensure reliable leg-length measurement over different bend angles. As a proof-of-concept application, bend deduction values were determined for EN AW-6082 T6 with a sheet thickness of 1.0 mm across bend angles from 45° to 135°. The experimentally derived values were transferred into a bend deduction table (BDT) by interpolation and implemented for CAD-based flat-pattern calculation. Validation against DIN 6935 predictions demonstrates that the proposed BDT significantly improves flat-pattern length accuracy, reducing deviations by up to 0.38 mm. The results demonstrate that an experimentally calibrated BDT can improve flat-pattern accuracy without requiring finite element simulations. The methodology can be transferred to other materials, sheet thicknesses, and bend radii through empirical calibration and is therefore suitable for broad industrial application.
Milling, especially of lightweight components, but also of geometrically complex functional parts, often requires the use of long-overhanging cutting tool systems. This, as well as the interrupted cut, often causes significant tool vibrations. These not only considerably reduce tool life, but can also lead to increased surface roughness or even substantial workpiece damage. The use of damped cutting tool holders significantly increases process stability and ensures reliable machining operations even at elevated parameters. In this context, the use of particle dampers, in which vibration energy is dissipated due to inelastic particle collisions and friction processes, have been fundamentally proven in recent investigations with rotating tool systems. The use of additive manufacturing processes, such as Laser Powder Bed Fusion (LPBF), enables the application of complex designed particle dampers close to the working zone. However, a process-specific modification is further possible applying selected filling particles. The investigations presented in this paper focus on analyzing the influence of additional internal structures integrated into the segmented cavities of HSK63 cutting tool holders on the dissipation capacity. In particular, a specially developed analogy test setup is used to demonstrate that the internal structure improves inhomogeneous particle distribution within the cavity segments due to the centripetal force that occurs during rotation. In addition, the additional contact surfaces resulting from the internal structure have a positive effect on the dissipation mechanisms. Another major part of the investigations is to develop an innovative strategy for the additive manufacturing of ball masses within the applied lattice structure. Thus, the dissipation potential could be further increased due to the higher masses of the individual particles in conjunction with the lattice structure. This structure prevents severe inhomogeneities resulting from centripetal force by allowing a defined motion range corresponding to the particle size. Comprehensive analogy tests demonstrate that this innovative particle damper design can enable broadband damping performance that considerably exceeds the dissipation potential of conventionally designed particle dampers.
Manufacturing companies are currently facing multiple challenges, ranging from international crises such as the COVID-19 pandemic, conflicts between countries, disrupted supply chains due to environmental disasters and demographic challenges in some regions. Nowadays many approaches, methods, and philosophies for continuous improvement, such as Lean Manufacturing, operational excellence, agile or Six Sigma and its respective elements, are firmly established in many industrial manufacturing processes. The multiple crises within the VUCA world may have a strong impact on the previously mentioned approaches. To manage these crises from a manufacturing company’s perspective, the concept of Organizational Resilience has become a widely discussed topic nowadays. This paper reviews possible conflicts and synergies between Lean Manufacturing and Organizational Resilience. The findings are integrated into a conceptual tension field illustration, which shows the common conflicts and synergies based on scientific literature. Based on the conceptual tension field, the Consensus Matrix and the Evidence Status Matrix are introduced discussing the underlying reasons and interactions of conflicting and synergetic Lean and Resilience principles. The findings show that synergies between Lean and Resilience dominate for human- and learning-centered Lean principles, while conflicts concentrate in supply-chain-related principles such as just-in-time, extreme inventory minimization and overly rigid standardization and depend on the type of disruption considered. This provides manufacturing companies with a structured basis for configuring Lean practices in a context-sensitive way that balances process efficiency and economically reasonable Organizational Resilience. Further directions for future research are identified to examine and validate how the optimal balance between Lean Manufacturing and Organizational Resilience can be addressed and achieved.
This study investigates the influence of laser surface texturing on tool wear evolution, thermal behavior, and cutting forces in high-feed milling of Inconel 718. Comparative analyses were conducted between the non-textured and laser-textured inserts under identical milling conditions. Wear progression was evaluated, revealing that the crater wear and localized notch formation dominate in both cases due to severe thermo-mechanical loading of the interrupted cutting. While both inserts exhibited similar qualitative wear evolution; however, the laser-textured inserts demonstrated reduced wear intensity and a more distributed wear along the rake and flank faces. Additionally, it was shown that the surface texturing significantly amplified the effective flank surface area by approximately a factor of ten, improving convective heat dissipation. The infrared thermography confirmed a substantial reduction in maximum cutting temperature (from 379 °C to 326 °C) for the textured inserts, indicating improved thermal management. Furthermore, the cutting force measurements reveal a more stable force evolution for the laser-textured tool, with reduced growth in axial and radial components, correlating with the corresponding flank and notch wear. These findings demonstrated that the laser texturing effectively modified the wear progression, thermal load, and could stabilize cutting performance.
This special issue was initiated within the framework of the DFG priority program 2231 FluSimPro - Efficient cooling, lubrication and transportation - coupled mechanical and fluid-dynamical simulation methods for efficient production processes. It features a broad spectrum of research approaches and highlights recent advances in modeling the interaction between cutting fluid, tool, and workpiece in machining operations. Resulting coupled mechanical and fluid dynamic simulations provide the foundation for the design of enhanced cooling lubricant supply systems tailored to specific process requirements, such as heat dissipation in the cutting zone and efficient chip evacuation. In addition to the numerical investigations, dedicated experimental test rigs are presented and discussed. These setups provide detailed insights into cooling lubricant supply, enable the characterization of relevant fluid properties, and support the validation of the developed simulation systems. The reported findings ultimately pave the way for a more efficient and sustainable use of cutting fluids. Alongside contributions from members of the research alliance, this issue also includes selected international papers that provide a broader perspective on current studies and emerging developments in optimized cooling lubricant supply for machining processes.
The effects of rolling temperature, intermediate heat treatment, equivalent strain, equivalent strain rate, and number of passes on the microstructure and mechanical properties of Mg alloys containing block-shaped long period stacking ordered (LPSO) phases were systematically investigated. Hot rolling at 500 °C and 520 °C produced predominantly recrystallized microstructures, with higher temperatures increasing the dynamic recrystallized (DRX) fraction (from 89 φ _v = 0.97, φ̇_v = 70 s⁻¹) yielded fine, homogeneous grains (1 μm) with extensive DRX and 25
Manual assembly continues to be an important part of industrial production systems due to its flexibility and adaptability, especially in environments with high variability. However, the increasing complexity of systems also leads to greater physical, mental, and emotional strain on workers. This paper outlines an integrated approach to assessing stress and strain in manual assembly systems, which is based on a structured analysis of existing measurement methods. Based on this, an integrative analysis of stress and strain in manual assembly was carried out to systematically categorize and examine these factors. Using established ergonomic and psychological framework concepts, we distinguish between external stress factors and individual stress responses. The analysis reveals that stress and strain are generally assessed in isolation from each other, which limits our understanding of how they interact with each other in socio-technical production environments. The interactions between stress and strain are visualized using a cause-and-effect representation. The proposed integrated assessment offers a structured approach to combining subjective and objective measurement techniques, supporting the design of health-oriented, performance-driven manual assembly systems.
This special issue was initiated within the framework of the DFG priority program 2231 FluSimPro – Efficient cooling, lubrication and transportation – coupled mechanical and fluid-dynamical simulation methods for efficient production processes. It features a broad spectrum of research approaches and highlights recent advances in modeling the interaction between cutting fluid, tool, and workpiece in machining operations. Resulting coupled mechanical and fluid dynamic simulations provide the foundation for the design of enhanced cooling lubricant supply systems tailored to specific process requirements, such as heat dissipation in the cutting zone and efficient chip evacuation. In addition to the numerical investigations, dedicated experimental test rigs are presented and discussed. These setups provide detailed insights into cooling lubricant supply, enable the characterization of relevant fluid properties, and support the validation of the developed simulation systems. The reported findings ultimately pave the way for a more efficient and sustainable use of cutting fluids. Alongside contributions from members of the research alliance, this issue also includes selected international papers that provide a broader perspective on current studies and emerging developments in optimized cooling lubricant supply for machining processes.
This work presents a comprehensive framework that combines modeling, simulation and experimental validation of an actuator-based textile draping system. The extended simulation incorporates geometric constraints, including fiber wrapping effects and contact point displacement. A key aspect of the study is the characterization and modeling of hysteresis in pneumatic continuum actuators. Nonlinear pressure–strain behavior is determined experimentally using a dedicated test setup and approximated using data-driven approaches. The accuracy and applicability of these modeling methods are then compared. To validate the results experimentally, a camera-based motion capture system from the company Optitrack is used to record the spatial positions of defined marker points. The acquired displacement data enable a detailed comparison between the nominal and actual draping lines, enabling the identification of systematic deviations. These are then integrated in the simulation model to increase the precision for the calculation of the control data. The integration enhances the tracking accuracy, as defined by the absolute mean deviation of all kinematics. This enhancement increases accuracy in the x-direction by 47
This collection provides an overview of current developments in the field of metallic hybrid and porous materials and components made of such materials. Hybrid materials have a locally defined varied material composition while porous materials feature an intentionally cellular material structure. Hybrid porous materials combine both a locally varying material composition and a porous structure. This collection was initiated by the DFG Collaborative Research Centre TRR 375 “Multifunctional High-Performance Components made of hybrid porous materials", which researches the design, manufacture, and characterization of these materials and components. In this collection, the additive manufacturing of hybrid and porous materials is highlighted and methods for the monitoring and control of the manufacturing processes and thus the control of the component properties are pointed out. In addition, approaches to determine the properties of hybrid and porous materials and the application characteristics of hybrid and porous components are outlined. The collection offers insights for those interested in manufacture, characterization, and applications of hybrid porous materials.
Dynamic scheduling in Human-Robot Collaboration (HRC) is crucial for realizing the human-centric manufacturing goals of Industry 5.0, yet it faces significant challenges from uncertainties, particularly those stemming from human factors, which impede real-time optimization. This paper proposes a novel dynamic scheduling model for HRC assembly environments, leveraging Deep Reinforcement Learning (DRL). The model utilizes a Graph Neural Network (GNN) to embed complex system states, including operator characteristics and task assignments (human-only, robot-only, and human-robot collaborative). A scheduling policy, trained using the Proximal Policy Optimization (PPO) algorithm, is developed to dispatch tasks dynamically with the objective of minimizing makespan while adapting to uncertain operational events. The efficacy of the proposed approach was evaluated through numerical experiments based on a realistic lithium battery pack assembly scenario, considering various HRC configurations and human performance variability modeled using skewed distributions. Comparative analysis against traditional metaheuristic algorithms (Genetic Algorithms with crisp, fuzzy, and neutrosophic number representations) and OR-Tools demonstrated the DRL model’s superior performance. It consistently achieved significantly lower makespan and improved scheduling stability (lower deviation in makespan), particularly in complex multi-human, multi-robot configurations and in managing process uncertainties. The findings highlight the potential of DRL to provide robust and adaptable solutions for dynamic HRC scheduling, thereby enhancing the efficiency and reliability of collaborative manufacturing systems.
Grinding of Ti-6Al-4 V remains challenging due to low thermal conductivity, high chemical reactivity, and pronounced ductile deformation behavior. Existing research suggests that titanium oxide layers may influence grinding behavior by modifying interfacial interactions and material removal mechanisms. The experimental isolation of oxygen-related effects during grinding remains challenging, as conventional machining environments typically allow continuous oxidation of surfaces and chips. Within the research framework of the Collaborative Research Centre SFB 1368 “Oxygen-Free Production”, dry surface grinding of Ti-6Al-4 V is investigated under ambient air and strictly oxygen-free conditions using a gastight grinding setup that enables an XHV-adequate atmosphere with extremely low oxygen partial pressure. This approach allows oxygen-induced effects to be systematically suppressed and their influence on grinding behavior to be directly assessed. Process forces, surface topography, residual stresses, wheel loading, and chemical surface composition are analyzed using force measurements, optical surface measurement, X-ray diffraction, scanning electron microscopy, EDX and optical microscopy of metallographic cross-sections. The results show that grinding under oxygen-free conditions leads to increased wheel loading, higher and more unstable forces, wave-like surface topographies, elevated tensile residual stresses, and increased surface roughness. These effects are attributed to the absence of process-induced oxide formation, which promotes adhesion and shifts the process from cutting to being more friction-dominated. These findings demonstrate that oxidation during grinding plays a critical role in stabilizing material removal mechanisms in titanium grinding.
Shorter product life cycles and an ever-growing variety of product variants require manufacturers to perform production ramp-ups more frequently. These ramp-ups, as well as the subsequent series production, are becoming increasingly complex. The associated productivity losses, especially during ramp-up, threaten companies’ competitiveness and must therefore be minimized. In largely manual processes, worker competence is the key lever for reducing these losses: the speed and quality with which workers perform a task depend primarily on their individual skills. This paper presents a methodology that integrates established learning- and forgetting-curve models to predict individual competence development as a function of the production program. Unlike existing approaches, it primarily focuses on the individual task-based execution time of one worker. Based on this, execution times can be examined in detail at the task, product, and workstation levels. The proposed methods and visualizations provide valuable insights for actively managing competence development and thereby effectively reducing productivity losses, enabling conclusions about task sequencing, employee assignment, and learning intervals that can accelerate ramp-up performance in practical applications.
For the efficient manufacturing of thin-walled components in large quantities, multistage progressive die forming processes are suitable due to the automation potential and high output rates. This process approach is extensively used for fineblanking and stamping components. However, potential for lightweight design can be further realized by functional integration of these parts. Gearing geometries are common functional elements in this case. The bulk forming of these functional elements using sheet metal as semi-finished material can be attributed to sheet-bulk metal forming (SBMF). This process class is accompanied by challenges regarding the obtainable die filling. Furthermore, previous studies have shown that there is a geometry-dependent anisotropic material flow during the extrusion of functional elements from coil. Within this study, the forming stage of a multistage process setup, which consists of various trim operations and the forming stage, is investigated using a metal strip as coil layout geometry. As a workpiece, a circular component with an abstracted locking tooth geometry is manufactured by forward extrusion. By comparing the process results of the coil layout to the forming of a circular blank, the influence of the coil layout on the direction-dependent material flow is analyzed. In particular, the effect of the workpiece connector to the carrier strip on the resulting material flow is relevant within this study. Subsequently, strategies for the improvement of die filling by geometric adaption of the semi-finished material and varying the stage sequence are derived.
