Chatter remains a persistent challenge in high-performance milling as it limits surface quality, tool life and productivity. Conventional detection methods based on external sensors or single-axis current signals often fail to capture the multidimensional dynamics of modern machining. This work advances the development toward sensorless chatter detection by incorporating bidirectional NC signal analysis in circular ramp milling. The new experiment enables simultaneous X- and Y-axis engagement, generating variable cutting force components and revealing cross-axis interactions that are not observable in uniaxial ramp tests. High-frequency NC signals from the position control loop are acquired via a Siemens Industrial Edge Device and evaluated using Principal Component Analysis (PCA) to identify dominant vibration modes and their axis-specific contributions. The results demonstrate that multiaxial motion enhances the observability of chatter phenomena, while PCA effectively compresses redundant information into a compact feature set that is suitable for future use at the periphery and thus for sensorless chatter detection. Beyond chatter detection, the approach provides deeper insight into dynamic tool-workpiece interactions and contributes to data-driven methods that support resilient, sustainable and AI-enabled manufacturing systems.
The arrangement of components within a robot cell is up to this point a mainly manual task. This paper presents a method for an automatic layout optimization of robot assembly cells in Visual Components using a genetic algorithm. The main goal of this contribution is to improve the cell layout of a robotic assembly cell automatically instead of manually changing it. In addition, a sensitivity analysis is conducted to evaluate different optimizers to present the most suitable parameter configuration for two differently packed robotic assembly cells. As expected, the quality and velocity of the optimization depend heavily on suitable parameters for the optimization method. While the quality of the results for the spacious cell remained virtually unchanged (~20% lower space utilisation, ~10% faster cycle time under the same conditions) and only the speed of approaching the optimum was improved by parameter optimisation, the difference was much more pronounced for the compact cell: the average optimisation was almost doubled.
Directed Energy Deposition (DED) as an additive manufacturing (AM) method offers great potential across various applications. However, challenges persist in controlling thermal history, grain structure, distortion, and residual stresses when manufacturing. Numerical simulation of the DED process enables prediction of these characteristics and supports first-time-right manufacturing. In most cases, computer-aided design (CAD) models of the post-machined component are used for toolpath planning and simulation. Since AM parts are produced layer by layer, their as-built geometry deviates from the idealized CAD model due to process-specific effects. This mismatch introduces errors in process simulations. To address this problem, a path-based geometry modeling tool is introduced that reconstructs a geometry closely approximating the as-built part, based on the actual toolpath and deposition bead dimensions. This model more accurately represents the final part’s volume and geometry. For thermal process simulation, material deposition is also modeled based on the same toolpath, establishing a unified workflow that uses standardized toolpath data for both geometry creation and thermal analysis. This ensures independence from specific machine types and enables early-stage simulation. The approach is validated through thermal simulations and experimental measurements, demonstrating high agreement and confirming that the proposed geometry modeling method improves the prediction of process parameters such as correct dwell times or interlayer temperatures.
Upper limb exoskeletons (ULEs) have shown significant potential in enhancing mobility and rehabilitation outcomes. However, pediatric applications pose unique challenges due to anatomical differences and the need for adaptable and comfortable components. This study develops an automated design-to-print optimization framework that combines simulation-informed design of experiments, physical testing, and artificial intelligence (AI) and machine learning (ML) to rapidly identify design and print parameters for 3D-printed exoskeleton joints. The workflow employs a two-stage design of experiments (DoE) approach: the first identifies optimal geometric parameters using finite element analysis (FEA), and the second optimizes print parameters through mechanical testing. The resulting data are used to train a multiple linear regression (MLR) model that predicts joint strength and print quality from design and manufacturing settings. Statistical analysis identified shaft diameter as the most significant design parameter and wall perimeter count as the dominant print factor influencing load capacity. The trained MLR model achieved an average prediction error below 0.10 MPa. This integrated workflow demonstrates how AI-assisted surrogate modeling can bridge virtual design and physical fabrication, enabling rapid, data-driven optimization of 3D-printed rehabilitation components.
Cyber-Physical Systems (CPS) are central to modern manufacturing, yet their increasing interconnectivity exposes production environments to sophisticated cyberattacks. Existing resilience approaches often remain fragmented, focusing on isolated detection or prevention measures without providing actionable guidance for controller-level stabilization and recovery. This paper contributes a controller-level taxonomy for cybersecurity resilience in production CPS, integrating detection, mitigation, and recovery requirements directly into the control loop. The taxonomy is rooted in dynamic systems principles, and it structures resilience in terms of two classes of tuples, namely, attack tuple, specifying adversarial logic through vectors, targets, methods, effects, and informing outputs; and defence tuple, comprising detection mechanisms, defence actions, objectives, redundancy, secure estimation, and resilient controllers. To demonstrate how the taxonomy guides controller design, we instantiate it through a Model Predictive Control (MPC) formulation that embeds resilience objectives into predictive optimization, constraint handling, and trajectory reconfiguration. Comparative results with a Linear Quadratic Regulator (LQR) baseline illustrate how taxonomy-informed adaptations enable MPC to maintain stability and enforce constraints under adversarial conditions. Together, these contributions establish a foundation for embedding resilience into controller design, enabling CPS to anticipate, withstand, and recover from cyber threats in real time.
