Open Access

In manufacturing, 3D CAD models define the geometry of a part while 2D technical drawings specify its manufacturing requirements, yet the two are rarely linked automatically. Associating drawing annotations with their corresponding geometric surfaces in the 3D model is a prerequisite for automated Product Manufacturing Information (PMI) extraction and remains unsolved. We formulate this as self-supervised CAD-face localization: given a point marker on a projected surface in a technical drawing, the model identifies the corresponding face in the 3D CAD model. In deployment, such markers are obtained from PMI arrow tips, making our approach a composable component of an end-to-end PMI transfer pipeline. A Graph Neural Network encodes 3D geometry while a CNN or vision transformer encodes 2D drawing crops, fused via cross-attention. Training uses synthetic data of varying complexity, requiring no manual annotation. Our best configuration achieves 98.04% accuracy on the face matching task, demonstrating successful cross-modal correspondence learning.

A coupled nonlinear simulation framework for a drone carrying a payload suspended via rope is presented. The tethered configuration introduces pendulum-like dynamics coupled to the drone’s motion. The framework is intended to support the development of strategies for safe trajectory-following with collision avoidance. The payload is modeled as a rigid pendulum with a holonomic length constraint enforced via Lagrange multipliers and stabilized using the Baumgarte method, ensuring numerical consistency while preserving physically correct rope forces. The quadrotor model is calibrated up to the attitude-control level, enabling direct specification of thrust magnitude and direction consistent with state-of-the-art autopilot interfaces, while retaining actuator limitations and operating close to the physical origin of the pendulum dynamics. Simulations demonstrate that the model reproduces physically plausible system behavior, validating its suitability as a basis for control design. System identification experiments confirm frequency-dependent coupling between drone and payload, including tension loss phenomena and nonlinear interaction effects. The presented framework establishes a foundation for future research into trajectory tracking in the presence of pendulum dynamics, fast and safe payload placement during swing motion, and integrated collision avoidance under simultaneous consideration of pendulum motion, drone trajectory, and actuator constraints.

Automated sheet metal handling in the automotive industry using robot manipulators is a standard in modern production. However, the desire of automotive companies to speed up the production process on the assembly line and at the same time to reduce expensive hardware components poses new challenges for robotics. Excessively rapid movement of flexible parts or sheet metal can either lead to its plastic deformation or increase the decay time of the vibrations to such an extent that it is necessary to wait before the part can be further processed, for example by welding. The traditional approach to solving these problems is to add fixing points for the part and/or to allow for waiting times at the end of the robot movement to ensure that the sheet metal vibration subsides. This means that more effort than necessary has to be put into the hardware setup or a poor cycle time has to be accepted. In our work, we propose to improve virtual commissioning systems by conducting a deeper analysis of the dynamics of sheet metal parts during its movement by the robot. To achieve this, a three-step optimization strategy is proposed. The first step is a structural transient analysis of the thin metal part under disturbances that arise during its movements. The second step is the creation of a substitute model, which is trained on the basis of the data obtained in the first phase and considerably reduces the computing time required compared to the time needed for a finite element method (FEM) simulation. Subsequently, this is used to optimize robotic handling efficiency by optimizing the robot trajectory. By addressing the challenges posed by sheet metal dynamics, enhanced process control, reduced cycle times, and ultimately, improved manufacturing outcomes are anticipated.

Thread forming requires precise lubricant application because high contact pressures and process temperatures strongly influence tool loading, friction, and process stability. Although minimum quantity lubrication (MQL) systems are widely used, current spray-based approaches can still suffer from spray losses, insufficient wetting of the thread grooves, and unstable droplet transport. This article presents a concept for adaptive precision lubrication in thread forming based on computational fluid dynamics (CFD)-supported flow analysis, experimental validation, and artificial intelligence (AI)-assisted optimization. The focus is on droplet size, spray jet geometry, nozzle position, ambient flow conditions, and their influence on wetting intensity. Preliminary simulation-based investigations indicate that data-driven optimization can help identify wetting deficiencies and support the development of future control strategies for resource-efficient lubricant application.

This study presents a method for early identification of AM-suitable components and circular design routes. A structured, data-lean questionnaire with AHP-based weighting, combined with rule-based R-strategy identification and an AM-specific feasibility gate, enables transparent screening in early development. Demonstrated on a structural bus component for both diesel and electric operation, the approach proposes a dominant R-strategy and integrates a prospective LCA-in-the-loop workflow, showing how early circularity signals and life-cycle feedback inform robust redesign decisions.