Digital sensing of production areas and subsequent automated analysis of the captured data can significantly improve the efficiency of factory planning processes. The segmentation of class-specific regions in the captured data using deep neural networks shows great potential for such analysis. However, previous approaches are based on supervised learning and, therefore, require comprehensive, annotated datasets that are costly to generate. The use of large foundation models such as DINOv2 or SAM, combined with few-shot learning approaches, could reduce these efforts in the future. In this work, we first present a method that implements such a combination and subsequently evaluate its performance on an exemplary dataset. The obtained results confirm the method’s potential, especially in scenarios with limited availability of labeled data.

Remanufacturing describes a process where worn products are restored to like-new condition and it offers vast ecological and economic potentials. A key step is the quality inspection of disassembled components, which is mostly done manually due to the high variety of parts and defect patterns. Deep neural networks show great potential to automate such visual inspection tasks but struggle to generalize to new product variants, components, or defect patterns. To tackle this challenge, we propose a novel image dataset depicting typical gearbox components in good and defective condition from two automotive transmissions. Depending on the train-test split of the data, different distribution shifts are generated to benchmark the generalization ability of a classification model. We evaluate different models using the dataset and propose a contrastive regularization loss to enhance model robustness. The results obtained demonstrate the ability of the loss to improve generalisation to unseen types of components.

Vacuum grippers are widely used for handling tasks in manufacturing, due to their simple design and robustness. Unfortunately, their dimensioning often results in oversizing due to limited knowledge about the suction cups, stemming from insufficient insight into how they perform in practice. Recent studies have shown the potential for energy savings through precise dimensioning, which allows for more sustainable production. However, research in this field has been dominated by investigating the behavior of single suction cups, although industrial applications typically involve gripper systems with multiple suction cups. When multiple cups are considered, optimization algorithms can be applied to improve the positioning of individual suction cups to ensure more stable grasps. Here, heuristics decide which parameters must be varied to achieve an optimized suction cup distribution. Subsequently, there is a lack of systematic investigations on the influence of different positions of the suction cups. In addition, the influence of the handling task, including the robot’s acceleration, has received limited attention. To address this gap, we conducted experiments to evaluate the influence of key design parameters. These include the offset between the workpiece’s center of gravity and the point of force application, as well as varying suction cup sizes and their geometric arrangement.

Today’s production systems are characterized by a high variety of products and small batch sizes offering potential for industrial robots, due to their flexibility. For economical automation of handling processes, it is also necessary to increase the flexibility of the vacuum gripper used. However, the explorative design of the latter is time-intensive and requires experts. This paper presents a concept to automate the design of vacuum grippers and increase their flexibility. For this purpose, the selection of gripping points are not only evaluated for individual components but also across variants to reduce the number of vacuum grippers required.