In this paper, we assess the detection of the graspable edge of parts printed with powder-based methods, particularly HP Multi Jet Fusion (MJF). When extracted from the printer, the parts remain coated with unprocessed powder, and their unconventional shapes and white color make it challenging to identify pre-existing grasp points compared to everyday items. We introduce a pipeline that combines the YOLOv7 Convolutional Neural Network (CNN) with over-segmentation to detect a graspable edge on such printed parts. The Point Cloud Data (PCD) captured from a depth camera is used to validate the graspable edge, employing methods such as RANSAC and nearest neighbor search. Our method demonstrates the feasibility of edge pose detection for powder-based parts in an industrial processing line, where the excess unprocessed powder is removed automatically. The purpose of finding a graspable edge is to facilitate material handling and avoid obscuring the part while the gripper holds it during the inspection process. An image dataset from 12 different powder-printed parts was collected to train the CNN model. The dataset and scripts for this pipeline are accessible at https://github.com/thd-research/edge-grasp-pose-detection.

In this paper, the design of a lower limb active exoskeleton for rehabilitation purposes is discussed. Active exoskeletons provide the additional required force for motion and gait correction, especially for patients who have suffered from limb impairment. The development was carried out taking into account sensor input for the control system. A Finite-State Machine (FSM) enables the different motions which rely on the data from the torque and acceleration sensors. To replicate the anatomical knee rotation, a four-bar linkage was modelled and integrated in the actuator drive. For this project, a prototype was created using Additive Manufacturing Processes (AMP). The prototype is part of the ‘ForCEs' project at Deggendorf Institute of Technology (DIT) and was tested at an experimental stage for an initial set of results.