Due to the change from mass production to mass personalized production and the resulting intrinsic product flexibility, the automotive industry, among others, is looking for cost-efficient and resource-saving production methods to combining global just-in-time production. In addition to geometric manufacturing flexibility, additive manufacturing offers a resource-saving application for rapid prototyping and small series in predevelopment. In this study, the FDM process is utilized to manufacture the tooling to draw a small series of sheet metal parts in combination with the rubber pad forming process. Therefore, a variety of common AM polymer materials (PETG, PLA, and ABS) is compared in compression tests, from which PLA is selected to be applied as sheet metal forming die. For the rubber pad forming process, relevant processing parameters, i.e., press force and rubber cushion hardness, are studied with respect to forming depth. The product batch is examined by optical evaluation using a metrological system. The scans of the tool and sheet metal parts confirm the mechanical integrity of the additively manufactured die from polymer and thus the suitability of this approach for small series in sheet metal drawing processes, e.g., for automotive applications.

In human–robot collaboration (HRC), human motion capture can be considered an enabler for switching autonomy between humans and robots to create efficient and safe operations. For this purpose, wearable motion tracking systems such as IMU and lighthouse-based systems have been used to transfer human joint motions into robot controller models. Due to reasons such as global positioning, drift, and occlusion, in some situations, e.g., HRC, both systems have been combined. However, it is still not clear if the motion quality (e.g., smoothness, naturalness, and spatial accuracy) is sufficient when the human operator is in the loop. This article presents a novel approach for measuring human motion quality and accuracy in HRC. The human motion capture has been implemented in a laboratory environment with a repetition of forty-cycle operations. Human motion, specifically of the wrist, is guided by the robot tool center point (TCP), which is predefined for generating circular and square motions. Compared to the robot TCP motion considered baseline, the hand wrist motion deviates up to 3 cm. The approach is valuable for understanding the quality of human motion behaviors and can be scaled up for various applications involving human and robot shared workplaces.

Human-robot collaboration combines the extended capabilities of humans and robots to create a more inclusive and human-centered production system in the future. However, human safety is the primary concern for manufacturing industries. Therefore, real-time motion tracking is necessary to identify if the human worker body parts enter the restricted working space solely dedicated to the robot. Tracking these motions using decentralized and different tracking systems requires a generic model controller and consistent motion exchanging formats. In this work, our task is to investigate a concept for a unified real-time motion tracking for human-robot collaboration. In this regard, a low cost and game-based motion tracking system, e.g., HTC Vive, is utilized to capture human motion by mapping into a digital human model in the Unity3D environment. In this context, the human model is described using a biomechanical model that comprises joint segments defined by position and orientation. Concerning robot motion tracking, a unified robot description format is used to describe the kinematic trees. Finally, a concept of assembly operation that involves snap joining is simulated to analyze the performance of the system in real-time capability. The distribution of joint variables in spatial-space and time-space is analyzed. The results suggest that real-time tracking in human-robot collaborative assembly environments can be considered to maximize the safety of the human worker. However, the accuracy and reliability of the system regarding system disturbances need to be justified.

In recent years, smart workplaces that adapt to human activity and motion behavior have been proposed for cognitive production systems. In this respect, methods for identifying the feelings and activities of human workers are being investigated to improve the cognitive capability of smart machines such as robots in shared working spaces. Recognizing human activities and predicting the possible next sequence of operations may simplify robot programming and improve collaboration efficiency. However, human activity recognition still requires explainable models that are versatile, robust, and interpretative. Therefore, recognizing and analyzing human action details using continuous probability density estimates in different workplace layouts is essential. Three scenarios are considered: a standalone, a one-piece flow U-form, and a human-robot hybrid workplace. This work presents a novel approach to human activity recognition based on a probabilistic spatial partition (HAROPP). Its performance is compared to the geometric-bounded activity recognition method. Results show that spatial partitions based on probabilistic density contain 20% fewer data frames and 10% more spatial areas than the geometric bounding box. The approach, on average, detects human activities correctly for 81% of the cases for a pre-known workplace layout. HAROPP has scalability and applicability potential for cognitive workplaces with a digital twin in the loop for pushing the cognitive capabilities of machine systems and realizing human-centered environments.

Many manmade machines and mechanisms including robots function based on the concept of nature-inspired design so that they can perform their intended duties by mimicking the working mechanisms of animals and insects. Accordingly, walking machines (robots) use wheels and tracks to cross rough...