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.

Today’s methods for planning manual assembly processes have been developed for many decades. Besides technical advances, organisational innovations such as concurrent engineering have made a major contribution to both cost and quality of the final product. However, mass customisation and personalisation of products have imposed additional requirements. Planning should be performed for many different product variants while at the same time planning cost and time to market should be kept at a minimum level. The objective of this paper is to present a new approach for planning of manual assembly processes that goes beyond established industry practices, which has the potential to provide planning teams with tools to evaluate different assembly process scenarios faster, eliminating the need for physical prototypes. The approach is based on controlled natural language textual descriptions of assembly tasks, automatic generation human motions from the textual descriptions based on data-driven, statistical motion models and interactive process optimisation. The proposed method is evaluated in a pilot case from automotive industry regarding ease of use, extensibility, adaptability, motion realism and motion diversity. Results suggest that while the approach addresses practical needs of production planning, technical challenges remain in order to make it ready to be implemented into digital planning environments.

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.

Manufacturing companies are progressively applying digital manufacturing tools to respond to increased product complexity in shortened product lifecycles. The application results in a comprehensive documentation of the product emergence process. Furthermore at Daimler, a controlled natural language has recently been established, which enables automated analysis of natural language work task description texts. This work proposes a methodology, which enhances planning efficiency by automatically presenting a set of potentially suitable work plans for novel products. The presented work plans are reused from past planning activities. Assessment of work plan suitability is based on a statistical analysis that employs Methods-Time Measurement (MTM) data as well as work task descriptions in a controlled natural language (cnl). The proposed methodology is compared to a previously presented approach, in which text mining is used instead of a controlled natural language. The test comprises 104 work tasks of a Daimler assembly line. While result quality is only slightly improved for the cnl based approach, mapping results from product clusters to assembly sequences are simplified and analysis effort can be reduced if a cnl is already established. Future investigations should focus on investigations of applicability to different production and assembly domains