Strategic considerations and publications dealing with the future of industrial production are significantly influenced these days by the concept of “Industrie 4.0”. For this reason the field of measurement technology for industrial production must also tackle this concept when thinking about future trends and challenges in metrology. To this end, the Manufacturing Metrology Roadmap 2020 of the VDI/VDE Society for Measurement and Automatic Control (GMA) was published in 2011 (VDI/VDE-GMA, 2011; Imkamp et al., 2012). The content of this roadmap is reviewed and extended here, covering new developments in the field of the Industrie 4.0 concept and presented with expanded and updated content.

Microlens arrays (MLAs) have a variety of applications in, e.g., display systems, projection optics, and sensors. They can be manufactured by various fabrication methods. Among all, inkjet printing stands out because it offers a straightforward, versatile, and low‐cost fabrication route. However, extra manufacturing steps such as photolithography are so far involved to pre‐structure the substrate in order to improve the uniformity of the printed MLAs and achieve a high fill factor (FF). In this study, the fabrication of MLAs is reported on unstructured substrates by inkjet printing using an optimized UV‐curable ink on top of self‐assembled monolayers (SAMs). The latter allows for tuning the surface free energy and thus the aspect ratio of the microlenses. The high uniformity of the printed MLAs is demonstrated by the automatic quantitative evaluations, where the standard deviations of the radii are below 2.5% and of the sag heights are less than 3.9%. An unprecedented FF of 88% amongst all inkjet‐printed MLAs on unstructured substrates is achieved. Digitally controlled large area fabrication is demonstrated, both, on rigid as well as on flexible substrates, opening a pathway for customized microoptics by additive manufacturing.

Deflectometry is a full-field gradient technique that lends itself very well to testing specular surfaces. It uses the geometry of specular reflection to determine the gradient of the surface under inspection. In consequence, a necessary precondition to apply deflectometry is the presence of at least partially specular reflection. Surfaces with larger roughness have increasingly diffuse reflection characteristics, making them inaccessible to usual deflectometry. However, many industrially relevant surfaces exist that change their reflection characteristic during production and processing. An example is metal sheets that are used as car body parts. Whereas the molded but otherwise raw metal sheets show a mostly diffuse reflection without sufficient specular reflection, the final car body panels have a high specular reflectance due to the lacquering. In consequence, it would be advantageous to apply the same inspection approach both for the raw material and for the final product. To solve this challenge, specular reflection from rough surfaces can be achieved using light with a larger wavelength, as the specular reflectivity of a surface depends on the ratio of the surface roughness and the wavelength of the light applied. Wavelengths in the thermal infrared range create enough specular reflection to apply deflectometry on many visually rough metal surfaces. This contribution presents the principles of thermal deflectometry, its special challenges, and illustrates its use with examples from the inspection of industrially produced surfaces.

The technology of hairpin welding, which is frequently used in the automotive industry, entails high-quality requirements in the welding process. It can be difficult to trace the defect back to the affected weld if a non-functioning stator is detected during the final inspection. Often, a visual assessment of a cooled weld seam does not provide any information about its strength. However, based on the behavior during welding, especially about spattering, conclusions can be made about the quality of the weld. In addition, spatter on the component can have serious consequences. In this paper, we present in-process monitoring of laser-based hairpin welding. Using an in-process image analyzed by a neural network, we present a spatter detection method that allows conclusions to be drawn about the quality of the weld. In this way, faults caused by spattering can be detected at an early stage and the affected components sorted out. The implementation is based on a small data set and under consideration of a fast process time on hardware with limited computing power. With a network architecture that uses dilated convolutions, we obtain a large receptive field and can therefore consider feature interrelation in the image. As a result, we obtain a pixel-wise classifier, which allows us to infer the spatter areas directly on the production lines.

Robust people tracking in an indoor or outdoor environment is an important feature for fitness applications, surveillance systems for security, and assisted living for the elderly. Camera-based systems offer the state of the art approach but raise privacy concerns and consume a significant amount of power for the sensor and the processing. On the other hand, modern millimeter-wave Radar sensors provide a low-cost and low-power alternative with very good resolution in range, radial velocity and angle of arrival. In this work, we use two popular algorithms, for tracking people using Radar, and compare them with our novel method. We show that our method is robust in certain corner cases and is suitable for scenarios with many users in close proximity. In addition, we present two consumer applications that are possible with a Radar system, multi-user macro gesture recognition and hand tracking. We believe that the combination of above methods will open new gateways for consumer applications.