Volume 89, Issue 11

Zusammenfassung Zur Verwendung von Bildaufnahmen zu messtechnischen Zwecken werden verzeichnungsfreie Bildaufnahmen benötigt. Besteht durch die Rahmenbedingungen des Optikaufbaus keine Möglichkeit verzeichnungsarme Optiken zu verwenden, können verzeichnete Bildaufnahmen nachträglich durch Software-Algorithmen korrigiert werden. In dieser Publikation wird ein solcher Vorgang zur Korrektur radialer Verzeichnungseffekte für einen 3D-Lichtschnittsensor dargestellt. Der Kalibriervorgang wurde für einen 3D-Sensor entwickelt, findet jedoch mit Bildern in 2D statt. Die Auswahl passender Kalibrierobjekte sowie die Beschreibung der Verzeichnungseffekte durch eine entsprechende Korrekturfunktion basierend auf einer Taylor-Reihe wird in der Publikation dargestellt. Der Korrekturvorgang findet durch einen Optimierungsalgorithmus statt und wird zur Minimierung einer Gütefunktion genutzt, welche verbleibende Verzeichnungsfehler beschreibt. Der dargestellte Vorgang beinhaltet eine Verzeichnungskorrektur mit einer Genauigkeit im zweistelligen Mikrometerbereich für ein Weitwinkelobjektiv.

In view of the universal applicability of color information index and the ability of spectrum index to measure small roughness, this paper studies the influence of machined surface texture on roughness machine-vision measurement. Based on the micro-topography of different types of texture surface, the correlations between the average color difference, five typical spectral indices and the surface roughness parameters of two types of texture representative workpiece milling and grinding are focused on in the discussion. On this basis, the influences of the angle of light source and camera relative to the surface normal of the sample and the sample surface texture direction on roughness measurement are further discussed, and the reasons for the difference in index to characterize different process roughness are analyzed from the mechanism. The experimental results show that the different surface textures have different effects on light scattering, which in turn affects the sensitivity of the index to the roughness parameters.

Design and operational performance of table-top measurement apparatus is presented towards direct Planck constant traceable high accuracy and high precision small forces and optical power measurements within the SI unit system. Electromagnetic force compensation weighing balances, highly reflective mirrors and high-energy pulsed laser unit (static average power 20 W) are tailored together with a specially developed opto-electro-mechanical measurement infrastructure for cross-mapping the scale-systems of two different precision small force measurement methods. One of these methods obtains the force measurements by a state-of-the-art classical kinematic system employing the partial use of Kibble balance principle in the range of 10 nN  to  4000 nN to be compared with forces generated due to quantum-mechanical effect namely the transfer of the momentum of photons from a macroscopic object. Detailed overview of the adapted measurement methodology, the static and the limits of dynamic measurement, the metrological traceability routes of the measurement parameters, quantities and their measurement uncertainties, parametric estimation of up (down)-scaling perspectives of the measurements are presented with respect to the state-of-the-art measurement principles and standard procedures within the newly redefined International System of Units (SI).

Over the years, it has been proven that all machines that run continuously or even for a short period of time experiences breakdowns, reduced efficiency and damage to the parts. Considering one such manufacturing process is welding process where strip welding is studied for condition monitoring and prediction of online quality of the weld. The proposed embodiment discusses a predictive condition monitoring of a welding process, using acoustic sensors and processing their data. Converting the processed data to a predictive model, comparing different Machine Learning (ML) algorithms like Decision Tree (DT), Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) based on the validation and training results, selecting the suitable algorithm for online or real-time weld quality prediction is the objective of the proposed embodiment. According to the research, effectively executing the proposed solution can improve its life, efficiency, and utilization. The training accuracy is observed to be 97 % for DT, 50 % for ANN and 67 % for CNN. DT, on the other hand, has a validation accuracy of 94.7 %, ANN has 47.65 % and CNN has 65.8 %. DT produced the best results in the study, initially DT prediction model is used in offline testing of the new data-set for individual weld class and the system produced 97 % accuracy. Furthermore the DT prediction model is implemented in online weld quality prediction in three classes: Bad weld, OK weld and Good weld where the system produced 90 % accuracy in real-time prediction.

Flake, pit and through-hole defects are the three common types of defects in oil pipelines. In practical testing applications, it is necessary to clarify the magnetic leakage characteristics of different defects and set reasonable detection points to distinguish different types of defects accurately and quantify the degree of each defect precisely. In this paper, the leakage magnetic field of flaky, pitted and through-hole tube wall defect is analyzed. Using three-dimensional finite element simulation, the spatial stereo model of leakage magnetic field is inverted by extracting the data of characteristic nodes. The distribution of the leakage magnetic field in space, for the radial and circumferential components is obtained. On this basis, the effect on the main components of magnetic leakage field at different locations in space is determined, and the best of placing a potential sensor for detecting three kinds of defects in space are derived. This provides a basis for determining sensor locations in magnetic leakage detection devices, inverting defect models from multi-sensor data and accurately quantifying the degree of defects.

In the context of the fourth industrial revolution, condition monitoring and predictive maintenance of machines are becoming increasingly important, as unplanned machine failures can lead to high costs for the operator. Many of these machine failures can be traced back to bearing damage, which must be prevented. In order to be able to make reliable statements about the remaining service life of the bearings, a condition monitoring system is required. For the development of these systems, bearings with a defined level of damage are needed. Since there are not enough reproducible methods for bearing damage so far, the defined generation of reproducible, local bearing damage is researched by using laser technology. The damage caused is checked and evaluated by means of vibration analysis and white light interferometry. The results confirm a reproducibility and scalability of the bearing damages by means of laser technology. The damaged bearings can be used for research-based development as well as in industrial implementations of condition monitoring systems.