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The utilization of lasers in dentistry expands greatly in recent years. For instance, fs-lasers are effective for both drilling and caries prevention, while cw-lasers are useful for adhesive hardening. A cutting-edge application of lasers in dentistry is the debonding of veneers. While there are pre-existing tools for this purpose, there is still potential for improvement. Initial efforts to investigate laser assisted debonding mechanisms with measurements of the optical and mechanical properties of teeth and prosthetic ceramics are presented. Preliminary tests conducted with a laser system used for debonding that is commercially available showed differences in the output power set at the systems console to that at specified distances from the handpiece. Furthermore, the optical properties of the samples (human teeth and ceramics) were characterised. The optical properties of the ceramics should closely resemble those of teeth in terms of look and feel, but they also influence the laser assisted debonding technique and thus must be taken into account. In addition first attempts were performed to investigate the mechanical properties of the samples by means of pump-probe-elastography under a microscope. By analyzing the sample surface up to 20 ns after a fs-laser pulse impact, pressure and shock waves could be detected, which can be utilized to determine the elastic constants of specific materials. Together such investigations are needed to shape the basis for a purely optical approach of debonding of veneers utilizing acoustic waves.
Power plant operators increasingly rely on predictive models to diagnose and monitor their systems. Data-driven prediction models are generally simple and can have high precision, making them superior to physics-based or knowledge-based models, especially for complex systems like thermal power plants. However, the accuracy of data-driven predictions depends on (1) the quality of the dataset, (2) a suitable selection of sensor signals, and (3) an appropriate selection of the training period. In some instances, redundancies and irrelevant sensors may even reduce the prediction quality.
We investigate ideal configurations for predicting the live steam production of a solid fuel-burning thermal power plant in the pulp and paper industry for different modes of operation. To this end, we benchmark four machine learning algorithms on two feature sets and two training sets to predict steam production. Our results indicate that with the best possible configuration, a coefficient of determination of R^2 = 0.95 and a mean absolute error of MAE=1.2 t/h with an average steam production of 35.1 t/h is reached. On average, using a dynamic dataset for training lowers MAE by 32% compared to a static dataset for training. A feature set based on expert knowledge lowers MAE by an additional 32 %, compared to a simple feature set representing the fuel inputs. We can conclude that based on the static training set and the basic feature set, machine learning algorithms can identify long-term changes. When using a dynamic dataset the performance parameters of thermal power plants are predicted with high accuracy and allow for detecting short-term problems.
Parametric anti-resonance is a phenomenon that occurs in systems with at least two degrees of freedom; this can be achieved by periodically exciting some parameters of the system. The effect of this properly tuned periodicity is to increase the dissipation in the system, which leads to a raising in the effective damping of vibrations. This contribution presents the design of an open-loop control to reduce the settling time using the anti-resonance concept. The control signal consists of a quasi-periodic signal capable of transferring the system’s oscillations from one mode to another mode of the system. The general averaging technique is used to characterize the dynamics, particularly the so-called slow dynamics of motion. With this analysis, the control signal is designed for the potential application of a microelectromechanical sensor arrangement; for this specific example, up to 96.8% reduction of settling time is achieved.
By a simple femtosecond laser process, we fabricated metal-oxide/gold composite films for electrical and optical gas sensors. We designed a dripple wavelength AWG-spectrometer, matched to the plasma absorption wavelength region of the composite films. H2/CO absorptions fit well with the AWG design for multi gas detection sensor arrays
Grey Box models provide an important approach for control analysis in the Heating, Ventilation and Air Conditioning (HVAC) sector. Grey Box models consist of physical models where parameters are estimated from data. Due to the vast amount of component models that can be found in literature, the question arises, which component models perform best on a given system or dataset? This question is investigated systematically using a test case system with real operational data. The test case system consists of a HVAC system containing an energy recovery unit (ER), a heating coil (HC) and a cooling coil (CC). For each component, several suitable model variants from the literature are adapted appropriately and implemented. Four model variants are implemented for the ER and five model variants each for the HC and CC. Further, three global optimization algorithms and four local optimization algorithms to solve the nonlinear least squares system identification are implemented, leading to a total of 700 combinations. The comparison of all variants shows that the global optimization algorithms do not provide significantly better solutions. Their runtimes are significantly higher. Analysis of the models shows a dependency of the model accuracy on the number of total parameters.
The main aims of this work are the validation of the developed process of gluing a single-mode optical fiber array with a photonic chip and the selection of a more suitable adhesive from the two adhesives being compared. An active alignment system was used for adjusting the two optical fiber arrays to a photonics chip. The gluing was done by two compared UV curable adhesives applied in the optical path. The insertion losses of glued coupling were measured and investigated at two discrete wavelengths 1310 nm and 1550 nm during temperature testing in the climatic chamber according to Telcordia GR_1209_Corei04 [3]. The measurement, investigation, and comparison of insertion losses of the glued coupling at the spectral band from 1530 nm to 1570 nm were done immediately after gluing process and after three temperature cycles in the climatic chamber with one month delay.
In 2021, a prominent Austria dairy producer suffered from an IT attack and was completely paralysed. Without clearly defined mitigation measures in place, major disruptions were caused alongside the whole supply chain, including logistics service providers, governmental food safety bodies, as well as retailers (i.e., supermarkets and convenience stores). In this paper, we ask the question how digitisation and digital transformation impact IT security, especially when considering the complex company ecosystems of food production and food supply chains in Austria. The problem statement stems from a gap in knowledge of key differences in approaches towards IT security, resilience, risk management and especially business interfaces between food suppliers, supermarkets, distributors, logistics and other service providers. In order to answer related research questions, firstly, the authors conduct literature research, and highlight common guidelines and standardisation as well as look at state-based recommendations for critical infrastructure. In a second step, the paper describes a quantitative and qualitative survey with Austrian food companies (producers and retailers) which is described in detail in the paper. A description of recommended measures for the industry, further steps, as well as an outlook conclude the paper.
Im vorliegenden Paper wird ein Vergleich zwischen Produktions-und Simulationsdaten präsentiert welches im Rahmen einer größeren Initiative zur Verwendung von Shopfloor Daten bei einem Projektpartner in der Automobilindustrie umgesetzt wurde. In diesem Projekt wurden die Daten die während der Füllbildsimulation entstehen mit den Daten aus der finalen Werkzeugabnahme verglichen um zu analysieren, wie genau diese miteinander über einstimmen. Je besser die Simulation ist, desto schneller kann der gesamte Werkzeugentwicklungsprozess abgewickelt werden, welcher als Kernprozess massives Einsparungspotenzial und damit Wettbewerbsvorteil mit sich bringt.
The usage of data gathered for Industry 4.0 and smart factory scenarios continues to be a problem for companies of all sizes. This is often the case because they aim to start with complicated and time-intensive Machine Learning scenarios. This work evaluates the Process Capability Analysis (PCA) as a pragmatic, easy and quick way of leveraging the gathered machine data from the production process. The area of application considered is injection molding. After describing all the required domain knowledge, the paper presents an approach for a continuous analysis of all parts produced. Applying PCA results in multiple key performance indicators that allow for fast and comprehensible process monitoring. The corresponding visualizations provide the quality department with a tool to efficiently choose where and when quality checks need to be performed. The presented case study indicates the benefit of analyzing whole process data instead of considering only selected production samples. The use of machine data enables additional insights to be drawn about process stability and the associated product quality.