University of Siegen
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Publication Access status: Open Access , Advances in biomimetic photoelectrocatalytic reduction of carbon dioxide(2022)Emerging photoelectrocatalysis (PEC) systems synergize the advantages of electrocatalysis (EC) and photocatalysis (PC) and are considered a green and efficient approach to CO2 conversion. However, improving the selectivity and conversion rate remains a major challenge. Strategies mimicking natural photosynthesis provide a prospective way to convert CO2 with high efficiency. Herein, several typical strategies are described for constructing biomimetic photoelectric functional interfaces; such interfaces include metal cocatalysts/semiconductors, small molecules/semiconductors, molecular catalysts/semiconductors, MOFs/semiconductors, and microorganisms/semiconductors. The biomimetic PEC interface must have enhanced CO2 adsorption capacity, preferentially activate CO2, and have an efficient conversion ability; with these properties, it can activate C=O bonds effectively and promote electron transfer and C-C coupling to convert CO2 to single-carbon or multicarbon products. Interfacial electron transfer and proton coupling on the biomimetic PEC interface are also discussed to clarify the mechanism of CO2 reduction. Finally, the existing challenges and perspectives for biomimetic photoelectrocatalytic CO2 reduction are presented.Source Type:Article6 5 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Open Access , Novel methodologies for multiaxial strain measurements with piezoresistive films based on graphene nanoplateletsMany recent investigations in the context of graphene nanoplatelets (GNPs) coatings report surface strain measurements by using piezoresistive sensing capabilities. An often underestimated problem is that the strain field is unknown and the principal strain components as well as their orientations must be determined. Herein, GNP films subjected to multiaxial strain are examined. Experimental results show that although the sensitivity to longitudinal strain is the highest, the ratio between transverse and longitudinal sensitivity exceeds 0.5. The sensitivity to shear strain is much lower. A model assisted study of a random network provides additional guidelines for the different electromechanical sensitivities. In practice, the GNP film is usually subjected to different strains simultaneously so that the multiaxial strain measurement becomes difficult. Therefore, two novel approaches for sensing plane strain components with circular GNP films are developed and successfully verified in experiments. The numerical approach is called strain-differential electrical impedance tomography (SD-EIT), where the proposed piezoresistive model elementwise in a finite element model is implemented and the strain components of a strain rosette are reconstructed. Moreover, an analytical approach is derived from SD-EIT and exhibits further the opportunity to detect anomalies within the piezoresistive sensing behavior of GNP films.Source Type:Article1 13 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Open Access , On the low temperature creep controlling mechanism in a high strength spring steelDespite of the well-known fact that high strength steels are exhibiting low temperature creep deformation, its rate controlling mechanism is yet to be understood. The strain hardening theory and the exhaustion creep model were proposed almost seven decades ago to unravel the low temperature creep mechanism in a single phase homogeneous isotropic material. However, their applicability to low temperature creep deformation in a technical material like a modern high strength steel is still a matter of investigation owing to their nature of multi-phase, in-homogeneous, and an-isotropic behavior. The authors have grabbed this chance to experimentally validate the exhaustion creep model based on low temperature creep tests of the SAE 9254 spring steel.Source Type:Article297 173 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Open Access , Characterization of 3D-printed PLA parts with different raster orientations and printing speedsFabrication based on additive manufacturing (AM) process from a three-dimensional (3D) model has received significant attention in the past few years. Although 3D printing was introduced for production of prototypes, it has been currently used for fabrication of end-use products. Therefore, the mechanical behavior and strength of additively manufactured parts has become of significant importance. 3D printing has been affected by different parameters during preparation, printing, and post-printing processes, which have influence on quality and behavior of the additively manufactured components. This paper discusses the effects of two printing parameters on the mechanical behavior of additively manufactured components. In detail, polylactic acid material was used to print test coupons based on fused deposition modeling process. The specimens with five different raster orientations were printed with different printing speeds. Later, a series of tensile tests was performed under static loading conditions. Based on the results, strength and stiffness of the examined specimens have been determined. Moreover, dependency of the strength and elastic modulus of 3D-printed parts on the raster orientation has been documented. In the current study, fractured specimens were visually investigated by a free-angle observation system. The experimental findings can be used for the development of computational models and next design of structural components.Source Type:Article3 10 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Open Access , A WaveNet-based fully stochastic dynamic stall modelAccurate modeling of the dynamic stall remains a challenge for the design and construction of turbine blades and helicopter rotors. At the same time, wind turbines, for instance, are becoming steadily larger, further increasing the demands on their structure and necessitating even more detailed modeling of the forces at hand. The primarily used (semi-)empirical models today have a long research history and are invariably based on phase-averaged data from oscillating blade pitch experiments. However, much potential for more accurate modeling of uncertainties and force peaks is wasted here, since averaging blurs many features of the response signals. Even computational fluid dynamics can help little in this regard, since the Reynolds-averaged Navier–Stokes equations used in practice cannot account for cycle variations, and scale-resolving models require extremely large amounts of computational resources. This paper presents an approach for a fully stochastic machine learning model that can nevertheless simulate these critical properties. Aerodynamic coefficients are compared with experimental data for different test cases. It is shown that synthetic force profiles which cannot be distinguished from the experimental data visually and are very close to them in the frequency spectrum can be generated. Additionally, attention is drawn to the difficulty of evaluating such a model, as traditional error metrics are of little use. A combination of dynamic time warping and the Earth mover's distance provides a robust solution for this problem.Source Type:Article345 102 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Open Access , Influence of residual stresses on the crack initiation and short crack propagation in a martensitic spring steelThe crack initiation and short crack propagation in a martensitic spring steel were investigated by means of in-situ fatigue testing. Shot peened samples as well as untreated samples were exposed to uniaxial alternating stress to analyze the impact of compressive residual stresses. The early fatigue damage started in both sample conditions with the formation of slip bands, which subsequently served as crack initiation sites. Most of the slip bands and, correspondingly, most of the short fatigue cracks initiated at or close to prior austenite grain boundaries. The observed crack density of the emerging network of short cracks increased with the number of cycles and with increasing applied stress amplitudes. Furthermore, the prior austenite grain boundaries acted as obstacles to short crack propagation in both sample conditions. Compressive residual stresses enhanced the fatigue strength, and it is assumed that this beneficial effect was due to a delayed transition from short crack propagation to long crack propagation and a shift of the crack initiation site from the sample surface to the sample interior.Source Type:Article2 18 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Open Access , Machine learning-based prediction of missing components for assembly - a case study at an engineer-to-order manufacturerFor manufacturing companies, especially for machine and plant manufacturers, the assembly of products in time has an essential impact on meeting delivery dates. Often missing individual components lead to a delayed assembly start, hereinafter referred to as assembly start delayers. Identifying the assembly start delayers early in the production process can help to set countermeasures to meet the required delivery dates. In order to achieve this, we set up 24 prediction models on four different levels of detail utilizing different machine learning-algorithms - six prediction models on every level of detail - and applying a case-based research approach in order to identify the model with the highest model quality. The modeling approach on the four levels of detail is different. The models on the coarsest level of detail predict assembly start delayers utilizing a classification approach. The models on the three finer levels of detail predict assembly start delayers via a regression of different lead times and subsequent postprocessing operations to identify the assembly start delayers. After training of the 24 prediction models based on a real data set of a machine and plant manufacturer and evaluating their model quality, the classification model utilizing a Gradient Boosting classifier showed best results. Thus, performing a binary classification to identify assembly start delayers was the best modelling approach. With the achieved results, our study is a first approach to predict assembly start delayers and gives insights in the performance of different modeling approaches in the area of a production planning and control.Source Type:Article5 12 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Open Access , The impact of additive manufacturing on the mechanical properties of a stainless precipitation hardening steelA stainless precipitation hardening steel has been qualified for powder bed fusion of metals using a laser beam. The mechanical and microstructural properties of additively manufactured samples have been thoroughly investigated and compared to samples of the very same material made by means of ingot casting. Additive manufacturing is feasible and can deliver superior mechanical properties. Additionally, the surface quality will be improved by means of wet abrasive jet machining, and its impact on the residual stresses has been shown.Source Type:Article3 15 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Open Access , Bibliometric study on the use of machine learning as resolution technique for Facility Layout ProblemsFacility Layout Problems (FLP) are concerned with finding efficient factory layouts. Numerous resolution approaches are known in literature for layout optimization. Among those, intelligent approaches are less researched than solutions from exact or approximating approaches. The recent surge of research interest in Artificial Intelligence, and specifically Machine Learning (ML) techniques, presages an increase of such techniques' usage in FLP. However, previous reviews on FLP research induce that, to date, this trend has not yet emerged. Utilizing a systematic literature review coupled with a k-Means based clustering algorithm, we analyzed 25 relevant publication full-texts from an original sample of 1,425 papers. Our findings corroborate the statement that ML techniques have attracted substantially less research interest than most other resolution approaches. While a few papers used Unsupervised Learning algorithms directly as a solution to the FLP, Supervised and Reinforcement Learning were found to be practically irrelevant. ML usage was significantly higher in FLP-adjacent planning tasks such as group technology. Drawing from experiences with other NP-hard combinatorial optimization problems in manufacturing research, we conclude that Reinforcement Learning is most promising to bridge the evident gap between FLP and ML research. Our study further contributes to FLP research by extending established classification frameworks.Source Type:Article1 8 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Open Access , A clustering approach for topic filtering within systematic literature reviewsWithin a systematic literature review (SLR), researchers are confronted with vast amounts of articles from scientific databases, which have to be manually evaluated regarding their relevance for a certain field of observation. The evaluation and filtering phase of prevalent SLR methodologies is therefore time consuming and hardly expressible to the intended audience. The proposed method applies natural language processing (NLP) on article meta data and a k-means clustering algorithm to automatically convert large article corpora into a distribution of focal topics. This allows efficient filtering as well as objectifying the process through the discussion of the clustering results. Beyond that, it allows to quickly identify scientific communities and therefore provides an iterative perspective for the so far linear SLR methodology. • NLP and k-means clustering to filter large article corpora during systematic literature reviews. • Automated clustering allows filtering very efficiently as well as effectively compared to manual selection. • Presentation and discussion of the clustering results helps to objectify the nontransparent filtering step in systematic literature reviews.Source Type:Article2 4
