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A reinforcement of concrete structures is needed because of the low tensile strength of concrete. Corrosion of the usually used steel reinforcments cause issues during time. Therfore, alternative reinforcements produced from non-corosive materials - like polymer fibers - become more interesting. A polymer fiber has to reach high mechanical properties and a good bonding ability to concrete. Both properties can be influenced by the selection of polymer and the used production process. Two polymers, polypropylene and aliphatic polyketone are tested within this study. First mono-material fibers of each material are produced and tested, later the materials are combined in a core-shell fiber. All fibers are produced by standard extrusion or coextrusion and a later drawing process. The mechanical properties are determined by tensile tests. The calculation of interfacial shear strength from single fiber pull-out tests allows an evaluation of the bonding ability. Additionally, fiber surface before and after pull-out is examined using reflected light microscopy. Contact angle measurements are done to evaluate possible influences of the surface energy and polarity. Density measurements are used to compare weight potential of the different mono-material and core-shell fibers. The results show good mechanical properties for all fiber materials. The interfacial shear strength is ≈ 2-3 times higher for fibers with polyketone compared to the ones with polypropylene at the fibers surface, which can be explained by higher surface energy and polarity of the polyketone compared to polypropylene and different surface deformation during pull-out. Lower densities are reached by fibers containing polypropylene.
Previous research on identifying plastic types and differentiating plastics from environmental material is promising by utilizing the specific fluorescence lifetime, but the evaluation still has to be automated. Therefore, an automated Gaussian analysis is developed for evaluating frequency-domain fluorescence lifetime images of plastics and environmental materials. Furthermore, we applied a “Multilayer Perceptron” and “Random Forest Classifier” to the data resulting from the Gaussian analysis of the frequency domain fluorescence lifetime imaging microscopy data. The classification results show high F1-scores, whereby the best “Multilayer Perceptron” and “Random Forest Classifier” achieved an F1-score of 90%. Thus, identifying and differentiating plastics and environmental materials is possible by applying a “Multilayer Perceptron” or “Random Forest Classifier” to the Gaussian-analyzed imaged fluorescence lifetime data.
Environmental pollution by plastics is an increasing problem. However, state-of-the-art methods have significant disadvantages in detecting and identifying plastics directly in an environmental matrix. In this study, we propose a blob detection algorithm in combination with a neural network for fast and automated identification of plastics and non-plastics in a single fluorescence lifetime image. Therefore an artificial environmental matrix is prepared that contains soil, grass, spruce and HDPE (high density polyethylene) particles. Several FD-FLIM (frequency domain fluorescence lifetime imaging microscopy) images are taken, and the detection algorithm and the neural network are applied. We successfully demonstrated the suitability of the thresholding algorithm and the binary classification of the HDPE particles directly in the environmental matrix.
The FD-FLIM technique has a high potential for automated post-consumer wood sorting. A problem of analysing post-consumer wood fluorescence properties is the uncertainty of the post-consumer wood category as the origin of the samples are unknown. In this study, the fluorescence properties of actual post-consumer wood is compared with prepared wood samples. The post-consumer wood samples display slightly different fluorescence intensities and lifetimes due to environmental influences and a higher sample diversity. For improved training of evaluation algorithms for post-consumer wood sorting, the prepared sample set should be extended or the post-consumer wood should be additionally analysed in the laboratory.
Development of a Neural Network for Automatic Classification of Post-Consumer Wood Using Rapid-FLIM
(2023)
The economic use of wood is a growing sector, not only because of the significant advantage of wood to retain CO 2 . It is crucial to increase the material recycling of wood in several lifecycles, but currently, there is no reliable post-consumer wood sorting technique in line. This research measures 365 post-consumer wood samples of classes A1-4 four times with the frequency domain fluorescence lifetime imaging microscopy (FD-FLIM) subset method rapid fluorescence lifetime imaging microscopy (Rapid-FLIM). The data is analyzed on their statistical features. Four neural networks based on Multilayer perceptron are then trained and tested with twelve statistical features extracted from the Rapid-FLIM images. The best model for this application contains the optimizer RMSprop, the activation function SELU and the loss function binary crossentropy. The best model of this structure could achieve a false positive ratio of 4.79 % over the ten folds.
The fast classification and identification of plastics presents a significant challenge. The study assesses the suitability of a Multilayer Perceptron to classify and identify commonly found plastic types using Rapid-FLIM, achieving an accuracy of 88.33%. @inproceedingsSchwarz:23, Increasing demand for wood products requires methods to determine its harvest origin and ensure sustainable and legal sourcing. In 15 out of 21 cases, the origin of Quercus robur was differentiable in FD-FLIM studies.
Investigation of tool wear of the trimming unit and resulting quality in the edgebanding process
(2023)
The edgebanding process, i.e. the covering of the raw edge of the laminated particleboard by means of decorative plastic or real wood veneer bands, is an important process step in the manufacture of furniture components. The quality of the final product is significantly influenced by this production step. The manufacturing process step within the edgebanding machine begins with the milling of the raw edge. The quality of the edgebanding process is determined by a large number of process input variables, such as the tool used and the cutting geometry, the type of laminate used for the wood-based material, adhesives, etc. The quality of the edgebanding process is also determined by the type of material processed. Particularly with regard to the materials, the increased use of recycled wood in the particleboard sector and different types of adhesives (with reduced formaldehyde emissions) results in increased demands on the milling process. Due to this developments, most of the earlier investigations (in the years 1990 - 2000) are not comparable with today’s general conditions. The mechanical and optical properties of the edge applied to the end product are decisively influenced by the quality of this joint. The milling technology used, as well as the tool used for this purpose and its condition, is a decisive influencing factor in the formation of quality. Tests on the indication of various influencing variables in the edgebanding process at the Rosenheim Technical University of Applied Sciences showed that in the case of chipboard milling, a decrease in the electrical power required for the cutting process can take place with increasing tool wear. It was also demonstrated that previous process steps in furniture production, such as panel dividing by means of sawing or milling/nesting, generally have a significantly different influence on the subsequent joining process during edge banding process. Furthermore, a direct correlation was determined between the mechanical properties of the end product and the condition of the tool used, which can be used as a measurable indicator. In addition to the effect of the tool condition on the mechanical properties of the edge banding, this also has an impact on the optical quality characteristics. Accordingly, it could be demonstrated here that an increase in the size and number of chippings takes place in the laminate of the board material to be processed.
The machining of furniture components requires a complete geometrical specification of dimensions with associated tolerances to define in the first step appropriate manufacturing processes and to carry out the design and setup. Furthermore, a verification of the defined specification after production is a next step to check the quality and related to the production equipment, to calculate process capability and effectiveness.
In 2011, the system of Geometrical Product Specification (GPS) has been published completely. The system is built for a distinct range of geometrical quality characteristics and consists of chains of standards referring to each other. With three elements of these chains, the product can be specified. With three additional elements, the verification of the product and the measuring system is possible. It does not contain the verification of the production processes, the process qualification.
The German standard DIN 68100 provides a frame for dimensioning and tolerancing lengths and also angles, parallelism and straightness for furniture components. It includes a method to consider swelling and shrinking of wood and wood-based materials. Apart from its benefit of specifying products in a supply chain, it is rarely used in the branch nowadays. DIN 68100 is currently far away from GPS.
To gain all benefits arising from GPS the branch needs a consolidated action. New symbols for drafts and an updated method for tolerancing with moisture consideration have to be developed as well as measuring dimensions of flexible work pieces, like most parts in the furniture industry. In GPS, the link between product verification and process specification is not that consistent. In other branches SPC and methods of process qualifications are established. At the moment the kitchen industry is establishing SPC or similar procedures. An action towards GPS can also accompany these efforts.
Due to a shift in the tolerance principles, the GPS system is hardly directly applicable to the furniture industry, e.g. DIN 68100. But there is a real threat that the system is called up accidentally and out of ignorance.
This paper sketches a comprehensive system of product specification, verification for furniture components and process qualification. It will share experience gained in the kitchen furniture industry and will add a theoretical analysis of a possible application of GPS-chains for important examples of quality characteristics. The activities are part of a process (VDI 3415-2) carried out in the working group 102 of the Society of German Engineers (VDI).
Due to the increasing customer demands, companies face the challenge of adapting their manufacturing facilities to produce batch size one parts. In this context, each component comes with specific customer requirements, significantly increasing the complexity of its manufacturing process. Traditional process planning done by employees is no longer feasible in the face of the growing complexity and diversity of batch size one parts. Therefore, there is a need for novel computer-aided process planning methods capable of autonomously learning and identifying the necessary machining features of these parts. We propose a configurable automated feature recognition framework tailored to batch size one parts to address this need. This framework comprises two main components. First, a customizable data generator is developed to create a dataset of computer-aided design models incorporating user-specific machining features. Second, a graph neural network is employed to learn these user-specific machining features by leveraging the graph structure represented by the vertices and edges in each computer-aided design model. We evaluate the effectiveness of our framework using a real-world example from the furniture industry. Specifically, we construct a dataset comprising over 7000 computer-aided design models of wooden boards using our framework's data generator. Each wooden board contains up to 30 randomly placed machining features for the application of connectors and fittings, randomly selected from 14 standard machining features commonly used in the furniture industry. An optimization algorithm adapts a graph neural network for classifying these specific machining features. The results demonstrate that our framework addresses the automated feature recognition problem without requiring data conversions, such as pixels or voxels, as other approaches often do. By introducing this configurable automated feature recognition framework, we solve the challenges posed by batch size one manufacturing. The framework's effectiveness and accuracy are demonstrated through extensive testing on the example of the furniture industry, showcasing its potential for enhancing manufacturing processes in various domains
In the whole field of woodworking, from carpentry to industry, new products are created and designed every day and manufacturers wonder if similar workpieces have already been produced in the company and if corresponding drawings, information, tools, jigs, etc. can be reused. Under certain circumstances, considerable duplication of work can be avoided and a more efficient production is possible. In order to find similar workpieces, it must be clarified what exactly is meant by similarity. The different fields of knowledge do not provide a clear definition. However, similarities are always used to compare objects. In woodworking, objects can be a wide variety of components and products, such as carcase sides, furniture fronts, fittings, tools or profile strips. The search for similar profiled elements – related to the potential, data structure and algorithms – is the focus of this paper. There is a wide range of different profiled elements, which are used constructively as well as decoratively, e.g. for baseboards, door frames, window frames or decorative moulding profiles. These profiles are manufactured in a multi-stage production process in which the workpieces are first machined with profiled tools before surface finishing takes place. Along this production chain, tools, machine settings and, in some cases, fixtures are used that are adapted to the respective profile and can be reused for the same or similar jobs. Inheritance of information offers the greatest potential of similarity search. Inheritance means that existing knowledge in the form of drawings, CNC programs, process parameters and setup specifications, etc., is transferred from existing profiles to a new profile. By inheriting existing information, for example, the effort required for process setting can be reduced by adopting spindle positions, positions of guides and stops, or feed and cutting speeds. However, this can only be done if the process knowledge and other technological information concerning a profile are sufficiently documented. This is the only way how all information is made available for the similarity search and inheritance, and also for setting the processes when the profile is manufactured once again. The profile data must therefore be stored in a meaningful structure without loss of information. Therefore, a holistic product data model was developed that combines the geometrical with the technological profile data. The developed model allows a combined search for geometrically and e.g. materially similar products – also with a weighted evaluation of the individual criteria. The research has shown that data modelling is the essential foundation for the implementation of modern AI algorithms to enable similarity search. In conclusion, the use of a search system for geometrically and technologically similar profiles represents a real innovation for wood processing companies. This paper presents a holistic approach for geometrical and technological similarity search presenting different algorithms
Das Force Concept Inventory ist ein international etabliertes Diagnoseinstrument für das Konzeptverständnis der Newtonschen Mechanik. Wir haben über einen Zeitraum von zehn Jahren von knapp 5000 Studierenden der Ingenieurwissenschaften an der TH Rosenheim zu Studienbeginn Testdaten erhoben. Die Auswertung mittels Item Response Curves erlaubt für jede Frage die Analyse sowohl der richtigen als auch aller falschen Antwortoptionen. Insbesondere lassen sich auf diese Weise Distraktoren identifizieren, die gängige Fehlvorstellungen widerspiegeln. Es zeigt sich, dass die am häufigsten gewählten Distraktoren auf der Annahme beruhen, jeder Bewegung müsse eine Kraft in Bewegungsrichtung zu Grunde liegen. Daten von Universitäten der USA zeigen ein vergleichbares Ergebnis. Die vorgestellte Analyse dient als Grundlage zur Verbesserung von Lehrsequenzen.
The variation of the contact impedance of adhesive conductive EMI shielding tapes under high temperature storage (HTS) of 110degC is investigated. The adhesive tapes under test are implemented as signal return paths in microstrip lines. Changes in the ground plane impedance by adhesive deterioration are analyzed by S-parameter measurements from 1MHz to 3GHz. A circuit model for contact impedance degradation is proposed and applied in simulation of board level shielding. Contact resistance and contact capacitance are found to increase significantly after 2400h, causing a declined magnetic field shielding effectiveness by 30 dB below 500 MHz.