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Additive Manufacturing has brought about substantial benefits to the manufacturing industry due to the numerous advantages it provides, at the same time there are factors that can be improved upon. Temperature control is an important parameter during the build process as it affects build quality. The main objective of this thesis project was to investigate what sensors could be used for monitoring the temperature during the additive manufacturing processand to compare and evaluate their performance. This involved implementing two 2-color pyrometers and a short-wave infrared camera to monitor the temperature of the area behind the melt pool and then visualizing the respective data. Initial issues arose during test runs in the form of noise in the pyrometer data, this was solved by implementing a smoothing filter to the signal. Multiple runs were conducted to capture the required data as images produced by the camera were overexposed and out of focus during initial runs. This was solved by changing the camera position and exposure settings. Reading the temperature values from the images involved interpreting the Average Dark Units (ADU) values of the region of interest and then comparing those values to a reference chart. The data gathered with the help of LabVIEW software and the proprietary imaging software of the camera showed that the selected sensors were in fact suitable for the intended task and could be used in conjunction with each other. This data could then be used to create a closed-loop system in the future (not in the scope of this thesis work) and thus enable the increase in the level of automation for Robotized Laser Wire Additive Manufacturing.

Yes / Super-fine stainless wire (SSW) can not only form widely distributed enhancing, toughening and conductive network in reactive powder concrete (RPC) at low dosage level, but also improve weak interface area and refine cracks due to its micron scale diameter and large specific surface. In addition, the crack resistance zone generated by SSWs and RPC matrix together has potential to further enhance the fracture properties of composites. Therefore, fracture and self-sensing characteristics of SSW reinforced RPC composites were investigated in this paper. Experimental results indicated that adding 1.5 vol. % of SSW leads to 183.1% increase in the initial cracking load of RPC specimens under three-point bending load. Based on two parameter fracture model calculations, an increase of 203.4% in fracture toughness as well as an increase of 113.3% in crack tip opening displacement of the composites reinforced with 1.5% SSWs are achieved. According to double-K fracture model calculations, the initiation fracture toughness and unstable fracture toughness of the composites are enhanced by 185.2% and 179.2%, respectively. The increment for fracture energy of the composites reaches up to 1017.1% because of the emergence of blunt and tortuous cracks. The mixed mode Ⅰ-Ⅱ fracture toughness of the composites is increased by 177.1% under four-point shearing load. The initial angle of mixed mode Ⅰ-Ⅱ cracks of the composites decreases with the increase of SSW content. The initiation and propagation of cracks in the composites can be monitored by their change in electrical resistivity. The excellent fracture toughness of the composites is of great significance for the improvement of structure safety in serviceability limit states, and the self-sensing ability of the composites can also provide early warning for the degradation of structure safety. / National Key Research and Development Program of China (2018YFC0705601), the National Science Foundation of China (51578110), China Postdoctoral Science Fundation (2019M651116) and the Fundamental Research Funds for the Central Universities in China (DUT18GJ203).

A building must meet requirements related to energy usage and good indoor environment. The building ́s ventilation system aims to maintain optimal air quality, thermal condition, and efficient energy usage. By being able to control and adjust airflow as needed with a Variable Air Volume (VAV) system, energy usage of the ventilation system can be reduced without sacrificing indoor air quality. The VAV system operates with a variable airflow, so the accuracy of the VAV terminal at low flows is important. The study aims to verify the accuracy of three different methods for measuring airflow: iris damper, hot- wire anemometer, and volume flow hood. Data collection was carried out through measurements in a laboratory environment. The collected data were managed in the Excel spreadsheet program. The three methods were tested on a prototype consisting of a supply airline and an exhaust airline, each equipped with a VAV unit that varied the airflow through a potentiometer, allowing different airflow values to be obtained for the experiment. The results of the iris damper method were chosen as reference for the other methods due to its ability to predict results using manufacturer tables, which provide useful information about airflow values when varying pressure drop and the iris damper loss k-factor. In this way, set points were stablished for all tests. Two experiments were conducted with different set points. In retrospect it is acknowledged that an air flow meter of higher accuracy had been needed to ascertain the results of the study. That said, the attained results indicate the following: In the experiments, measurements were taken for 12 different airflow values, ranging from 200 l/s to 20 l/s. The test results showed that the most inaccurate method was the volume flow hood, with an average error ranging from ±19% to ±49%, followed by the hot wire anemometer, which had a measurement error between ±15% to ±18%. The high inaccuracy of the volume flow hood was due to a lack of instrument recalibration, resulting in highly inaccurate measurements. All methods showed lower uncertainty between 20 l/s and 50 l/s. Finally, it was verified that the iris damper method improved its average reading error by increasing the pressure drop across the damper and reducing its loss k-factor, achieving the lowest error of the entire experiment at ±12%.