Treatment effects on the compressive strength of Reactive Powder Concrete (RPC) at 7 days

Reactive powder concrete (RPC) is produced by controlling three main factors: additives to mix composition, pre-setting application of pressure, and post-setting heating. Densification of the mortar is achieved by the application of pressure (either unidirectional static load or omnidirectional air pressure load) in order to minimize the presence of macro defects in the form of entrapped air voids. Heat curing is applied after final setting with temperatures of at least 90 °C in order to accelerate the hydration and pozzolanic reactions. The purpose of this research was to characterize the relative effect of these three treatment approaches on the compressive strength of RPC at 7 days. The variables assessed in this study include heating rate, treatment curing types; and with/ without static pressure (8 MPa). The results show that a heating rate of 50 °C/hr preceded by pressure application for 2 days were the optimum conditions for statically-pressed RPC samples. Assuming variables of 8 MPa static pressure and 2-day heat curing at 240°C, compressive strength increased by: 6 % using static pressure only, 60 % using heat curing only, and 83 % using both static pressure and heat curing. Further work will investigate the micro structural and chemical composition within the interfacial transition zone and mineral product evolution during hydration in combination with high temperature/ pressure curing conditions

Prediction of product quality in glass

This report presents solutions of EUNITE Competition 2003 problem "Prediction of product quality in glass manufacturing process". The first solution is based on Local Transfer Function Approximator (LTF-A) neural network, while the next three solutions utilize simple rules to predict glass qualit

Dataset relaterat till processövervakning och tillståndsövervakning av en lagerringsslipmaskin - Dataset for the Implementation of Condition-based Maintenance and Maintenance Decision-making of a Bearing Ring Grinder

In the article (Ahmer, M., Sandin, F., Marklund, P. et al., 2022), we have investigated the effective use of sensors in a bearing ring grinder for failure classification in the condition-based maintenance context. The proposed methodology combines domain knowledge of process monitoring and condition monitoring to successfully achieve failure mode prediction with high accuracy using only a few key sensors. This enables manufacturing equipment to take advantage of advanced data processing and machine learning techniques. The grinding machine is of type SGB55 from Lidköping Machine Tools and is used to produce functional raceway surface of inner rings of type SKF-6210 deep groove ball bearing. Additional sensors like vibration, acoustic emission, force, and temperature sensors are installed to monitor machine condition while producing bearing components under different operating conditions. Data is sampled from sensors as well as the machine's numerical controller during operation. Selected parts are measured for the produced quality. Ahmer, M., Sandin, F., Marklund, P., Gustafsson, M., & Berglund, K. (2022). Failure mode classification for condition-based maintenance in a bearing ring grinding machine. In The International Journal of Advanced Manufacturing Technology (Vol. 122, pp. 1479–1495). https://doi.org/10.1007/s00170-022-09930-6 The files are of three categories and are grouped in zipped folders. The pdf file named "readme_data_description.pdf" describes the content of the files in the folders. The "lib" includes the information on libraries to read the .tdms Data Files in Matlab or Python. The raw time-domain sensors signal data are grouped in seven main folders named after each test run e.g. "test_1"... "test_7". Each test includes seven dressing cycles named e.g. "dresscyc_1"... "dresscyc_7". Each dressing cycle includes .tdms files for fifteen rings for their individual grinding cycle. The column description for both "Analogue" and "Digital" channels are described in the "readme_data_description.pdf" file. The machine and process parameters used for the tests as sampled from the machine's control system (Numerical Controller) and compiled for all test runs in a single file "process_data.csv" in the folder "proc_param". The column description is available in "readme_data_description.pdf" under "Process Parameters". The measured quality data (nine quality parameters - normalized) of the selected produced parts are recorded in the file "measured_quality_param.csv" under folder "quality". The description of the quality parameters is available in "readme_data_description.pdf". The quality parameter disposition based on their actual acceptance tolerances for the process step is presented in file "quality_disposition.csv" under folder "quality".I publikationen (Ahmer, M., Sandin, F., Marklund, P. et al., 2022) har vi undersökt användningen av sensorer i en lagerringsslipmaskin för felklassificering och tillståndsövervakning. Föreslagen metod kombinerar domänkunskap om processövervakning och tillståndsövervakning för att framgångsrikt uppnå fellägesförutsägelse med hög noggrannhet med endast ett fåtal nyckelsensorer. Denna forskning visar att tillverkningsutrustning kan dra fördel av avancerad databehandling och maskininlärningsteknik. Slipmaskinen är av typ SGB55 från Lidköping Machine Tools och används i detta fall för att slipa löpbanor på lagerinnerringar av typ SKF-6210 spårkullager. Sensorer för vibration, akustisk emission, kraft och temperatur är installerade för att övervaka maskinens tillstånd under slipning och olika driftsförhållanden. Data insamlas från sensorerna samt maskinens numeriska styrenhet under drift. Utvalda producerade kvalitetsparametrar mäts efter slipoperationen. Ahmer, M., Sandin, F., Marklund, P., Gustafsson, M., & Berglund, K. (2022). Failure mode classification for condition-based maintenance in a bearing ring grinding machine. In The International Journal of Advanced Manufacturing Technology (Vol. 122, pp. 1479–1495). https://doi.org/10.1007/s00170-022-09930-6 Filerna är grupperade i mappar i zip-filer. Pdf-filen "readme_data_description.pdf" beskriver innehållet i filerna i mapparna. "lib" innehåller information om bibliotek som kan användas för att läsa .tdms-datafilerna i Matlab eller Python. Se den engelska beskrivningen för mer information