Papers by Keyword: Quality Monitoring

Abstract: To know the quality of drinking water in Indonesia, conventional method, which must be done by testing water samples in a water testing laboratory which not everyone knows where it is and takes quite a long time to get the results. For this reason, it is necessary to design a drinking water monitoring system prototype in a water tank that is easy to use using IoT technology. Sensors are used to retrieve water quality parameter data in real time and then the ESP32 microcontroller which is equipped with a WIFI module will send data to a database server which can then be downloaded and displayed via an application. The parameters monitored in this work are the pH value, electric conductivity, temperature and turbidity, following the standards of the Indonesian government for drinking water. The test results show that the prototype is working according to the design.

Abstract: This spirit of establishment and perfection of teaching management of high purpose, construction design consists of four interconnected system of the teaching quality of the subsystem; "end of test" Converts the whole process of quality control. After the given weights of evaluation on teaching quality, with cloud model tools, empirical through multi-factor comprehensive evaluation model for integrated measurement, final result based on cloud model evaluation results more clearly and objectively reflect the distribution of data, has the correct decision in terms of usability.

Abstract: A test method for monitoring of cement mortar quality has been devised and an empirical relational equation between cement mortar fluidity and torque has been worked out. A test method for monitoring of indoor fresh cement concrete quality has been devised and a relational equation between fresh cement concrete and torque has been worked out. A test method for monitoring of fresh cement concrete in a mixing plant has been devised and a relational equation between fresh cement concrete and current property has been worked out. By means of online observed torque and current data, relevant fluidity and slump can be predicted to provide test methods and relevant theoretical basis for intelligent monitoring of fresh cement mortar and cement concrete quality in actual engineering.

Abstract: In this paper a quality monitoring system for seismic while drilling (SWD) that integrates the whole process of data acquisition was developed. The acquisition equipment, network status and signals of accelerometer and geophone were monitored real-time. With fast signal analysis and quality evaluation, the acquisition parameters and drilling engineering parameters can be adjusted timely. The application of the system can improve the quality of data acquisition and provide subsequent processing and interpretation with high qualified reliable data.

Abstract: The surface roughness and roundness(SRR) are widely used indexes of mechanical product quality. How to implement the SRR monitoring is a crucial task. In this study, the hidden Markov models (HMMs) and the cutting vibration signals are applied to monitor the SRR in variant cutting conditions. Unlike most of the prior work only to reveal one element of the geometric specifications, based on the theoretical analysis of the influence of tool vibration displacement on the SRR, the vibration energy characteristic(VEC) is determined to serve as the characteristic for monitoring surface roughness(Ra) and roundness(Rd) synchronously.Which make up the insufficiency of the comprehensive monitoring of workpiece quality. Moreover, although classical hidden Markov models (HMMs) have been successfully used for fault diagnostics of mechanical systems, this method based on recognition rate is becoming unreliable to monitor the accuracy of the workpiece. Hence, the HMM-based judgment matrix method is proposed and it is tested and validated successfully using for SRR monitoring through a series of experiments.

Abstract: This paper presents a quality monitoring and prognostic method to evaluating quality of electronics through monitoring degradation path. Electronics multiple performance parameter degradation data are treated as multidimensional time series and described using multidimensional time series model to take into account implements of stochastic nature of environmental variables and to predict long-term trend of performance degradation. A degradation test is processed for certain electronics and three kinds of performance parameters degradation data are monitored for prognostics. A comparison between the predicted degradation path using multidimensional time series analysis, the predicted degradation path using one-dimensional time series analysis and the real degradation path of the electronics is processed and the results show that the degradation path prediction using the suggested method is more effective than one-dimensional time series analysis.

Abstract: In this paper laser welding AA5182 of aluminum alloy with AA5356 filler wire were performed with respect to laser power, welding speed, and wire feed rate. The experiments showed that the tensile strength of the weld was higher than that of the base material under sufficient heat input conditions. A genetic algorithm was used to optimize process parameters which were the laser power, welding speed, and wire feed rate. To do that, a fitness function was formulated, taking into account weldability and productivity. A factor for the weldabilty used tensile strength estimation model which was made by neural network, and as the productivity, welding speed, and wire feed rate were used. Weld monitoring system for aluminum laser welding with filler wire was constructed through the optical sensors to measure the plasma light intensity. Relationship between monitoring signal and plasma and keyhole behavior according to welding condition was analyzed and it was found that sensor signal could express the information for weld quality. Weld quality estimation algorithm was formulated fuzzy multi feature pattern recognition algorithm using the monitoring signals. Quality prediction system was also developed to apply this algorithm to production line.

Abstract: A fuzzy neural network based system was developed to control the quality of resistance spot welding. A fuzzy neural controller (FNC) was designed to realize the memory of the fuzzy neural control bases on a back propagation (BP) network, accordingly achieve the mapping of fuzzy input and output. The experiment results show that this method can compensate the multi-crossfire’s influence on the welding currency, keep a high control precision, and fulfill the demand of high precision quality monitoring to the resistance spot welding.

Abstract: Based on practice road tunnel engineering in Gansu province and near areas in west china, the main construction technology and emphasis of quality monitoring of waterproof and drainage of loess road tunnel are analyzed. Requirements of waterproof and drainage construction and methods of quality monitoring are obtained. Principle of design for waterproof and drainage in highway tunnels construction is provided. The analysis results provide technical guidance for the waterproof construction optimization of loess tunnels.

Abstract: This paper first introduces the principle of AGC and conventional AGC in Hot Strip Mill (HSM). A linearized and discretized state-space model used for rolling force and thickness control is obtained by using recursive squares method. A data fusion algorithm based on Kalman filter is presented. For hot strip systems with complex multi-variables, an asynchronous fusion estimation algorithm is built and applied to the thickness prediction of the hot strip mill and the plasticity coefficient Q of strip prediction. Finally, real-time prediction on thickness and plasticity coefficient of the coming strip is synthetically utilized in hot strip rolling thickness control system, to improve the quality of final coming strip thickness.