Analytical Solution for Model-Based Dynamic Power Factor Measurement in AC Resistance Spot Welding

On the basis of welding transformer circuit model, a new measuring method was proposed. This method measures the peak angle of the welding current, and then calculates the dynamic power factor in each half-wave. An artificial neural network is trained and used to generate simulation data for the analytical solution, i.e. a high-order binary polynomial, which can be easily adopted to calculate the power factor online. The tailored sensing and computing system ensures that the method possesses a real-time computational capacity and satisfying accuracy. A DSP-based resistance spot welding monitoring system was developed to perform ANN computation. The experimental results suggest that this measuring method is feasible.     

Intelligent drug delivery system using UML diagrams analysis

A novel intelligent drug delivery system potential for the more effective therapy of the diabeticswas proposed, and the composition of system was analyzed. Based on the design of micro-electro-mechanicalsystems (MEMS), an iterative modeling process was introduced. Unified modeling language (UML) was em-ployed to describe the function requirement, and different diagrams were built up to explore the static model,the dynamic model and the employment model. The mapping analysis of different diagrams can simply verifythe consistency and completeness of the system model.     

A vehicle traveling time prediction method based on grey theory and linear regression analysis

Vehicle traveling time prediction is an important part of the research of intelligent transportation system. By now, there have been various kinds of methods for vehicle traveling time prediction. But few consider both aspects of time and space. In this paper, a vehicle traveling time prediction method based on grey theory (GT) and linear regression analysis (LRA) is presented. In aspects of time, we use the history data sequence of bus speed on a certain road to predict the future bus speed on that road by GT. And in aspects of space, we calculate the traffic affecting factors between various roads by LRA. Using these factors we can predict the vehicle's speed at the lower road if the vehicle's speed at the current road is known. Finally we use time factor and space factor as the weighting factors of the two results predicted by GT and LRA respectively to find the fina0l result, thus calculating the vehicle's travehng time. The method also considers such factors as dwell time, thus making the prediction more accurate.     

Self-organizing map based quality assessment for resistance spot welding with featured electrode displacement signals

To classify the quality of the resistance spot welding process, a relationship between the welder electrode displacement curve characteristics and the weld shear force has been explored. Eleven statistical features of the displacement signals are extracted to represent the welding quality. Self-organizing map (SOM) neural networks have been employed to discover their quantitative relationship. In order to identify the influence of various displacement curve features, all of the available combinations have been used as inputs for SOM neural networks. Further we analyze the impact of each feature on the classification results, yielding the best quality-indicative combination of characteristics. There is no determinant relationship between the welding quality and the level of expulsion rate. The quality of welding is most impacted by the maximum electrode displacement, the span of welding process and the centroid of the electrode displacement curve. The experiments show that SOM is feasible to assess the welding quality and can render the visualized intuitive evaluation results.     

Modeling, identification and simulation of DC resistance spot welding process for aluminum alloy 5182

Currently the aluminum alloy resistance spot welding(AA-RSW) has been extensively used for light weight automotive body-in-white manufacturing.However the aluminum alloys such as AA5182 have inferior weldability for forming the joint due to their high reflectiveness to heat and light.Therefore it is necessary to further develop the high performance control strategy and the set-up of a new welding schedule.The welding process identification is the essential issue where the difficulty arises from the fact that the AA-RSW is a nonlinear time-varying uncertain process which couples the thermal,electrical,mechanical and metallurgical dynamics.To understand this complicated physical phenomenon a novel dual-phase M-series pseudo-random electrical pattern is adopted to excite the AA-RSW electrical-thermal process and the thermal response is recorded according to the welding power outputs.Based on the experimental information,the transfer function of an AA-RSW electrical- thermal mechanism is identified,and the optimum model order and parameters are determined.Subsequently a control-oriented DC AA-RSW model is established to explore the welding power control algorithm.The simulated results of the control model show agreement with the experimental data,which validates its feasibility for the corresponding welding control.