An automatic system based on thermoelectric cooler (TEC), a microfan, and microcontroller is first applied to thermal management of high-power light-emitting diodes (LEDs). Its hardware is composed of microcontroller as a control core, K-type thermocouples as acquisition devices, and TEC and a microfan with heatsink as cooling vehicles. The experiment confirms that the LEDs substrate temperature can be controlled effectively, and indicates that the LED chips are operating reliably. Specifically, in high-temperature environments of 43 °C, the system can automatically drop to the low set temperature (30 °C) due to thermoelectric effect driven by TEC. Heat transfer analysis shows that maximum LED power cooled by the system is 106.7 W, and the total power consumption of the automatic cooling system is only 8.85 W. The automatic cooling system has a high cooling efficiency.

A novel bi-piezoelectric jet dispenser with a zoom mechanism is proposed to distribute adhesives rapidly. The work frequency of the new jet dispenser can reach 500 Hz. The volume of the minimum dot is about 25 nL, and the volume error among dots is not more than ±10%. The dot size can be controlled by adjusting the driving voltage of the piezoelectric stack, filling pressure, and the opening time of the valve. Subsystem physical models of the jet valve are presented on the basis of the bi-piezoelectric principle and the zoom mechanism, which involves an electromechanical model, dynamic model, and fluid-solid coupling model. Based on these physical models, a coupled mechanical-electrical fluid simulation model is established, which can be simulated. The simulation results of the multiphysics-coupled model are in accordance with the experiment. The coupling model will develop a reliable simulating platform for high-speed fluid jet. The effectiveness of the bi-piezoelectric method and models is confirmed, and it will provide a new technology for microelectronics packaging.

Data-driven methods have shown its great latent capacity in the field of industrial process monitoring. However, the existing methods usually achieve well results under the assumption that the offline learning data and the online monitoring data are drawn from the same distribution. Unfortunately, in the industrial system, the assumption is often violated due to the harsh operating environment. Especially, with the increasing complexity and scale of industrial production, the supervisory control and data acquisition (SCADA) data of the industrial production process often collected from different machines, seasons or operating modes. In addition, owing to the cost of manual data labeling and real time requirement of process monitoring, the offline learning data, which was used to build the model, often have abundant source domain data and insufficient target domain data. Consequently, these methods have bad performance on the online monitoring data collected from target domain. In order to make full use of the knowledge from the abundant source domain data, a transfer dictionary learning method is proposed to address the cross-domain problem in this paper. The proposed method can learn an initial dictionary from the abundant source domain data, then the final dictionary is updated by incorporating the feature of insufficient target domain data in a smooth subspace interpolation way. The effectiveness of the proposed method is evaluated through a numerical simulation case, a continuous stirred tank heater (CSTH) case and a wind turbine system case, from which we can see the proposed method has better performance compared with some state-of-the-art methods.

In order to develop jetting technology in LED and microelectronics packaging, an innovative electromagnetic jet valve with zoom mechanism and opening electromagnetic coil is suggested, and an improved method of double voltage drive is applied in the electromagnetic control. It is found that the needle stroke of the new jet valve is increased to 1.3 times, and the needle velocity is raised to 1.8 times, and the temperature of opening electromagnetic coil is lowered to 6.7 °C. The jet valve can successfully jet 4 Pa·s high-viscosity adhesives at room temperature due to the improvement of dynamic characteristics. It will provide a new method for jetting high-viscosity adhesives.

Multivariate regression models are commonly used in industrial process measurements. Locally weighted partial least squares (LWPLS) is a just-in-time learning (JITL) method for nonlinear multivariate regression models. However, it considers only the sample importance. In this article, a novel JITL multivariate regression model termed double LWPLS (D-LWPLS) is proposed to solve complex nonlinear problems and promote the model accuracy. Both the sample importance and variable importance are taken into consideration for the weighted model. Variable contribution is modified according to the variable importance. Sample similarity measurement is a significant basis for the model sample selection and sample weight calculation. The traditional sample similarity measurement methods take only the input information of samples as the index. In the D-LWPLS, a weighted similarity measurement is employed. The information on both input and output is considered by weighting the input variables according to the correlation between them. The effectiveness and the flexibility of the D-LWPLS model are demonstrated by the prediction results of a numerical example, a low-dimensional data set of an industrial process, and a high-dimensional data set of UV-Vis spectra of a polymetallic ion solution, which represent three kinds of nonlinear problems of classical, time-varying, and blind background fluctuation.