Multiple Performance Optimization for Microstrip Patch Antenna Improvement.

As the Internet of Things (IOT) becomes more widely used in our everyday lives, an increasing number of wireless communication devices are required, meaning that an increasing number of signals are transmitted and received through antennas. Thus, the performance of antennas plays an important role in IOT applications, and increasing the efficiency of antenna design has become a crucial topic. Antenna designers have often optimized antennas by using an EM simulation tool. Although this method is feasible, a great deal of time is often spent on designing the antenna. To improve the efficiency of antenna optimization, this paper proposes a design of experiments (DOE) method for antenna optimization. The antenna length and area in each direction were the experimental parameters, and the response variables were antenna gain and return loss. Response surface methodology was used to obtain optimal parameters for the layout of the antenna. Finally, we utilized antenna simulation software to verify the optimal parameters for antenna optimization, showing how the DOE method can increase the efficiency of antenna optimization. The antenna optimized by DOE was implemented, and its measured results show that the antenna gain and return loss were 2.65 dBi and 11.2 dB, respectively.

Smart Monitoring of Manufacturing Systems for Automated Decision-Making: A Multi-Method Framework.

Smart monitoring plays a principal role in the intelligent automation of manufacturing systems. Advanced data collection technologies, like sensors, have been widely used to facilitate real-time data collection. Computationally efficient analysis of the operating systems, however, remains relatively underdeveloped and requires more attention. Inspired by the capabilities of signal analysis and information visualization, this study proposes a multi-method framework for the smart monitoring of manufacturing systems and intelligent decision-making. The proposed framework uses the machine signals collected by noninvasive sensors for processing. For this purpose, the signals are filtered and classified to facilitate the realization of the operational status and performance measures to advise the appropriate course of managerial actions considering the detected anomalies. Numerical experiments based on real data are used to show the practicability of the developed monitoring framework. Results are supportive of the accuracy of the method. Applications of the developed approach are worthwhile research topics to research in other manufacturing environments.

Yield improvement for lost mould rapid infiltration forming process by a multistage fractional factorial split plot design.

Statistical design of experiments is widely used among scientists and engineers to understand influential factors in a laboratory or manufacturing process. One of the underlying principles of using the statistical design of experiments method is randomisation, each run of experimental settings will be determined completely unsystematically. In practice, especially in a complicated process that consists of multiple stages, randomisation may pose too high a burden on time and cost.In this study, the multistage fraction factorial split plot design is proposed for green yield improvement in a lost mould rapid infiltration process that has been developed to fabricate zirconia ceramic parts. This design allows a relaxation of the randomisation principle so that certain experimental runs can be carried out in convenient groups. The results indicate that the type of immersion chemical and mould coating play a role in improving process yield. Additionally, the results suggest that a mould infiltration machine should be used to improve the reproducibility of the process.