Pressure Equilibrium Time of a Cyclic-Olefin Copolymer

Development of an Epoxy-Based Rapid Tool with Low Vulcanization Energy Consumption Channels for Liquid Silicone Rubber Injection Molding

Liquid silicone rubber (LSR) parts have some distinct characteristics such as superior heat stability, low-temperature flexibility, aging resistance, and chemical resistance. From an industrial standpoint, the uniform vulcanization temperature of LSR is an important research point. However, the uniformity of the vulcanization temperature of LSR has been limited since the layout of the cartridge heater incorporated in the conventional steel mold does not follow the profile of the mold cavity. Metal additive manufacturing can be used to make LSR injection molds with conformal heating channels and conformal cooling channels simultaneously. However, this method is not suitable for a mold required to develop a new LSR product. In this study, a cost-effective approach was proposed to manufacture an LSR injection mold for the pilot run of a new optical lens. A rapid tool with low vulcanization energy consumption channels was proposed, which was incorporated with both a conformal heating channel (CHC) and conformal cooling channel (CCC) simultaneously. The function of the CHC was to vulcanize the LSR in the cavity uniformly, resulting in a shorter cycle time. The function of the CCC was to keep the LSR in a liquid state for reducing runner waste. It was found that the equation of y = −0.006x³ + 1.2114x² − 83.221x + 1998.2 with the correlation coefficient of 0.9883 seemed to be an optimum trend equation for predicting the solidification time of a convex lens (y) using the vulcanizing hot water temperature (x). Additionally, the equation of y = −0.002x³ + 0.1329x² − 1.0857x + 25.4 with the correlation coefficient of 0.9997 seemed to be an optimum prediction equation for the solidification time of a convex lens (y) using the LSR weight (x) since it had the highest correlation coefficient. The solidification time of a convex lens could be reduced by about 28% when a vulcanizing hot water temperature of 70 °C was used in the LSR injection mold with CHC.

Recent Advances in Thermoplastic Microfluidic Bonding

Microfluidics is a multidisciplinary technology with applications in various fields, such as biomedical, energy, chemicals and environment. Thermoplastic is one of the most prominent materials for polymer microfluidics. Properties such as good mechanical rigidity, organic solvent resistivity, acid/base resistivity, and low water absorbance make thermoplastics suitable for various microfluidic applications. However, bonding of thermoplastics has always been challenging because of a wide range of bonding methods and requirements. This review paper summarizes the current bonding processes being practiced for the fabrication of thermoplastic microfluidic devices, and provides a comparison between the different bonding strategies to assist researchers in finding appropriate bonding methods for microfluidic device assembly.

Extraction of Time-Domain Characteristics and Selection of Effective Features Using Correlation Analysis to Increase the Accuracy of Petroleum Fluid Monitoring Systems

In the current paper, a novel technique is represented to control the liquid petrochemical and petroleum products passing through a transmitting pipe. A simulation setup, including an X-ray tube, a detector, and a pipe, was conducted by Monte Carlo N Particle-X version (MCNPX) code to examine a two-by-two mixture of four diverse petroleum products (ethylene glycol, crude oil, gasoline, and gasoil) in various volumetric ratios. As the feature extraction system, twelve time characteristics were extracted from the received signal, and the most effective ones were selected using correlation analysis to present reasonable inputs for neural network training. Three Multilayers perceptron (MLP) neural networks were applied to indicate the volume ratio of three kinds of petroleum products, and the volume ratio of the fourth product can be feasibly achieved through the results of the three aforementioned networks. In this study, increasing accuracy was placed on the agenda, and an RMSE < 1.21 indicates this high accuracy. Increasing the accuracy of predicting volume ratio, which is due to the use of appropriate characteristics as the neural network input, is the most important innovation in this study, which is why the proposed system can be used as an efficient method in the oil industry.