Inspection of Enamel Removal Using Infrared Thermal Imaging and Machine Learning Techniques.

Within aerospace and automotive manufacturing, the majority of quality assurance is through inspection or tests at various steps during manufacturing and assembly. Such tests do not tend to capture or make use of process data for in-process inspection and certification at the point of manufacture. Inspection of the product during manufacturing can potentially detect defects, thus allowing consistent product quality and reducing scrappage. However, a review of the literature has revealed a lack of any significant research in the area of inspection during the manufacturing of terminations. This work utilises infrared thermal imaging and machine learning techniques for inspection of the enamel removal process on Litz wire, typically used for aerospace and automotive applications. Infrared thermal imaging was utilised to inspect bundles of Litz wire containing those with and without enamel. The temperature profiles of the wires with or without enamel were recorded and then machine learning techniques were utilised for automated inspection of enamel removal. The feasibility of various classifier models for identifying the remaining enamel on a set of enamelled copper wires was evaluated. A comparison of the performance of classifier models in terms of classification accuracy is presented. The best model for enamel classification accuracy was the Gaussian Mixture Model with expectation maximisation; it achieved a training accuracy of 85% and enamel classification accuracy of 100% with the fastest evaluation time of 1.05 s. The support vector classification model achieved both the training and enamel classification accuracy of more than 82%; however, it suffered the drawback of a higher evaluation time of 134 s.

Salt Template Assisted BN Scaffold Fabrication toward Highly Thermally Conductive Epoxy Composites.

With the trend of device miniaturization and higher integration, polymer composites with high thermal conductivity are highly desirable for efficient removal of accumulated heat to maintain high performance of electronics. In this work, epoxy composites embedded with three-dimensional hexagonal boron nitride (BN) scaffold were fabricated. The BN-poly(vinylidene difluoride) (PVDF) scaffold was prepared by the salt template method using PVDF as the adhesive, while the corresponding epoxy composite was manufactured with vacuum-assisted impregnation. The epoxy/BN-PVDF composite exhibits high thermal conductivity with low loading of BN. The thermal conductivity of epoxy/BN-PVDF composite achieved 1.227 W/(m K) with 21 wt % BN, contributed by the constructed BN pathway held together by PVDF adhesive. In addition, PVDF could be further converted into carbon by thermal treatment, further enhancing the thermal conductivity of epoxy/BN-C composites through alleviating the phonon scattering at the interfaces, eventually obtaining thermal conductivity of 1.466 W/(m K). This type of epoxy-based composite with high thermal conductivity is promising to be used as thermal management materials in advanced electronic devices.

Atmospheric Ice Adhesion on Water-Repellent Coatings: Wetting and Surface Topology Effects.

Recent studies have shown the potential of water-repellent surfaces such as superhydrophobic surfaces in delaying ice accretion and reducing ice adhesion. However, conflicting trends in superhydrophobic ice adhesion strength were reported by previous studies. Hence, this investigation was performed to study the ice adhesion strength of hydrophobic and superhydrophobic coatings under realistic atmospheric icing conditions, i.e., supercooled spray of 20 µm mean volume diameter (MVD) droplets in a freezing (-20 °C), thermally homogeneous environment. The ice was released in a tensile direction by underside air pressure in a Mode-1 ice fracture condition. Results showed a strong effect of water repellency (increased contact and receding angles) on ice adhesion strength for hydrophobic surfaces. However, the extreme water repellency of nanocomposite superhydrophobic surfaces did not provide further adhesion strength reductions. Rather, ice adhesion strength for superhydrophobic surfaces depended primarily on the surface topology spatial parameter of autocorrelation length (Sal), whereby surface features in close proximities associated with a higher capillary pressure were better able to resist droplet penetration. Effects from other surface height parameters (e.g., arithmetic mean roughness, kurtosis, and skewness) were secondary.