Recyclable Technology of Thermosetting Resins for High Thermal Conductivity Materials Based on Physical Crushing

Epoxy resin,characterized by prominent mechanical and electric-insulation properties,is the preferred material for packaging power electronic devices.Unfortunately,the efficient recycling and reuse of epoxy materials with thermally cross-linked molecular structures has become a daunting challenge.Here,we propose an economical and operable recycling strategy to regenerate waste epoxy resin into a high-performance material.Different particle size of waste epoxy micro-spheres(100–600μm)with core-shell structure is obtained through simple mechanical crushing and boron nitride surface treatment.By using smattering epoxy monomer as an adhesive,an eco-friendly composite material with a“brick-wall structure”can be formed.The continuous boron nitride pathway with efficient thermal conductivity endows eco-friendly composite materials with a preeminent thermal conductivity of 3.71 W m^(−1)K^(−1)at a low content of 8.5 vol%h-BN,superior to pure epoxy resin(0.21 W m^(−1)K^(−1)).The composite,after secondary recycling and reuse,still maintains a thermal conductivity of 2.12 W m^(−1)K^(−1)and has mechanical and insulation properties comparable to the new epoxy resin(energy storage modulus of 2326.3 MPa and breakdown strength of 40.18 kV mm^(−1)).This strategy expands the sustainable application prospects of thermosetting polymers,offering extremely high economic and environmental value.

A new ensemble-based classifier for IGBT open-circuit fault diagnosis in three-phase PWM converter

Three-phase pulse width modulation converters using insulated gate bipolar transistors(IGBTs)have been widely used in industrial application.However,faults in IGBTs can severely affect the operation and safety of the power electronics equipment and loads.For ensuring system reliability,it is necessary to accurately detect IGBT faults accurately as soon as their occurrences.This paper proposes a diagnosis method based on data-driven theory.A novel randomized learning technology,namely extreme learning machine(ELM)is adopted into historical data learning.Ensemble classifier structure is used to improve diagnostic accuracy.Finally,time window is defined to illustrate the relevance between diagnostic accuracy and data sampling time.By this mean,an appropriate time window is achieved to guarantee a high accuracy with relatively short decision time.Compared to other traditional methods,ELM has a better classification performance.Simulation tests validate the proposed ELM ensemble diagnostic performance.