Yun Zhang

Bio: Yun Zhang is an academic researcher from Huazhong University of Science and Technology. The author has contributed to research in topics: Finite element method & Molding (process). The author has an hindex of 20, co-authored 94 publications receiving 1140 citations.

Timely online chatter detection in end milling process

Abstract: Chatter is one of the most unexpected and uncontrollable phenomenon during the milling operation. It is very important to develop an effective monitoring method to identify the chatter as soon as possible, while existing methods still cannot detect it before the workpiece has been damaged. This paper proposes an energy aggregation characteristic-based Hilbert–Huang transform method for online chatter detection. The measured vibration signal is firstly decomposed into a series of intrinsic mode functions (IMFs) using ensemble empirical mode decomposition. Feature IMFs are then selected according to the majority energy rule. Subsequently Hilbert spectral analysis is applied on these feature IMFs to calculate the Hilbert time/frequency spectrum. Two indicators are proposed to quantify the spectrum and thresholds are automatically calculated using Gaussian mixed model. Milling experiments prove the proposed method to be effective in protecting the workpiece from severe chatter damage within acceptable time complexity.

Effect of defects on thermal conductivity of graphene/epoxy nanocomposites

Abstract: Owing to the super thermal conductivity of graphene, graphene/polymer nanocomposites have the potential as thermal management materials in many applications. Previous studies have proved that the defects in the graphene sheets can greatly reduce the thermal conductivity of suspended graphene. However, the effects of defects on the interfacial thermal conductance and thermal conductivity of graphene/epoxy nanocomposites have not been well understood. In this paper, the effect of four common types of defects, i.e., single-vacancy, double-vacancy, Stone-Wales and Multi-vacancy, on the interfacial thermal transport between the epoxy and graphene was systematically investigated by using molecular dynamic simulations. The simulation results showed that the interfacial thermal conductance between graphene-epoxy could be considerably enhanced with the existence of Stone-Wales and Multi-vacancy defects. The underlying mechanism was explicated by using the phonon vibration power spectrum. Additionally, based on the effective medium theory and the simulation results, the effect of defects on the thermal conductivity of graphene/epoxy nanocomposites was investigated concerning different graphene filler sizes and volume fractions. Although the inherent thermal conductivity of embedded graphene may be decreased by its defects, it was possible to increase the thermal conductivity of the nanocomposites when the graphene filler size was smaller than a critical value.

Delamination toughening of carbon fiber/epoxy laminates by hierarchical carbon nanotube-short carbon fiber interleaves

Abstract: In this study, hierarchical short carbon fibers (SCFs) synthesized with carbon nanotubes (CNTs) were used as CNT-SCF interleaves to increase the mode I delamination fracture energy G IC of carbon fiber/epoxy (CF/EP) composite laminates. Even at a relatively low CNT-SCF areal density, 1.0 mg/cm 2 , G IC (1.17 kJ/m 2 ) was increased by 125% compared to the control laminates (0.52 kJ/m 2 ), which is a very high value compared to those results obtained by other interleaving methods in CF/EP laminates. The toughening effects of SCFs in bulk epoxy and interleaved CF/EP laminates were also studied to better understand the failure mechanisms of the hierarchical CNT-SCF structure. SEM observations revealed synergistic toughening mechanisms in these CNT-SCF interleaved CF/EP laminates.

Monitoring and dynamic control of quality stability for injection molding process

Abstract: Stability control of production is an important aspect of injection molding. However, challenges continue to exist with respect to improving product quality stability to achieve a faster forming speed and a higher automation for injection molding because the injection process is usually disturbed by several inevitable variations. The difficulty in overcoming the fore-mentioned inevitable disturbances and achieving dynamic control of product quality is related to establishing a quantitative relationship between product quality and process variables. In this study, a quality prediction model based on polymer melt properties is established to monitor product weight variation online. A pressure integral (PI) based on the prediction model is proposed as an effective process variable to predict product weight variation. Additionally, a dynamic control method is proposed to improve product quality stability. The experimental results indicate that PI presents advantages of consistency and stability in monitoring product weight variation when compared with models proposed by extant studies. The proposed control method results in a decrease in product weight variation from 0.16% to 0.02% in the case of varying mold temperature and the number of cycles to return stability decreases from 11 to 5 in with respect to variations in the melt temperature.

On adhesive properties of nano-silica/epoxy bonded single-lap joints

Abstract: The effects of nano-silica on the adhesive properties of epoxy were systematically studied by single lap-shear tests under quasi-static and cyclic loadings. The adhesives were produced from different amount of nano-silica particles incorporated into diglycidyl ether of bisphenol A (DGEBA) epoxy. Stainless steel plates were chosen as adherends. Quasi-static tests were conducted on single lap-shear joints at ambient, with and without exposure to 100% RH at 60 °C for different times. Cyclic fatigue tests were also performed on these bonded joints under tension–tension loading. The fracture surface morphology was examined using scanning electron microscopy (SEM) to identify the failure mechanisms. Compared to neat epoxy, it was found that the adhesive strength is increased by 20% under quasi-static loadings. Even after hygrothermal treatment, the benefit of having nano-silica in neat epoxy on the adhesive joint strength was retained. In cyclic fatigue, without hygrothermal aging, nano-slica/epoxy adhesives have longer lifetimes than neat epoxy; but after hygrothermal treatment, they have similar lifetimes for given stress amplitudes.

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Electroactive phases of poly(vinylidene fluoride) : determination, processing and applications

Abstract: Poly(vinylidene fluoride), PVDF, and its copolymers are the family of polymers with the highest dielectric constant and electroactive response, including piezoelectric, pyroelectric and ferroelectric effects. The electroactive properties are increasingly important in a wide range of applications such as in biomedicine, energy generation and storage, monitoring and control, and include the development of sensors and actuators, separator and filtration membranes and smart scaffolds, among others. For many of these applications the polymer should be in one of its electroactive phases. This review presents the developments and summarizes the main characteristics of the electroactive phases of PVDF and copolymers, indicates the different processing strategies as well as the way in which the phase content is identified and quantified. Additionally, recent advances in the development of electroactive composites allowing novel effects, such as magnetoelectric responses, and opening new applications areas are presented. Finally, some of the more interesting potential applications and processing challenges are discussed.

Deep learning and its applications to machine health monitoring

Abstract: Since 2006, deep learning (DL) has become a rapidly growing research direction, redefining state-of-the-art performances in a wide range of areas such as object recognition, image segmentation, speech recognition and machine translation. In modern manufacturing systems, data-driven machine health monitoring is gaining in popularity due to the widespread deployment of low-cost sensors and their connection to the Internet. Meanwhile, deep learning provides useful tools for processing and analyzing these big machinery data. The main purpose of this paper is to review and summarize the emerging research work of deep learning on machine health monitoring. After the brief introduction of deep learning techniques, the applications of deep learning in machine health monitoring systems are reviewed mainly from the following aspects: Auto-encoder (AE) and its variants, Restricted Boltzmann Machines and its variants including Deep Belief Network (DBN) and Deep Boltzmann Machines (DBM), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). In addition, an experimental study on the performances of these approaches has been conducted, in which the data and code have been online. Finally, some new trends of DL-based machine health monitoring methods are discussed.

An updated review of adhesively bonded joints in composite materials

Abstract: Continuing interest and more developments in recent years indicated that it would be useful to update Banea and da Silva paper entitled “Adhesively bonded joints in composite materials: an overview”. This paper presents an updated review of adhesively bonded joints in composite materials, which covers articles published from 2009 to 2016. The main parameters that affect the performance of bonded joints such as surface treatment, joint configuration, geometric and material parameters, failure mode etc. are discussed. The environmental factors such as pre-bond moisture, moisture and temperature are also discussed in detail and how they affect the durability of adhesive joints. Lots of shortcomings were resolved during the last years by developing new materials, new methods and models. However, there is still a potential to evaluate and identify the best possible combination of parameters which would give the best performance of composite bonded joints.

Factors affecting thermal conductivities of the polymers and polymer composites: A review

Abstract: It is of considerable scientific and technological importance to enhance the thermal conductivity coefficient (λ) values of the polymers and polymer composites. Limited understanding of heat transfer in polymers and polymer composites imposes restrictions on the designing and fabricating better thermally conductive polymers and polymer composites. This review attempts to help understand the thermal conduction mechanisms by analyzing the effects of different components in polymers and polymer composites on heat transfer. Factors of micro- and macro-characteristics, such as chain structures, interfaces, functionalization and processing techniques, etc., are all illustrated to elucidate their impacts on the thermal conductivities. In general, chain structures of polymers, intrinsic λ values of thermally conductive fillers and interfacial thermal resistances are the main and internal factors to determine the λ values of polymers and polymer composites. Meantime, processing and environmental factors are only auxiliary factors to improve the thermal conductivities. We expect this review will give some guidance to the future studies in thermally conductive polymers and polymer composites.