Huamin Zhou

Bio: Huamin Zhou is an academic researcher from Huazhong University of Science and Technology. The author has contributed to research in topics: Materials science & Composite material. The author has an hindex of 25, co-authored 166 publications receiving 2100 citations. Previous affiliations of Huamin Zhou include South China University of Technology & Center for Advanced Materials.

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.

Graphene/epoxy interleaves for delamination toughening and monitoring of crack damage in carbon fibre/epoxy composite laminates

Abstract: Functionalized graphene sheets were fabricated by a simple thermal reduction method in air at 700 °C and used to prepare graphene/epoxy (G/E) composites. The incorporation of 1.0 wt% of graphene into epoxy was found to significantly improve the matrix fracture energy by ∼150% and reduce the thermal expansion coefficient by ∼30%. When these partially cured G/E composites were used as interleaves in carbon fiber/epoxy (CF/E) composites and co-cured, a remarkable 140% increase in mode I interlaminar fracture energy was achieved. Detailed toughening mechanisms of the graphene sheets in both G/E and CF/E composites were studied. Moreover, the damage sensing capacity of the graphene interleaved CF/E composites with a simple electrical response method was demonstrated, where the electrical resistance change increased almost linearly with crack increment.

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.

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.

Boundary Layer Theory

Abstract: The boundary layer equations for plane, incompressible, and steady flow are $$\matrix{ {u{{\partial u} \over {\partial x}} + v{{\partial u} \over {\partial y}} = - {1 \over \varrho }{{\partial p} \over {\partial x}} + v{{{\partial ^2}u} \over {\partial {y^2}}},} \cr {0 = {{\partial p} \over {\partial y}},} \cr {{{\partial u} \over {\partial x}} + {{\partial v} \over {\partial y}} = 0.} \cr }$$

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.