Search

Preforming process would change yarn angle and yarn orientation, its influence on the material properties and material orientations needs to be considered in the performance analysis. However, most current performance models fail to account for the preforming effect. An integrated performance model accounting for the impact of preforming has been developed in this research. In this integrated model, part geometry, yarn angle and orientation after preforming of multiple prepreg layers are predicted by Finite Element Analysis (FEA) using a non-orthogonal constitutive law. Experiments were conducted to validate the preforming simulation for a single dome composites structure made by two prepreg layers with different initial fiber orientations. Performance analysis until failure was then conducted for the single dome structure to validate the integrated performance model. Comparison between simulation and experiment shows that not only the failure mode and failure zone, but also the force-displacement curve during compression process are captured correctly by the performance model, demonstrating the effectiveness of the newly proposed model in accounting for the impact of preforming process.

In this study, an epoxy resin, YZ-05, designed specifically for carbon fiber reinforced polymer prepregs, was characterized and modeled. A degassing method for the highly viscous YZ-05 was established for specimen preparation. To numerically model the behavior of YZ-05 in curing, several components, including the heat transfer, curing kinetics, and viscoelastic constitutive law ones, were developed, and the corresponding material properties to be input were tested. As YZ-05 shows severe creep at high temperature under mechanical loading, the combination of digital image correlation (DIC) and thermography technique was utilized to obtain its thermal expansion and chemical shrinkage. In the aspects of viscoelastic behavior, stress relaxation tests were performed based on time–temperature superposition principle with a numerical method to calculate shift factors. After development and input identification, these modeling components for YZ-05 resin were integrated and the modeling results were validated using experiments, where bending of resin beams was induced during curing.

Manufacturing process would modify the yarn angle of woven composites. To have a high-fidelity mechanical analysis, it is necessary to study its potential impact. In this article, experimental tests were specifically designed and conducted to investigate the influence of yarn angle on the mechanical behaviors of cured woven composites. The results show that yarn angle variation has a significant impact on the elastic modulus, Poisson’s ratio, and yield stress. The tensile deformation along yarn directions and shear deformation between two yarns were also investigated and their coupling with yarn angle variation was analyzed. It shows that when the yarn angle variation (from 90°) is less than 10°, the influence of the tension–shear coupling on the tensile behavior along yarn directions and shear behavior between two yarn directions is small and can be neglected. When the yarn angle variation is more than 20°, the influence of tension–shear coupling is significant and must be accounted for in constructing the constitutive law for the cured non-orthogonal woven composites.

Carbon fiber reinforced plastics (CFRPs) have attracted growing attention from transportation industry because of their superior performance over metal counterparts. The thermoforming process has been invented for mass-production of CFRP parts. This thermoforming process involves multiple steps and numerous parameters, and one of the most important process parameters is the initial blank geometry. To optimize blank geometry in thermoforming and minimize trimming work for the final part, an innovative approach integrating automatic mesh adjustment and FEA modeling method for preforming is developed as foundation of design and reported in this paper. Preliminary trial-and-error optimization algorithm demonstrates that this newly-developed approach can accurately design blank geometry under various typical process conditions.

Hybrid titanium composite laminates (HTCLs) are high-performance light-weight fiber metal laminates (FMLs) that are being increasingly used in various industries such as aeronautical, military, and marine thanks to their optimized fracture toughness, impact resistance, and thermal performance. In the current study, the low-velocity impact (LVI) characteristics of a new generation of thermoplastic (TP) HTCLs at various energy levels are investigated. To do so, Ti-6Al-4 V sheets, carbon fabrics, and ultra-high molecular weight polyethylene (UHMWPE) fabrics are used to fabricate multiple laminates with different fiber types, metal volume fractions, and lamination layups. A low-cost resin infusion process is employed for manufacturing the laminates at room temperature by using a novel liquid thermoplastic methyl methacrylate resin, Elium® 188. Before fabrication, a multi-step surface treatment method is applied on Ti alloy sheets to enhance the interfacial properties between the composite layer and the metal alloy sheet. In addition to TP-HTCLs, equivalent thermosetting (TS) HTCLs with an epoxy resin, Epolam, are fabricated to compare the results and evaluate the possibility of fabricating recyclable TP-FMLs at room temperature with enhanced out-of-plane properties. Impact properties including contact force, deflection, energy parameters, and related damage modes are investigated and presented for each laminate. It is concluded that the newly developed TP-HTCLs can be cured at room temperature and have enhanced impact properties compared to those of TS-HTCLs. Besides, the HTCL with UHMWPE fabrics on its composite sides (before the Ti alloy sheets) performs better in LVI compared to that with carbon fibers on the top and bottom (of its composite core) since UHMWPE exhibits higher strain to failure and fracture toughness compared to carbon fibers.

Water Jet Incremental Micro-Forming (WJIMF) is a novel alternative to Single-Point Incremental Micro-Forming (SPIMF). Instead of using a rigid tool to form the sheet metal, a high-speed and high-pressure water jet is employed to induce plastic deformation. Dieless WJIMF has the capability to form a variety of shapes, but the total depth of the formed shape, which is affected by several process parameters, cannot be well controlled. To alleviate this problem, supporting dies can be used. This paper systematically studies the WJIMF process with several types of micro-machined supporting dies to produce micro-scale shell parts with designed geometries. It has been shown that the design of the toolpath has a profound impact on the final geometry of the part even with the same supporting die. The experimental results suggest that supporting dies with different geometries require unique toolpath strategies to achieve high geometric accuracy. Furthermore, it has been determined that water jet pressure plays an important role in the plastic deformation of the metal foil and that inappropriate combinations of toolpath and water jet pressure lead to buckling which is not found in macro-scale Water Jet Incremental Forming (WJIF). A numerical model in ABAQUS was developed to simulate the process and predict part geometry.

This paper proposes a deep learning-based Mandarin-Tibetan cross-lingual speech synthesis to realize both Mandarin speech synthesis and Tibetan speech synthesis under a unique framework. Because Tibetan training corpus is hard to record, we train the acoustic models with a large scale Mandarin multi-speaker corpus and a small scale Tibetan one-speaker corpus. The acoustic models are trained with deep neural network (DNN), hybrid long short-term memory (LSTM), and hybrid bi-directional long short-term memory (BLSTM). We also further extend our Chinese text analyzer by adding a Tibetan text analyzer for generating context-dependent labels from input Chinese or Tibetan sentences. The Tibetan text analyzer includes a text normalization, a novel Tibetan word segmentation that combines a BLSTM with conditional random field, a prosodic boundary prediction, and a grapheme-to-phoneme conversion. We select the initials and the finals of both Mandarin and Tibetan as the speech synthesis units to train a speaker-independent mixed language average voice model (AVM) with DNN, hybrid LSTM, and hybrid BLSTM from Mandarin and Tibetan mixed corpus. Then the speaker adaptation is applied to train speaker-dependent DNN, hybrid LSTM, or hybrid BLSTM models of Mandarin or Tibetan with a small target speaker corpus from an AVM. Finally, we synthesize the Mandarin speech, or Tibetan speech though the speaker-dependent Mandarin or Tibetan models. The experiments show that the hybrid BLSTM-based cross-lingual speech synthesis framework is better than the other two cross-lingual frameworks and the Tibetan monolingual framework. The mixed Tibetan training corpus does not influence the voice quality of synthesized Mandarin speech. Furthermore, the hybrid BLSTM-based cross-lingual speech synthesis framework only needs 60% of the training corpus to synthesize a similar voice as the Tibetan monolingual framework. Therefore, the proposed method can be used for speech synthesis of low resource languages by borrowing the same tremendous resource language's corpus.

Shearing is the most widely exploited method to fracture and remove rock formations in many man-made rock-opening activities. During shearing, rock breaks into chips due to the periodic nucleation, propagation, and coalescence of numerous micro-cracks . The crack interactions along with the rock's complex physical properties lead to extreme challenges in reaching a consensus on the rock's failure behavior in shearing. In this paper, force-drop events, as a quantitative measure of the global rock breakage occurrence, were recorded and analyzed. A power-law distribution with a tunable cut-off between the frequency of the force-drop events and their magnitudes is shown. The obtained results, for the first time, reveal the rock's tuned critical behavior in shearing which is intrinsically similar to its failure behavior widely observed in earthquakes.

In this paper, we constructed speech synthesis corpus of Kham dialect. At the same time, we designed SAMP-Kham machine-readable phonetic label of Kham dialect, and proposed a framework of Kham dialect speech synthesis based on deep learning. The deep learning architecture includes deep neural network (DNN), hybrid long short-term memory (LSTM) and hybrid Bidirectional long short-term memory (BLSTM). The objective and subjective evaluations test showed that quality of synthesized speech of Kham dialect was satisfied.

This paper is mainly about a speech synthesis system based on Deep Neural Network (DNN) model of Yi languages, a kind of minority language in china. The system is composed of relatively complete text analysis of Yi, model training and speech synthesis module. Especially in front-end, the word segmentation, pause handling, word-to-phoneme conversion and label processing are used to analysis text of Yi language. We designed the question set for decision tree of DNN model training and used vocoder: WORLD for synthesis. The system achieves a relatively good Mean Opinion Score (MOS) of 3.93 by Yi undergraduates as evaluators compared with a MOS of 4.58 of original speech. To investigate the factors affecting the quality of synthesized Yi speech, this paper also objectively evaluates the performance of different training set and DNN model. The system successfully synthesized Yi speech for the first time and synthesized speech is relatively good as the result of an only complete minority language speech synthesis system.

Woven composites are considered as a promising material choice for lightweight applications. An improved non-orthogonal material model that can decouple the strong tension and weak shear behavior of the woven composite under large shear deformation is proposed for simulating the preforming of woven composites. The tension, shear and compression moduli in the model are calibrated using the tension, bias-extension and bending experiments, respectively. The interaction between the composite layers is characterized by a sliding test. The newly developed material model is implemented in the commercial finite element software LS-DYNA® and validated by a double dome study.

Conventional single-point incremental forming (SPIF) is already in use for small batch prototyping and fabrication of customized parts from thin sheet metal blanks by inducing plastic deformation with a rigid round-tip tool. The major advantages of the SPIF process are its high flexibility and die-free nature. In lieu of employing a rigid tool to incrementally form the sheet metal, a high-speed water jet as an alternative was proposed as the forming tool. Since there is no tool-workpiece contact in this process, unlike in the traditional SPIF process, no lubricant and rotational motion of the tool are required to reduce friction. However, the geometry of the part formed by water jet incremental microforming (WJIMF) will no longer be controlled by the motion of the rigid tool. On the contrary, process parameters such as water jet pressure, stage motion speed, water jet diameter, blank thickness, and tool path design will determine the final shape of the workpiece. This paper experimentally studies the influence of the above-mentioned key process parameters on the geometry of a truncated cone shape and on the corresponding surface quality. A numerical model is proposed to predict the shape of the truncated cone part after WJIMF with given input process parameters. The results prove that the formed part's geometric properties predicted by the numerical model are in excellent agreement with the actually measured ones. Arrays of miniature dots, channels, two-level truncated cones, and letters were also successfully fabricated on stainless-steel foils to demonstrate WJIMF capabilities.

Carbon fiber reinforced plastics (CFRPs) are attracting growing attention in industry because of their enhanced properties. Preforming of thermoset carbon fiber prepregs is one of the most common production techniques of CFRPs. To simulate preforming, several computational methods have been developed. Most of these methods, however, obtain the material properties directly from experiments such as uniaxial tension and bias-extension where the coupling effect between tension and shear is not considered. Neglecting this coupling effect deteriorates the prediction accuracy of simulations. To address this issue, we develop a Bayesian model calibration and material characterization approach in a multiscale finite element preforming simulation framework that utilizes mesoscopic representative volume element (RVE) to account for the tension-shear coupling. A new geometric modeling technique is first proposed to generate the RVE corresponding to the close-packed uncured prepreg. This RVE model is then calibrated with a modular Bayesian approach to estimate the yarn properties, test its potential biases against the experiments, and fit a stress emulator. The predictive capability of this multiscale approach is further demonstrated by employing the stress emulator in the macroscale preforming simulation which shows that this approach can provide accurate predictions.

This study investigates the effects of fiber type, resin type, and stacking sequence on the dynamic response of fiber-reinforced polymer composite (FRPC) laminates under low-velocity impact (LVI) tests. Novel thermoplastic (TP) laminates are fabricated with a newly developed liquid methyl methacrylate thermoplastic resin, Elium® 188, at room temperature. FRPCs comprising woven ultra-high molecular weight polyethylene (UHMWPE) fabrics, woven carbon fabrics, and two different hybrid systems with alternative stacking sequences of those fibers are fabricated by the vacuum-assisted resin infusion (VARI) method. Besides, equivalent thermosetting-based (TS) composites with two epoxy systems are fabricated to compare the role of matrix type. Impact tests at different energy levels are performed on the TP and TS laminates to investigate the impact characteristics, namely contact force, deflection, energy attributes, structural integrity, and failure/damage modes. Besides, the mechanics of structure genome (MSG) and the commercial finite element code ABAQUS are used to verify the experimental results for one of the developed laminates. The results demonstrate that the hybrid system with UHMWPE fibers on the sides exhibits lower structural loss up to 47% and lower absorbed energy by 18% compared to those presented by the other type of hybrid system comprising carbon fabrics on the sides. Besides, it is found that the newly developed TP laminate underwent extended plasticity and presented a ductile behavior. The newly developed TP laminate demonstrated lower structural loss up to 200%, lower contact force by 14%, and lower absorbed energy by 48% compared to those of TS counterparts.

It is essential to accurately describe the large shear behavior of woven fabrics in the composite preforming process. An analytical model is proposed to describe the shear behavior of fabrics with different weave patterns, in which tension-shear coupling is considered. The coupling is involved in two parts, the friction between overlapped yarns and the in-plane transverse compression between two parallel yarns. By introducing the concept of inflection points of a yarn, the model is applicable for fabrics with different weave patterns. The analytical model is validated by biaxial tension-shear experiments. A parametric study is conducted to investigate the effects of external load, yarn geometry, and weave structure on the large shear behavior of fabrics. The developed model can reveal the physical mechanism of tension-shear coupling of woven fabrics. Moreover, the model has a high computational efficiency due to its explicit expressions, thus benefiting the material design process.

Carbon fiber reinforced composites have received growing attention because of their superior performance and high potential for lightweight systems. An economic method to manufacture the parts made of these composites is a sequence of forming followed by a compression molding. The first step in this sequence is called preforming that forms the prepreg, which is the fabric impregnated with the uncured resin, to the product geometry, while the molding process cures the resin. Slip between different prepreg layers is observed in the preforming step, and it is believed to have a non-negligible impact on the resulting geometry. This paper reports a method to characterize the interaction between different prepreg layers, which should be valuable for future predictive modeling and design optimization. An experimental device was built to evaluate the interactions with respect to various industrial production conditions. The experimental results were analyzed for an in-depth understanding about how temperature, relative sliding speed, and fiber orientation affect the tangential interaction between two prepreg layers. Moreover, a hydro-lubricant model was introduced to study the relative motion mechanism of this fabric-resin-fabric system, and the results agreed well with the experiment data. The interaction factors obtained from this research will be implemented in a preforming process finite element simulation model.

Carbon fiber composites have received growing attention because of their high performance. One economic method to manufacturing the composite parts is the sequence of forming followed by the compression molding process. In this sequence, the preforming procedure forms the prepreg, which is the composite with the uncured resin, to the product geometry while the molding process cures the resin. Slip between different prepreg layers is observed in the preforming step and this paper reports a method to characterize the properties of the interaction between different prepreg layers, which is critical to predictive modeling and design optimization. An experimental setup was established to evaluate the interactions at various industrial production conditions. The experimental results were analyzed for an in-depth understanding about how the temperature, the relative sliding speed, and the fiber orientation affect the tangential interaction between two prepreg layers. The interaction factors measured from these experiments will be implemented in the computational preforming program.

In most current works related to the structure analysis of cured woven composites, the influence of yarn angle variation on the local material properties is neglected. In this paper, its influence was studied, and an integrated preforming-performance simulation model, accounting for the influence of yarn angle variation, was proposed. The multi-scale modeling approach was adopted to predict the material properties of cured woven composites with different yarn angles. The results were compared with experiments to validate its effectiveness. In the integrated preforming-performance simulation model, non-orthogonal constitutive law was used in the preforming simulation to compute yarn orientations and yarn angle distribution. To demonstrate the capabilities of the integrated simulation model, simulations were conducted and compared with experiments. The results show that the proposed simulation model has more accurate prediction than the simulation model without considering the influence of yarn angle, and the impact of yarn angle shouldn’t be neglected.

Polycrystalline diamond compact (PDC) cutters, as a major cutting tool, have been widely applied in oil and gas drilling processes. The understanding of the complex interactions at the rock and cutter interfaces is essential for the advancement of future drilling technologies; yet, these interactions are still not fully understood. Linear cutting of rock, among all the testing methods, avoids the geometric and process complexities and offers the most straightforward way to reveal the intrinsic mechanisms of rock cutting. Therefore, this paper presents an experimental study of the cutter’s cutting performance and the rock’s failure behaviors on a newly developed linear rock cutting facility. A series of rock cutting tests were designed and performed. The acquired experimental data was analyzed to investigate the influences of process parameters and the rock’s mechanical properties on chip formation and force responses.

Polycrystalline diamond compact (PDC) cutter, as a major cutting tool, has been widely applied in oil and gas drilling processes. The understanding of the complex interactions at the rock and cutter interfaces are essential for the advancement of future drilling technologies, yet, these interactions are still not fully understood. Linear cutting of rock, among all the testing methods, avoids the geometric and process complexities and offer the most straightforward way to reveal the intrinsic mechanisms of rock cutting. Therefore, this paper presents an experimental study of the cutter’s cutting performance and the rock’s failure behaviors on a newly developed linear rock cutting facility. A series of rock cutting tests were designed and performed. The acquired experimental data was analyzed to investigate the influences of process parameter and the rock’s mechanical properties on chip formation and force responses.Copyright © 2016 by ASME

A real-time ECG signal acquisition system with the DSP chip TMS320VC5509A as its core is introduced in this paper. It introduces the design of ECG signal acquisition circuit in detail. ECG signal is analog filtered and amplified and then converted from analog to digital field. Finally, it is displayed in the LCD after digital low-pass filtering in DSP. The collected ECG data can truly reflect the condition of human heart's health. The system has advantages of small volume, little power consumption, low cost and real-time processing

In order to make cutoff frequency of filter that can be changed automatically with the time-varying signal, this chapter presents a new self-tracking filter structure which is based on frequency to voltage conversion. Input signal is converted into voltage signal through the F/V circuit. The output voltage of F/V is used to control cutoff frequency of the filter which is constituted of analog multiplier MLT04, current feedback amplifier AD844, a few resistors and capacitors. The design principle is introduced in detail, the design formulas are derived, and the circuit of self-tracking one-order low-pass and high-pass filter are given. When the input signal’s frequency is in the range of 10–100 kHz, the simulated and measured results show the effectiveness of this new method. The cutoff frequency of the filter can be adjusted automatically by the voltage which is proportional to frequency of input signal. So it realizes the frequency self-tracking.

In the XML query algorithm based on matching pretreatment, three important existing tree matching models are used and the match results of data sets are presented according to the match cost in a descending order. The function of matching pretreatment is added to improve the existing algorithm, and a series of experiments are conducted. The results show that this algorithm can remove the unwanted node in the tree to promote the efficiency of data sets retrieval especially the recall ratio, the precision ratio and the average response time, when the data scale is quite large. This algorithm is applied in the unity retrieval system of scientific and technological resources database. It can facilitate the navigation of resources, narrow the search scope and promote the efficiency of this system.

In this work, a multiscale modeling framework for CFRP is introduced to study hierarchical structure of CFRP. Four distinct scales are defined: nanoscale, microscale, mesoscale, and macroscale. Information at lower scales can be passed to higher scale, which is beneficial for studying effect of constituents on macroscale part’s mechanical property. This bottom-up modeling approach enables better understanding of CFRP from finest details. Current study focuses on microscale and mesoscale.