The Quantitative Research of Interaction between Key Parameters and the Effects on Mechanical Property in FDM

The central composite design (CCD) experiment is conducted to evaluate the interaction between parameters and the effect on mechanical property. The layer thickness, deposition velocity, and air gap are considered as the key factors. Three disparate levels of the parameters are utilized in the experiment. The experimental results suggest that all these parameters can affect the bonding degree of the filaments, which affects the final tensile strength of the specimen. A new numerical model is built to describe the cooling process of the fused filament, which shows a perfect coherence with the practical temperature file of filament. It reveals what the forming mechanism of the bonding between filaments is and how these parameters act on final tensile strength of the specimen of this way from temperature. It is concluded that the parameters are not working alone; in fact they all contribute to determining the mechanical property, while the air gap plays the predominant role in determining the final tensile strength, followed by layer thickness as the next predominant factor, and the effect of deposition velocity is the weakest factor

Fault diagnosis of rotating machinery under time-varying speed based on order tracking and deep learning

Due to the disadvantages that rely on prior knowledge and expert experience in traditional order analysis methods and deep learning cannot accurately extract the features in time-varying conditions. A fault diagnosis method for rotating machinery under time-varying conditions based on tacholess order tracking (TOT) and deep learning is proposed in this paper. Firstly, frequency domain periodic signals and estimated speed information are obtained by order tracking. Secondly, the frequency domain periodic signal is speed normalized using the estimated speed information. Finally, normalized features are extracted by deep learning network to form feature vector. The feature vector is fed into a softmax layer to complete fault diagnosis of the gearbox. The fault diagnosis of the gearbox results are compared with other traditional methods and show that the proposed fault diagnosis method can effectively identify the faults and obtain higher fault diagnosis accuracy under time-varying speed

Energy weighting method and its application to fault diagnosis of rolling bearing

Feature extraction of vibration signal is the key factor of machine fault diagnosis. This paper proposes a novel method of capturing shock energy based on multi-scale weight evaluation of high definition time-frequency map. Specifically, the proposed method is conducted by the following steps. First, ensemble empirical mode decomposition (EEMD) preprocessor-based Hilbert-Huang Transform (HHT) is applied to the raw signal for high definition time-frequency map acquisition. Second, an original algorithm named multi-scale binary spectrum was applied to extract impulsive energy features with different frequency characteristics. Then weights of energy can be calculated by dimensionality reduction of each binary spectrum and merged by summation after blank processing. Finally, power spectrum of compound weight of energy can reveal characteristic frequency corresponding to specific fault. In this method, the key point is enhancement of frequency spectrum using higher dimension details. The process of multi-scale binarization and weight summation were aligned and the effectiveness is verified by simulated signal processing and actual case of train bearing experiment

Multimodal Neuroimaging Predictors for Cognitive Performance Using Structured Sparse Learning

poster abstractRegression models have been widely studied to investigate whether multimodal neuroimaging measures can be used as effective biomarkers for predicting cognitive outcomes in the study of Alzheimer's Disease (AD). Most existing models overlook the interrelated structures either within neuroimaging measures or between cognitive outcomes, and thus may have limited power to yield optimal solutions. To address this issue, we propose to incorporate an L21 norm and/or a group L21 norm (G21 norm) in the regression models. Using ADNI-1 and ADNI-GO/2 data, we apply these models to examining the ability of structural MRI and AV-45 PET scans for predicting cognitive measures including ADAS and RAVLT scores. We focus our analyses on the participants with mild cognitive impairment (MCI), a prodromal stage of AD, in order to identify useful patterns for early detection. Compared with traditional linear and ridge regression methods, these new models not only demonstrate superior and more stable predictive performances, but also identify a small set of imaging markers that are biologically meaningful

Category Selectivity of Human Visual Cortex in Perception of Rubin Face–Vase Illusion

When viewing the Rubin face–vase illusion, our conscious perception spontaneously alternates between the face and the vase; this illusion has been widely used to explore bistable perception. Previous functional magnetic resonance imaging (fMRI) studies have studied the neural mechanisms underlying bistable perception through univariate and multivariate pattern analyses; however, no studies have investigated the issue of category selectivity. Here, we used fMRI to investigate the neural mechanisms underlying the Rubin face–vase illusion by introducing univariate amplitude and multivariate pattern analyses. The results from the amplitude analysis suggested that the activity in the fusiform face area was likely related to the subjective face perception. Furthermore, the pattern analysis results showed that the early visual cortex (EVC) and the face-selective cortex could discriminate the activity patterns of the face and vase perceptions. However, further analysis of the activity patterns showed that only the face-selective cortex contains the face information. These findings indicated that although the EVC and face-selective cortex activities could discriminate the visual information, only the activity and activity pattern in the face-selective areas contained the category information of face perception in the Rubin face–vase illusion

Constant Cutting Force Control for CNC Machining Using Dynamic Characteristic-Based Fuzzy Controller

This paper presents a dynamic characteristic-based fuzzy adaptive control algorithm (DCbFACA) to avoid the influence of cutting force changing rapidly on the machining stability and precision. The cutting force is indirectly obtained in real time by monitoring and extraction of the motorized spindle current, the feed speed is fuzzy adjusted online, and the current was used as a feedback to control cutting force and maintain the machining process stable. Different from the traditional fuzzy control methods using the experience-based control rules, and according to the complex nonlinear characteristics of CNC machining, the power bond graph method is implemented to describe the dynamic characteristics of process, and then the appropriate variation relations are achieved between current and feed speed, and the control rules are optimized and established based on it. The numerical results indicated that DCbFACA can make the CNC machining process more stable and improve the machining precision

Analytical Modeling of a Ball Screw Feed Drive for Vibration Prediction of Feeding Carriage of a Spindle

An analytical modeling approach for ball screw feed drives is proposed to predict the dynamic behavior of the feeding carriage of a spindle. Mainly considering the rigidity of linear guide modules, a ball-screw-feeding spindle is modeled by a mass-spring system. The contact stiffness of rolling interfaces in linear guide modules is accurately calculated according to the Hertzian theory. Next, a mathematical model is derived using the Lagrange method. The presented model is verified by conducting modal experiments. It is found that the simulated results correspond closely with the experimental data. In order to show the applicability of the proposed mathematical model, parameter-dependent dynamics of the feeding carriage of the spindle is investigated. The work will contribute to the vibration prediction of spindles

Study of Subdivision Surface Modelling for Spiral Bevel Gear Manufacturing

Precise machining spiral bevel gear (SBGs) on Numerical Control (NC) machine tools has attracted much research attention. This paper presents a new precise SBGs CAD/CAM model with Loop subdivision algorithm. The involute surfaces for spiral bevel gears and pinions are first modelled with parametric surfaces. From them and a set of parameters, an initial mesh model is created for gear surfaces. This mesh model is then used as a base for a subdivision surface. The precision of subdivision surfaces can be exactly controlled by a subdivision process which can generate enough machining points on the designed tooth surface model. As a result, all meshing points of the tooth surface for a pair of gear and pinion can be applied on NC machines for higher-precision surface manufacturing. This novel SBGs CAD/CAM modelling method has been evaluated with precision. The precise model could be extended to hypoid gear and other complex surface design

A Time-Distributed Spatiotemporal Feature Learning Method for Machine Health Monitoring with Multi-Sensor Time Series

Data-driven methods with multi-sensor time series data are the most promising approaches for monitoring machine health. Extracting fault-sensitive features from multi-sensor time series is a daunting task for both traditional data-driven methods and current deep learning models. A novel hybrid end-to-end deep learning framework named Time-distributed ConvLSTM model (TDConvLSTM) is proposed in the paper for machine health monitoring, which works directly on raw multi-sensor time series. In TDConvLSTM, the normalized multi-sensor data is first segmented into a collection of subsequences by a sliding window along the temporal dimension. Time-distributed local feature extractors are simultaneously applied to each subsequence to extract local spatiotemporal features. Then a holistic ConvLSTM layer is designed to extract holistic spatiotemporal features between subsequences. At last, a fully-connected layer and a supervised learning layer are stacked on the top of the model to obtain the target. TDConvLSTM can extract spatiotemporal features on different time scales without any handcrafted feature engineering. The proposed model can achieve better performance in both time series classification tasks and regression prediction tasks than some state-of-the-art models, which has been verified in the gearbox fault diagnosis experiment and the tool wear prediction experiment