arXiv subjects

Xiaowei Yue

Publications and source records attributed to Xiaowei Yue.

24 records · Page 2Linked to original sources

arXiv2020

StressNet: Deep Learning to Predict Stress With Fracture Propagation in Brittle Materials

Catastrophic failure in brittle materials is often due to the rapid growth and coalescence of cracks aided by high internal stresses. Hence, accurate prediction of maximum internal stress is critical to predicting time to failure and improving the fracture resistance and reliability of materials. Existing high-fidelity methods, such as the Finite-Discrete Element Model (FDEM), are limited by their high computational cost. Therefore, to reduce computational cost while preserving accuracy, a novel deep learning model, "StressNet," is proposed to predict the entire sequence of maximum internal stress based on fracture propagation and the initial stress data. More specifically, the Temporal Independent Convolutional Neural Network (TI-CNN) is designed to capture the spatial features of fractures like fracture path and spall regions, and the Bidirectional Long Short-term Memory (Bi-LSTM) Network is adapted to capture the temporal features. By fusing these features, the evolution in time of the maximum internal stress can be accurately predicted. Moreover, an adaptive loss function is designed by dynamically integrating the Mean Squared Error (MSE) and the Mean Absolute Percentage Error (MAPE), to reflect the fluctuations in maximum internal stress. After training, the proposed model is able to compute accurate multi-step predictions of maximum internal stress in approximately 20 seconds, as compared to the FDEM run time of 4 hours, with an average MAPE of 2% relative to test data.

cs.LG↗

arXiv2020

Optimal Path Planning for Automated Dimensional Inspection of Free-Form Surfaces

Structural dimensional inspection is vital for the process monitoring, quality control, and fault diagnosis in the mass production of auto bodies. Comparing with the non-contact measurement, the high-precision five-axis measuring machine with the touch-trigger probe is a preferred choice for data collection. It can assist manufacturers in making accurate inspection quickly. As the increase of free-form surfaces and diverse surface orientations in auto body design, existing inspection approaches cannot capture some new critical features in the curvature of auto bodies in an efficient way. Therefore, we need to develop new path planning methods for automated dimensional inspection of free-form surfaces. This paper proposes an optimal path planning system for automated programming of measuring point inspection by incorporating probe rotations and effective collision detection. Specifically, the methodological contributions include: (i) a dynamic searching volume-based algorithm is developed to detect potential collisions in the local path between measurement points; (ii) a local path generation method is proposed with the integration of the probe trajectory and the stylus rotation. Then, the inspection time matrix is proposed to quantify the measuring time of diverse local paths; (iii) an optimization approach of the global inspection path for all critical points on the auto body is developed to minimize the total inspection time. A case study has been conducted on an auto body to verify the performance of the proposed method. Results show that the collision-free path for the free-form auto body could be generated automatically with off-line programming, and the proposed method produces about 40% fewer dummy points and needs 32% less movement time in the auto body inspection process.

cs.RO↗

arXiv2020

Active Learning for Gaussian Process Considering Uncertainties with Application to Shape Control of Composite Fuselage

In the machine learning domain, active learning is an iterative data selection algorithm for maximizing information acquisition and improving model performance with limited training samples. It is very useful, especially for the industrial applications where training samples are expensive, time-consuming, or difficult to obtain. Existing methods mainly focus on active learning for classification, and a few methods are designed for regression such as linear regression or Gaussian process. Uncertainties from measurement errors and intrinsic input noise inevitably exist in the experimental data, which further affects the modeling performance. The existing active learning methods do not incorporate these uncertainties for Gaussian process. In this paper, we propose two new active learning algorithms for the Gaussian process with uncertainties, which are variance-based weighted active learning algorithm and D-optimal weighted active learning algorithm. Through numerical study, we show that the proposed approach can incorporate the impact from uncertainties, and realize better prediction performance. This approach has been applied to improving the predictive modeling for automatic shape control of composite fuselage.

stat.ML↗

arXiv2020

Real-time Data-driven Quality Assessment for Continuous Manufacturing of Carbon Nanotube Buckypaper

Carbon nanotube (CNT) thin sheet, or buckypaper, has shown great potential as a multifunctional platform material due to its desirable properties, including its lightweight nature, high mechanical properties, and good conductivity. However, their mass adoption and applications by industry have run into significant bottlenecks because of large variability and uncertainty in quality during fabrication. There is an urgent demand to produce high-quality, high-performance buckypaper at an industrial scale. Raman spectroscopy provides detailed nanostructure information within seconds, and the obtained spectra can be decomposed into multiple effects associated with diverse quality characteristics of buckypaper. However, the decomposed effects are high-dimensional, and a systematic quantification method for buckypaper quality assessment has been lacking. In this paper, we propose a real-time data-driven quality assessment method, which fills in the blank of quantifying the quality for continuous manufacturing processes of CNT buckypaper. The composite indices derived from the proposed method are developed by analyzing in-line Raman spectroscopy sensing data. Weighted cross-correlation and maximum margin clustering are used to fuse the fixed effects into an inconsistency index to monitor the long-term mean shift of the process and to fuse the normal effects into a uniformity index to monitor the within-sample normality. Those individual quality indices are then combined into a composite index to reflect the overall quality of buckypaper. A case study indicates that our proposed approach can determine the quality rank for ten samples, and can provide quantitative quality indices for single-walled carbon nanotube buckypaper after acid processing or functionalization. The quality assessment results are consistent with evaluations from the experienced engineers.

eess.SP↗

arXiv2019

Effective Model Calibration via Sensible Variable Identification and Adjustment, with Application to Composite Fuselage Simulation

Estimation of model parameters of computer simulators, also known as calibration, is an important topic in many engineering applications. In this paper, we consider the calibration of computer model parameters with the help of engineering design knowledge. We introduce the concept of sensible (calibration) variables. Sensible variables are model parameters which are sensitive in the engineering modeling, and whose optimal values differ from the engineering design values.We propose an effective calibration method to identify and adjust the sensible variables with limited physical experimental data. The methodology is applied to a composite fuselage simulation problem.

stat.AP↗

arXiv2019

Tensor Mixed Effects Model with Applications in Nanomanufacturing Inspection

Raman mapping technique has been used to perform in-line quality inspections of nanomanufacturing processes. In such an application, massive high-dimensional Raman mapping data with mixed effects is generated. In general, fixed effects and random effects in the multi-array Raman data are associated with different quality characteristics such as fabrication consistency, uniformity, defects, et al. The existing tensor decomposition methods cannot separate mixed effects, and existing mixed effects model can only handle matrix data but not high-dimensional multi-array data. In this paper, we propose a tensor mixed effects (TME) model to analyze massive high-dimensional Raman mapping data with complex structure. The proposed TME model can (i) separate fixed effects and random effects in a tensor domain; (ii) explore the correlations along different dimensions; and (iii) realize efficient parameter estimation by a proposed iterative double Flip-Flop algorithm. We also investigate the properties of the TME model, existence and identifiability of parameter estimation. The numerical analysis demonstrates the efficiency and accuracy of the parameter estimation in the TME model. Convergence and asymptotic properties are discussed in the simulation and surrogate data analysis. The case study shows an application of the TME model in quantifying the influence of alignment on carbon nanotubes buckypaper. Moreover, the TME model can be applied to provide potential solutions for a family of tensor data analytics problems with mixed effects.

stat.ME↗