Sensor Fusion and Process Monitoring for Ultrasonic Welding of Lithium-ion Batteries.

Ultrasonic metal welding is used for joining lithium-ion batteries of electric vehicles. The quality of the joints is essential to the performance of the entire battery pack. Hence, the ultrasonic welding process that creates the joints must be equipped with online sensing and real-time process monitoring systems. This would help ensure the process to be operated under the normal condition and quickly address quality-related issues. For this purpose, this dissertation develops methods in process monitoring and fault diagnosis using online sensing signals for ultrasonic metal welding. The first part of this dissertation develops a monitoring algorithm that targets near-zero misdetection by integrating univariate control charts and a multivariate control chart using the Mahalanobis distance. The proposed algorithm is capable of monitoring non-normal multivariate observations with adjustable control limits to achieve a near-zero misdetection rate while keeping a low false alarm rate. The proposed algorithm proves to be effective in achieving near-zero misdetection in process monitoring in ultrasonic welding processes. The second part of the dissertation develops a wavelet-based profile monitoring method that is capable of making decisions within a welding cycle and guiding real-time process adjustments. The proposed within-cycle monitoring technique integrates real-time monitoring and within-cycle control opportunity for defect prevention. The optimal decision point for achieving the most benefit in defect prevention is determined through the formulation of an optimization problem. The effectiveness of the proposed method is validated and demonstrated by simulations and case studies. The third part of this dissertation develops a method for effective monitoring and diagnosis of multi-sensor heterogeneous profile data based on multilinear discriminant analysis. The proposed method operates directly on the multi-stream profiles and then extracts uncorrelated discriminative features through tensor-to-vector projection, and thus preserving the interrelationship of different sensors. The extracted features are then fed into classifiers to detect faulty operations and recognize fault types. The research presented in this dissertation can be applied to general discrete cyclic manufacturing processes that have online sensing and control capabilities. The results of this dissertation are also applicable or expandable to mission-critical applications when improving product quality and preventing defects are of high interests.PhDIndustrial and Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113405/1/graceguo_1.pd

Box Spline Wavelet Frames for Image Edge Analysis

We present a new box spline wavelet frame and apply it for image edge analysis. The wavelet frame is constructed using a box spline of eight directions. It is tight and has seldom been used for applications. Due to the eight different directions, it can find edges of various types in detail quite well. In addition to step edges (local discontinuities in intensity), it is able to locate Dirac edges (momentary changes of intensity) and hidden edges (local discontinuity in intensity derivatives). The method is simple and robust to noise. Many numerical examples are presented to demonstrate the effectiveness of this method. Quantitative and qualitative comparisons with other edge detection techniques are provided to show the advantages of this wavelet frame. Our test images include synthetic images with known ground truth and natural, medical images with rich geometric information

A Case Study on Optimization of Building Design Based on CFD Simulation Technology of Wind Environment

AbstractFor green buildings, the building technology needs to fit the climatic characteristics to adjust to the existing conditions and create a more livable environment. CFD is a combination of modern fluid dynamics, numerical mathematics and computer science. It is employed in green building design to offer the architect an important basis for optimizing the architectural design. The Paper takes a case study to show the research on application of CFD simulation technology of wind environment in optimizing architectural design, gives some ideas to architects in green building design optimization

Research on the psychologically restorative effects of campus common spaces from the perspective of health

Contemporary college students are suffering from increasingly serious psychological health problems, such as attention fatigue, psychological stress and negative emotions. A growing body of evidence has revealed that restorative environment design is conducive to psychological health. As the main choice of venue for students’ daily activities, campus common spaces are supposed to be restorative to some extent. Given the above, the author studied 22 common spaces in the South China University of Technology (SCUT) Wushan Campus from the perspective of college students’ behavioral patterns based on theories pertaining to restorative environments, then constructed a structural equation model (SEM) analyzing the psychologically restorative effects exerted by the characteristics of campus common spaces upon college students through a scale design and questionnaire survey. With the analysis of 478 valid questionnaires, the research found that the characteristics of campus common spaces with psychologically restorative effects mainly comprise the architectural environment, landscape environment, rest facilities and activity facilities. Among them, the characteristics of activity facilities and the landscape environment have the greatest impact on psychologically restorative effects, accounting for 33 and 30% of the total effects, respectively; they are followed by those of the architectural environment, which accounts for 21% of the total effects; those of the rest facilities have the least impact, accounting for 16% of the total effects. The research also found that the characteristics of campus common spaces can both directly influence college students’ psychological recovery and produce psychologically restorative effects mediated by college students’ behavioral patterns. The mediation effect of college students’ behavioral patterns accounts for approximately 41% of the total effect of psychological restoration, in which the psychologically restorative effect of dynamic exercise behaviors is 2.5 times that of static leisure behaviors. The research reveals how the characteristics of campus common spaces promote the psychological restoration of college students, and it provides inspiration for healthy environment design in campus common spaces