A surface defect detection method for rolling magnesium alloy sheet based on computer vision

In the rolling process of magnesium alloy sheet, defects such as edge crack, fold and ripple are easy to appear on the surface of the sheet. These defects will affect the product yield and quality, and cause waste of resources. In this paper, computer vision technology is used to analyze the image of rolling magnesium alloy sheet in real-time, extract its defect features, and Bayesian classifier and Random Forest (RF) classifier are used to identify defects. The experimental results show that the comprehensive defect recognition rate of the RF algorithm is up to 92,4 %, which is much higher than the accuracy of Bayesian classifier, and it is more suitable for the recognition of surface defects of magnesium sheet

Industry 4.0: Mining Physical Defects in Production of Surface-Mount Devices

With the advent of Industry 4.0, production processes have been endowed with intelligent cyber-physical systems generating massive amounts of streaming sensor data. Internet of Things technologies have enabled capturing, managing, and processing production data at a large scale in order to utilize this data as an asset for the optimization of production processes. In this work, we focus on the automatic detection of physical defects in the production of surfacemount devices. We show how to build a classification model based on random forests that efficiently detects defect products with a high degree of precision. In fact, the results of our preliminary experimental analysis indicate that our approach is able to correctly determine defects in a simulated production environment of surface-mount devices with a MCC score of 0.96. We investigate the feasibility of utilizing this approach in realistic settings. We believe that our approach will help to advance the production of surface-mount devices

Fast and Resource-Efficient Object Tracking on Edge Devices: A Measurement Study

Object tracking is an important functionality of edge video analytic systems and services. Multi-object tracking (MOT) detects the moving objects and tracks their locations frame by frame as real scenes are being captured into a video. However, it is well known that real time object tracking on the edge poses critical technical challenges, especially with edge devices of heterogeneous computing resources. This paper examines the performance issues and edge-specific optimization opportunities for object tracking. We will show that even the well trained and optimized MOT model may still suffer from random frame dropping problems when edge devices have insufficient computation resources. We present several edge specific performance optimization strategies, collectively coined as EMO, to speed up the real time object tracking, ranging from window-based optimization to similarity based optimization. Extensive experiments on popular MOT benchmarks demonstrate that our EMO approach is competitive with respect to the representative methods for on-device object tracking techniques in terms of run-time performance and tracking accuracy. EMO is released on Github at https://github.com/git-disl/EMO

Nlcviz: Tensor Visualization And Defect Detection In Nematic Liquid Crystals

Visualization and exploration of nematic liquid crystal (NLC) data is a challenging task due to the multidimensional and multivariate nature of the data. Simulation study of an NLC consists of multiple timesteps, where each timestep computes scalar, vector, and tensor parameters on a geometrical mesh. Scientists developing an understanding of liquid crystal interaction and physics require tools and techniques for effective exploration, visualization, and analysis of these data sets. Traditionally, scientists have used a combination of different tools and techniques like 2D plots, histograms, cut views, etc. for data visualization and analysis. However, such an environment does not provide the required insight into NLC datasets. This thesis addresses two areas of the study of NLC data---understanding of the tensor order field (the Q-tensor) and defect detection in this field. Tensor field understanding is enhanced by using a new glyph (NLCGlyph) based on a new design metric which is closely related to the underlying physical properties of an NLC, described using the Q-tensor. A new defect detection algorithm for 3D unstructured grids based on the orientation change of the director is developed. This method has been used successfully in detecting defects for both structured and unstructured models with varying grid complexity

Edge waves and resonance on elastic structures: An overview

Copyright @ 2011 SAGE PublicationsOver 50 years have elapsed since the first experimental observations of dynamic edge phenomena on elastic structures, yet the topic remains a diverse and vibrant source of research activity. This article provides a focused history and overview of such phenomena with a particular emphasis on structures such as strips, rods, plates and shells. Within this context, some of the recent research highlights are discussed and the contents of this special issue of Mathematics and Mechanics of Solids on dynamical edge phenomena are introduced