A new method for in-situ process monitoring of AM cooling rate-related defects
TL;DR: In this article, the capabilities of a new machine learning based framework for the detection of cooling rate-related defects in metal additive manufacturing processes via in-situ high-speed cameras are presented and discussed.
About: This article is published in Procedia CIRP.The article was published on 2021-01-01 and is currently open access. It has received None citations till now.
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TL;DR: The detection and localization of anomalous behaviors in crowded scenes is considered, and a joint detector of temporal and spatial anomalies is proposed, based on a video representation that accounts for both appearance and dynamics, using a set of mixture of dynamic textures models.
Abstract: The detection and localization of anomalous behaviors in crowded scenes is considered, and a joint detector of temporal and spatial anomalies is proposed. The proposed detector is based on a video representation that accounts for both appearance and dynamics, using a set of mixture of dynamic textures models. These models are used to implement 1) a center-surround discriminant saliency detector that produces spatial saliency scores, and 2) a model of normal behavior that is learned from training data and produces temporal saliency scores. Spatial and temporal anomaly maps are then defined at multiple spatial scales, by considering the scores of these operators at progressively larger regions of support. The multiscale scores act as potentials of a conditional random field that guarantees global consistency of the anomaly judgments. A data set of densely crowded pedestrian walkways is introduced and used to evaluate the proposed anomaly detector. Experiments on this and other data sets show that the latter achieves state-of-the-art anomaly detection results.
844 citations
TL;DR: In this paper, the authors proposed charting schemes based on quantilequantile plots and profile monitoring techniques to improve the detection performance of a conventional control chart in a data-rich environment.
Abstract: In-process sensors with huge sample size are becoming popular in the modern manufacturing industry, due to the increasing complexity of processes and products and the availability of advanced sensing technology. Under such a data-rich environment, a sample with huge size usually violates the assumption of homogeneity and degrades the detection performance of a conventional control chart. Instead of charting summary statistics such as the mean and standard deviation of observations that assume homogeneity within a sample, this paper proposes charting schemes based on the quantile–quantile (Q–Q) plot and profile monitoring techniques to improve the performance. Different monitoring schemes are studied based on various shift patterns in a huge sample and compared via simulation. Guidelines are provided for applying the proposed schemes to similar industrial applications in a data-rich environment. Copyright © 2005 John Wiley & Sons, Ltd.
127 citations
TL;DR: This work presents a novel solution to detect defects in nanofibrous materials by analyzing scanning electron microscope images using an algorithm that learns, during a training phase, a model yielding sparse representations of the structures that characterize correctly produced nan ofiborus materials.
Abstract: Nanoproducts represent a potential growing sector and nanofibrous materials are widely requested in industrial, medical, and environmental applications. Unfortunately, the production processes at the nanoscale are difficult to control and nanoproducts often exhibit localized defects that impair their functional properties. Therefore, defect detection is a particularly important feature in smart-manufacturing systems to raise alerts as soon as defects exceed a given tolerance level and to design production processes that both optimize the physical properties and control the defectiveness of the produced materials. Here, we present a novel solution to detect defects in nanofibrous materials by analyzing scanning electron microscope images. We employ an algorithm that learns, during a training phase, a model yielding sparse representations of the structures that characterize correctly produced nanofiborus materials. Defects are then detected by analyzing each patch of an input image and extracting features that quantitatively assess whether the patch conforms or not to the learned model. The proposed solution has been successfully validated over 45 images acquired from samples produced by a prototype electrospinning machine. The low computational times indicate that the proposed solution can be effectively adopted in a monitoring system for industrial production.
96 citations
TL;DR: This article shows how image data can be monitored using a spatiotemporal framework that is based on an extension of a generalized likelihood ratio control chart, and shows that this method is capable of quickly detecting the emergence of a fault.
Abstract: Machine vision systems are increasingly being used in industrial applications because of their ability to quickly provide information on product geometry, surface defects, surface finish, and other product and process characteristics. Previous research for monitoring these visual characteristics using image data has focused on either detecting changes within an image or between images. Extending these methods to include both the spatial and the temporal aspects of image data would provide more detailed diagnostic information, which would be of great value to industrial practitioners. Therefore, in this article, we show how image data can be monitored using a spatiotemporal framework that is based on an extension of a generalized likelihood ratio control chart. The performance of the proposed method is evaluated through computer simulations and experimental studies. The results show that our proposed spatiotemporal method is capable of quickly detecting the emergence of a fault. The computer simulations also show that our proposed generalized likelihood ratio control charting method provides a good estimate of the change point and the size/location of the fault, which are important fault diagnostic metrics that are not typically provided in the image monitoring literature. Finally, we highlight some research opportunities and provide some advice to practitioners. Copyright © 2012 John Wiley & Sons, Ltd.
84 citations