Smart augmented reality instructional system for mechanical assembly

Quality and efficiency are pivotal indicators of a manufacturing company. Many companies are suffering from shortage of experienced workers across the production line to perform complex assembly tasks such as assembly of an aircraft engine. This could lead to a significant financial loss. In order to further reduce time and error in an assembly, a smart system consisting of multi-modal Augmented Reality (AR) instructions with the support of a deep learning network for tool detection is introduced. The multi-modal smart AR is designed to provide on-site information including various visual renderings with a fine-tuned Region-based Convolutional Neural Network, which is trained on a synthetic tool dataset. The dataset is generated using CAD models of tools augmented onto a 2D scene without the need of manually preparing real tool images. By implementing the system to mechanical assembly of a CNC carving machine, the result has shown that the system is not only able to correctly classify and localize the physical tools but also enables workers to successfully complete the given assembly tasks. With the proposed approaches, an efficiently customizable smart AR instructional system capable of sensing, characterizing the requirements, and enhancing worker\u27s performance effectively has been built and demonstrated --Abstract, page iii

Worker Activity Recognition in Smart Manufacturing Using IMU and sEMG Signals with Convolutional Neural Networks

In a smart manufacturing system involving workers, recognition of the worker\u27s activity can be used for quantification and evaluation of the worker\u27s performance, as well as to provide onsite instructions with augmented reality. In this paper, we propose a method for activity recognition using Inertial Measurement Unit (IMU) and surface electromyography (sEMG) signals obtained from a Myo armband. The raw 10-channel IMU signals are stacked to form a signal image. This image is transformed into an activity image by applying Discrete Fourier Transformation (DFT) and then fed into a Convolutional Neural Network (CNN) for feature extraction, resulting in a high-level feature vector. Another feature vector representing the level of muscle activation is evaluated with the raw 8-channel sEMG signals. Then these two vectors are concatenated and used for work activity classification. A worker activity dataset is established, which at present contains 6 common activities in assembly tasks, i.e., grab tool/part, hammer nail, use power-screwdriver, rest arm, turn screwdriver, and use wrench. The developed CNN model is evaluated on this dataset and achieves 98% and 87% recognition accuracy in the half-half and leave-one-out experiments, respectively

Phase transformation-induced improvement in hardness and high-temperature wear resistance of plasma-sprayed and remelted NiCrBSi/WC coatings

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. The remelting method is introduced to improve the properties of the as-sprayed NiCrBSi coatings. In this work, tungsten carbide (WC) was selected as reinforcement and the as-sprayed and remelted NiCrBSi/WC composite coatings were investigated by X-ray diffraction, scanning electron microscopy, hardness test and tribology test. After spraying, WC particles are evenly distributed in the coating. The remelting process induced the decarburizing reaction of WC, resulting in the formation of dispersed W2 C. The dispersed W2 C particles play an important role in the dispersion strengthening. Meanwhile, the pores and lamellar structures are eliminated in the remelted NiCrBSi/WC composite coating. Due to these two advantages, the hardness and the high-temperature wear resistance of the remelted NiCrBSi/WC composite coating are significantly improved compared with those with an as-sprayed NiCrBSi coating; the as-sprayed NiCrBSi coating, as-sprayed NiCrBSi/WC composite coating and remelted NiCrBSi/WC composite coating have average hardness of 673.82 HV, 785.14 HV, 1061.23 HV, and their friction coefficients are 0.3418, 0.3261, 0.2431, respectively. The wear volume of the remelted NiCrBSi/WC composite coating is only one-third of that of the as-sprayed NiCrBSi coating

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

FAST CLASSIFICATION OF LEAF IMAGES FOR AGRICULTURAL REMOTE SENSING APPLICATIONS

This paper introduces a method of classifying leaves using machine learning. Considerable emphasis has been put on leaf classification for use in remote sensing applications such as plant phenotyping and precision agriculture. Convolutional neural networks (CNN) have been extensively used in computer vision for image classification. However, CNN can be computationally expensive. This paper describes a method that achieves a comparable accuracy, with a lower computational burden, using a support vector machine (SVM) classifier. This method uses image processing algorithms to extract features from Hough transform and Hough Lines. These features are then integrated with those extracted from binary images, and “eigenleaves” extracted from grayscale, gradient, and different color-space images of leaves as data preprocessing for classification. The classifier is implemented on two publicly available datasets: Flavia and Swedish; and is able to achieve state-of-the-art accuracies using a SVM classifier.International Foundation for TelemeteringProceedings from the International Telemetering Conference are made available by the International Foundation for Telemetering and the University of Arizona Libraries. Visit http://www.telemetry.org/index.php/contact-us if you have questions about items in this collection

Manufacturing Assembly Simulations in Virtual and Augmented Reality

The following sections are included:IntroductionMethodologies for Assembly Simulations in VR/ARApplication and Examples of MAS using VR/ARA Case StudyTechnology Limitations and Research NeedsConclusionReference

Smart Augmented Reality Instructional System for Mechanical Assembly towards Worker-Centered Intelligent Manufacturing

Quality and efficiency are crucial indicators of any manufacturing company. Many companies are suffering from a shortage of experienced workers across the production line to perform complex assembly tasks. To reduce time and error in an assembly task, a worker-centered system consisting of multi-modal Augmented Reality (AR) instructions with the support of a deep learning network for tool detection is introduced. The integrated AR is designed to provide on-site instructions including various visual renderings with a fine-tuned Region-based Convolutional Neural Network, which is trained on a synthetic tool dataset. The dataset is generated using CAD models of tools and displayed onto a 2D scene without using real tool images. By experimenting the system to a mechanical assembly of a CNC carving machine, the result of a designed experiment shows that the system helps reduce the time and errors of the given assembly tasks by 33.2 % and 32.4 %, respectively. With the integrated system, an efficient, customizable smart AR instruction system capable of sensing, characterizing requirements, and enhancing worker\u27s performance has been built and demonstrated

A Self-Aware and Active-Guiding Training & Assistant System for Worker-Centered Intelligent Manufacturing

Training and on-site assistance is critical to help workers master required skills, improve worker productivity, and guarantee the product quality. Traditional training methods lack worker-centered considerations that are particularly in need when workers are facing ever-changing demands. In this study, we propose a worker-centered training & assistant system for intelligent manufacturing, which is featured with self-awareness and active-guidance. Multi-modal sensing techniques are applied to perceive each individual worker and a deep learning approach is developed to understand the worker\u27s behavior and intention. Moreover, an object detection algorithm is implemented to identify the parts/tools the worker is interacting with. Then the worker\u27s current state is inferred and used for quantifying and assessing the worker performance, from which the worker\u27s potential guidance demands are analyzed. Furthermore, onsite guidance with multi-modal augmented reality is provided actively and continuously during the operational process. Two case studies are used to demonstrate the feasibility and great potential of our proposed approach and system for applying to the manufacturing industry for frontline workers

Motion of self-rewetting drop on a substrate with a constant temperature gradient

We investigate the dynamics of a self-rewetting drop placed on a substrate with a constant temperature gradient via three-dimensional numerical simulations using a conservative level-set approach to track the interface of the drop. The surface tension of a so-called self-rewetting fluid exhibits a parabolic dependence on temperature with a well-defined minimum. Two distinct drop behaviours, namely deformation and elongation, are observed when it is placed at the location of the minimum surface tension. The drop spreads slightly and reaches a pseudo-steady state in the deformation regime, while it continuously spreads until breakup in the elongation regime. Theoretical models based on the forces exerted on the drop have been developed to predict the critical condition at which the drop undergoes the transition between the two regimes, and the predictions are in good agreement with the numerical results. We also investigate the effect of the initial position of the drop with respect to the location of the minimum surface tension on the spreading and migration dynamics. It is found that, at early times, the migration of the drop obeys an exponential function with time, but it diverges at the later stage due to an increase in the drop deformation