Additive Manufacturing (AM) technology provides new opportunities to automatically and flexibly fabricate parts with complicated shapes architectures that could not be produced by conventional manufacturing processes, thus enabling unprecedented design flexibilities application opportunities. The lattice structure possesses many superior properties solid material structures. It is able integrate more than one function into a physical part, which makes it attractive wide range of...

The common modeling of digital twins uses an information model to describe the physical machines. integration into productive cyber-physical cloud manufacturing (CPCM) systems imposes strong demands such as reducing overhead and saving resources. In this paper, we develop investigate a new method for building cloud-based (CBDT), which can be adapted CPCM platform. Our helps reduce computing resources in processing center efficient interactions between human users We introduce knowledge...

In a smart manufacturing system involving workers, recognition of the worker’s activity can be used for quantification and evaluation performance, as well to provide onsite instructions with augmented reality. this paper, we propose method using Inertial Measurement Unit (IMU) surface electromyography (sEMG) signals obtained from Myo armband. The raw 10-channel IMU are stacked form signal image. This image is transformed into an by applying Discrete Fourier Transformation (DFT) then fed...

Production innovations are occurring faster than ever. Manufacturing workers thus need to frequently learn new methods and skills. In fast changing, largely uncertain production systems, manufacturers with the ability comprehend workers’ behavior assess their operation performance in near real-time will achieve better peers. Action recognition can serve this purpose. Despite that human action has been an active field of study machine learning, limited work done for recognizing worker actions...

Zero-shot learning (ZSL) typically leverages semantic knowledge and textual descriptions of classes to forge connections between seen unseen classes. ZSL can classify new categories data in the training set. Prior research has focused on aligning image features with their corresponding auxiliary information, overlooking limitation whereby individual may not capture full spectrum information inherent original image. Additionally, there are concerns regarding bias predicted results towards...

In a human-centered intelligent manufacturing system, every element is to assist the operator in achieving optimal operational performance. The primary task of developing such system accurately understand human behavior. this paper, we propose fog computing framework for assembly operation recognition, which brings power close data source order achieve real-time recognition. For collection, operator's activity captured using visual cameras from different perspectives. instead directly...

Insufficient illumination makes driver face detection at night challenging. This paper proposes an adaptive attenuation quantification retinex (AAQR) method to enhance the details of nighttime images. There are three phases in this method: restriction, prediction, and quantification. The performance proposed was evaluated by employing a robust via sparse representation. collected images were categorized into groups (up-down, left-right, mixed) according distribution each image. Results have...

Assembly carries paramount importance in manufacturing. Being able to support workers real time maximize their positive contributions assembly is a tremendous interest of manufacturers. Human action recognition has been way automatically analyze and understand worker actions real-time assistance for facilitate worker–machine collaboration. are distinct from activities that have well studied the literature. Actions taken by intricate, variable, may involve very fine motions. Therefore,...

Human Activity Recognition (HAR) using wearable devices such as smart watches embedded with Inertial Measurement Unit (IMU) sensors has various applications relevant to our daily life, workout tracking and health monitoring. In this paper, we propose a novel attention-based approach human activity recognition multiple IMU worn at different body locations. Firstly, sensor-wise feature extraction module is designed extract the most discriminative features from individual Convolutional Neural...

Abstract With the development of industrial automation and artificial intelligence, robotic systems are developing into an essential part factory production, human-robot collaboration (HRC) becomes a new trend in field. In our previous work, ten dynamic gestures have been designed for communication between human worker robot manufacturing scenarios, gesture recognition model based on Convolutional Neural Networks (CNN) has developed. Based model, this study aims to design develop real-time...

Abstract Human-robot collaboration (HRC) is a challenging task in modern industry and gesture communication HRC has attracted much interest. This paper proposes demonstrates dynamic recognition system based on Motion History Image (MHI) Convolutional Neural Networks (CNN). Firstly, ten gestures are designed for human worker to communicate with an industrial robot. Secondly, the MHI method adopted extract features from video clips generate static images of as inputs CNN. Finally, CNN model...

In a human-centered intelligent manufacturing system, sensing and understanding of the worker's activity are primary tasks. this paper, we propose novel multi-modal approach for worker recognition by leveraging information from different sensors in modalities. Specifically, smart armband visual camera applied to capture Inertial Measurement Unit (IMU) signals videos, respectively. For IMU signals, design two feature transform mechanisms, both frequency spatial domains, assemble captured as...