An artificial intelligence-based collaboration approach in industrial IoT manufacturing : key concepts, architectural extensions and potential applications

The digitization of manufacturing industry has led to leaner and more efficient production, under the Industry 4.0 concept. Nowadays, datasets collected from shop floor assets and information technology (IT) systems are used in data-driven analytics efforts to support more informed business intelligence decisions. However, these results are currently only used in isolated and dispersed parts of the production process. At the same time, full integration of artificial intelligence (AI) in all parts of manufacturing systems is currently lacking. In this context, the goal of this manuscript is to present a more holistic integration of AI by promoting collaboration. To this end, collaboration is understood as a multi-dimensional conceptual term that covers all important enablers for AI adoption in manufacturing contexts and is promoted in terms of business intelligence optimization, human-in-the-loop and secure federation across manufacturing sites. To address these challenges, the proposed architectural approach builds on three technical pillars: (1) components that extend the functionality of the existing layers in the Reference Architectural Model for Industry 4.0; (2) definition of new layers for collaboration by means of human-in-the-loop and federation; (3) security concerns with AI-powered mechanisms. In addition, system implementation aspects are discussed and potential applications in industrial environments, as well as business impacts, are presented

Discovering phase and causal dependencies on manufacturing processes

Discovering phase and causal dependencies on manufacturing processes. Keyword machine learning, causality, Industry 4.

Energy efficiency in discrete-manufacturing systems: insights, trends, and control strategies

Since the depletion of fossil energy sources, rising energy prices, and governmental regulation restrictions, the current manufacturing industry is shifting towards more efficient and sustainable systems. This transformation has promoted the identification of energy saving opportunities and the development of new technologies and strategies oriented to improve the energy efficiency of such systems. This paper outlines and discusses most of the research reported during the last decade regarding energy efficiency in manufacturing systems, the current technologies and strategies to improve that efficiency, identifying and remarking those related to the design of management/control strategies. Based on this fact, this paper aims to provide a review of strategies for reducing energy consumption and optimizing the use of resources within a plant into the context of discrete manufacturing. The review performed concerning the current context of manufacturing systems, control systems implemented, and their transformation towards Industry 4.0 might be useful in both the academic and industrial dimension to identify trends and critical points and suggest further research lines.Peer ReviewedPreprin

Fast human motion prediction for human-robot collaboration with wearable interfaces

In this paper, we aim at improving human motion prediction during human-robot collaboration in industrial facilities by exploiting contributions from both physical and physiological signals. Improved human-machine collaboration could prove useful in several areas, while it is crucial for interacting robots to understand human movement as soon as possible to avoid accidents and injuries. In this perspective, we propose a novel human-robot interface capable to anticipate the user intention while performing reaching movements on a working bench in order to plan the action of a collaborative robot. The proposed interface can find many applications in the Industry 4.0 framework, where autonomous and collaborative robots will be an essential part of innovative facilities. A motion intention prediction and a motion direction prediction levels have been developed to improve detection speed and accuracy. A Gaussian Mixture Model (GMM) has been trained with IMU and EMG data following an evidence accumulation approach to predict reaching direction. Novel dynamic stopping criteria have been proposed to flexibly adjust the trade-off between early anticipation and accuracy according to the application. The output of the two predictors has been used as external inputs to a Finite State Machine (FSM) to control the behaviour of a physical robot according to user's action or inaction. Results show that our system outperforms previous methods, achieving a real-time classification accuracy of $94.3\pm2.9\%$ after $160.0msec\pm80.0msec$ from movement onset

IoT and Industry 4.0 technologies in Digital Manufacturing Transformation

The evolution of internet of things, cyber physical system, digital twin and artificial intelligence is stimulating the transformation of the product-centric processes toward smart control digital service-oriented ones. With the implementation of artificial intelligence and machine learning algorithms, IoT has accelerated the movement from connecting devices to the Internet to collecting and analyzing data by using sensors to extract data throughout the lifecycle of the product, in order to create value and knowledge from the huge amount of the collected data, such as the knowledge of the product performance and conditions. The importance of internet of things technology in manufacturing comes from its ability to collect real time data and extract valuable knowledge from these huge amount of data which can be supported through the implementation of smart IoT-based servitization framework which was presented in this research together with a 10-steps approach diagram. Moreover, literature review has been carried out to develop the research and deepen the knowledge in the field of IoT, CPS, DT and Artificial Intelligence, and then interviews with experts have been conducted to validate the contents, since DT is a quite new technology, so there are different points of view about certain concepts of this technology. The main scope and objective of this research is to allow organizational processes and companies to benefit form the value added information that can be achieved through the right implementation of advanced technologies such as IoT, DT, CPS, and artificial intelligence which can provide financial benefits to the manufacturing companies and competitive advantages to make them stand among the other competitors in the market. The effectiveness of such technologies can not only improve the financial benefits of the companies, but the workers\u2019 safety and health through the real time monitoring of the work environment. Here in this research the main aim is to present the right frameworks that can be used in the literature through companies and researchers to allow them to implement these technologies correctly in the boundaries of their businesses. In addition to that, the Smart factory concept, as introduced in the context of Industry 4.0, promotes the development of a new interconnected manufacturing environment where human operators cooperate with machines. While the role of the operator in the smart factory is substantially being rediscussed, the industrial approach towards safety and ergonomics still appears frequently outdated and inadequate. This research approaches such topic referring to the vibration risk, a well-known cause of work-related pathologies, and proposes an original methodology for mapping the risk exposure related to the performed activities. A miniaturized wearable device is employed to collect vibration data, and the obtained signals are segmented and processed in order to extract the significant features. An original machine learning classifier is then employed to recognize the worker\u2019s activity and evaluate the related exposure to vibration risks. Finally, the results obtained from the experimental analysis demonstrate feasibility and the effectiveness of the proposed methodology

FullExpression - Emotion Recognition Software

During human evolution emotion expression became an important social tool that contributed to the complexification of societies. Human-computer interaction is commonly present in our daily life, and the industry is struggling for solutions that can analyze human emotions, in an attempt to provide better experiences. The purpose of this study was to understand if a software built using the transfer-learning technique on a deep learning model was capable of classifying human emotions, through facial expression analysis. A Convolutional Neuronal Network model was trained and used in a web application, which is available online. Several tools were created to facilitate the software development process, including the training and validation processes, and these are also available online. The data was collected after the combination of several facial expression emotion databases, such as KDEF_AKDEF, TFEID, Face_Place and jaffe. Software evaluation reveled an accuracy in identifying the correct emotions close to 80%. In addition, a comparison between the software and preliminary data from human’s performance, on recognizing facial expressed emotions, suggested that the software performed better. This work can be useful in many different domains such as marketing (to understand the effect of marketing campaigns on people’s emotional states), health (to help mental diseases diagnosis) and industry 4.0 (to create a better collaborating environment between humans and machines).Durante a evolução da espécie humana, a expressões de emoções tornou-se uma ferramenta social importante, que permitiu a criação de sociedades cada vez mais complexas. A interação entre humanos e máquinas acontece regularmente, evidenciando a necessidade da indústria desenvolver soluções que possam analisar emoções, de modo a proporcionar melhores experiências aos utilizadores. O propósito deste trabalho foi perceber se soluções de software desenvolvidas a partir da técnica de transfer-learning são capazes de classificar emoções humanas, a partir da análise de expressões faciais. Um modelo que implementa a arquitetura Convolutional Neuronal Network foi escolhido para ser treinado e utilizado na aplicação web desenvolvida neste trabalho, que está disponível online. A par da aplicação web, diferentes ferramentas foram criadas de forma a facilitar o processo de criação e avaliação de modelos Deep Learning, e estas também estão disponíveis online. Os dados foram recolhidos após a combinação de várias bases de dados de expressões de emoções (KDEF_AKDEF, TFEID, Face_Place and jaffe). A avaliação do software demostrou uma precisão na classificação de emoções próxima dos 80%. Para além disso, uma comparação entre o software e dados preliminares relativos ao reconhecimento de emoções por pessoas sugere que o software é melhor a classificar emoções. Os resultados deste trabalho podem aplicados em diversas áreas, como a publicidade (de forma a perceber os efeitos das campanhas no estado emocional das pessoas), a saúde (para um melhor diagnóstico de doenças mentais) e na indústria 4.0 (de forma a criar um melhor ambiente de colaboração entre humanos e máquinas)

Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions

Digital twin reference model development to prevent operators' risk in process plants

In the literature, many applications of Digital Twin methodologies in the manufacturing, construction and oil and gas sectors have been proposed, but there is still no reference model specifically developed for risk control and prevention. In this context, this work develops a Digital Twin reference model in order to define conceptual guidelines to support the implementation of Digital Twin for risk prediction and prevention. The reference model proposed in this paper is made up of four main layers (Process industry physical space, Communication system, Digital Twin and User space), while the implementation steps of the reference model have been divided into five phases (Development of the risk assessment plan, Development of the communication and control system, Development of Digital Twin tools, Tools integration in a Digital Twin perspective and models and Platform validation). During the design and implementation phases of a Digital Twin, different criticalities must be taken into consideration concerning the need for deterministic transactions, a large number of pervasive devices, and standardization issues. Practical implications of the proposed reference model regard the possibility to detect, identify and develop corrective actions that can affect the safety of operators, the reduction of maintenance and operating costs, and more general improvements of the company business by intervening both in strictly technological and organizational terms