Digitalization and artificial intelligence in industry

In the last decades, we have witnessed many changes regarding the improvement of physical models and processes with positive impact on humanity, which we call Industrial Revolutions. In this paper, a description of the fundamental concepts of the last of these revolutions present until today is made, referring to the Fourth Industrial Revolution, called "Industry 4.0". In this work, a description of the State of the Art of the enabling technologies of this Industry 4.0 is presented, but because this topic is very broad, the description is limited to Artificial Intelligence and Cyber-Physical Systems applied to the industrial sector. Likewise, a case study is presented on how to carry out the design of an application and the analytics associated with a typical service in Industry 4.0, based on a cyber-physical system structure and supported by machine learning procedures. The methodology carried out to address this work consists of two stages. Firstly, an exploratory stage based on document search and management, mainly through academic research articles to identify, organize and describe the concepts and components involved in the application of artificial intelligence and cyber-physical systems in the industrial context. Secondly, a design and implementation stage of the logical part of an industrial maintenance application based on data processing in the Matlab platform. Finally, from the case study developed in the second stage, the results are presented using graphs and statistics to summarize the data obtained. And a conclussion section where the interpretation of the results and recomendations for future directions of the case of study are presented.En las últimas décadas, hemos presenciado muchos cambios respecto a la mejora de modelos y procesos físicos con repercusión positiva en la humanidad, a los que llamamos Revoluciones Industriales. En este trabajo, se realiza una descripción sobre conceptos fundamentales de la última de estas revoluciones presente hasta el día de hoy, refiriéndonos a la Cuarta Revolución Industrial, denominada “Industria 4.0”. Se presenta, en este trabajo, una descripción del Estado de Arte de las tecnologías habilitadoras de dicha Industria 4.0, pero debido a que este tema es muy amplio, se limita la descripción, a la Inteligencia Artificial y a los Sistemas Ciber-físicos aplicados al sector industrial. Asimismo, se presenta un caso de estudio sobre llevar a cabo el diseño de una aplicación y la analítica asociada a un servicio tipo en industria 4.0, basado en una estructura de sistema ciber-físico y soportado por procedimientos de machine learning. La metodología llevada a cabo para afrontar este trabajo, consta de dos etapas. En primer lugar, una etapa exploratoria a base de búsqueda y gestión documental, principalmente a través de artículos de investigación académica para identificar, organizar y describir los conceptos y componentes que intervienen en la aplicación de inteligencia artificial y sistemas ciber-físicos en el contexto industrial. En segundo lugar, una etapa de diseño e implementación de la parte lógica de una aplicación de mantenimiento industrial basada en el tratamiento de datos bajo plataforma Matlab. Finalmente, a partir del caso práctico desarrollado en la segunda etapa, se presentan los resultados mediante gráficos y estadísticas para resumir los datos obtenidos. Y una sección de conclusiones donde se presenta la interpretación de los resultados y recomendaciones para las direcciones futuras del caso de estudio.Incomin

Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook

UIDB/00066/2020The advent of the Industry 4.0 initiative has made it so that manufacturing environments are becoming more and more dynamic, connected but also inherently more complex, with additional inter-dependencies, uncertainties and large volumes of data being generated. Recent advances in Industrial Artificial Intelligence have showcased the potential of this technology to assist manufacturers in tackling the challenges associated with this digital transformation of Cyber-Physical Systems, through its data-driven predictive analytics and capacity to assist decision-making in highly complex, non-linear and often multistage environments. However, the industrial adoption of such solutions is still relatively low beyond the experimental pilot stage, as real environments provide unique and difficult challenges for which organizations are still unprepared. The aim of this paper is thus two-fold. First, a systematic review of current Industrial Artificial Intelligence literature is presented, focusing on its application in real manufacturing environments to identify the main enabling technologies and core design principles. Then, a set of key challenges and opportunities to be addressed by future research efforts are formulated along with a conceptual framework to bridge the gap between research in this field and the manufacturing industry, with the goal of promoting industrial adoption through a successful transition towards a digitized and data-driven company-wide culture. This paper is among the first to provide a clear definition and holistic view of Industrial Artificial Intelligence in the Industry 4.0 landscape, identifying and analysing its fundamental building blocks and ongoing trends. Its findings are expected to assist and empower researchers and manufacturers alike to better understand the requirements and steps necessary for a successful transition into Industry 4.0 supported by AI, as well as the challenges that may arise during this process.publishersversionepub_ahead_of_prin

knowlEdge Project –Concept, methodology and innovations for artificial intelligence in industry 4.0

AI is one of the biggest megatrends towards the 4th industrial revolution. Although these technologies promise business sustainability as well as product and process quality, it seems that the ever-changing market demands, the complexity of technologies and fair concerns about privacy, impede broad application and reuse of Artificial Intelligence (AI) models across the industry. To break the entry barriers for these technologies and unleash its full potential, the knowlEdge project will develop a new generation of AI methods, systems, and data management infrastructure. Subsequently, as part of the knowlEdge project we propose several major innovations in the areas of data management, data analytics and knowledge management including (i) a set of AI services that allows the usage of edge deployments as computational and live data infrastructure as well as a continuous learning execution pipeline on the edge, (ii) a digital twin of the shop-floor able to test AI models, (iii) a data management framework deployed along the edge-to-cloud continuum ensuring data quality, privacy and confidentiality, (iv) Human-AI Collaboration and Domain Knowledge Fusion tools for domain experts to inject their experience into the system, (v) a set of standardisation mechanisms for the exchange of trained AI models from one context to another, and (vi) a knowledge marketplace platform to distribute and interchange trained AI models. In this paper, we present a short overview of the EU Project knowlEdge –Towards Artificial Intelligence powered manufacturing services, processes, and products in an edge-to-cloud-knowledge continuum for humans [in-the-loop], which is funded by the Horizon 2020 (H2020) Framework Programme of the European Commission under Grant Agreement 957331. Our overview includes a description of the project’s main concept and methodology as well as the envisioned innovations.The research leading to these results has received funding from the Horizon 2020 Programme of the European Commission under Grant Agreement No. 957331 for EU Project knowlEdge –Towards Artificial Intelligence powered manufacturing services, processes, and products in an edge-to-cloud-knowledge continuum for humans [in-the-loop].Peer ReviewedTreball signat per 21 autors/autores: Sergio Alvarez-Napagao, Barcelona Supercomputing Center, Spain; Boki Ashmore, ICE, United Kingdom; Marta Barroso, Barcelona Supercomputing Center, Spain; Cristian Barrué, Barcelona Supercomputing Center, Spain; Christian Beecks, University of Münster, Germany; Fabian Berns, University of Münster, Germany; Ilaria Bosi, LINKS Foundation, Italy; Sisay Adugna Chala, Fraunhofer FIT, Germany; Nicola Ciulli, Nextworks, Italy; Marta Garcia-Gasulla, Barcelona Supercomputing Center, Spain; Alexander Grass, Fraunhofer FIT, Germany; Dimosthenis Ioannidis, CERTH/ITI, Greece; Natalia Jakubiak, Universitat Politècnica de Catalunya, Spain; Karl Köpke, Kautex Textron, Germany; Ville Lämsä, VTT Technical Research Centre, Finland; Pedro Megias, Barcelona Supercomputing Center, Spain; Alexandros Nizamis, CERTH/ITI, Greece; Claudio Pastrone, LINKS Foundation, Italy; Rosaria Rossini, LINKS Foundation, Italy; Miquel Sànchez-Marrè, Universitat Politècnica de Catalunya, Spain; Luca Ziliotti, Parmalat, ItalyPostprint (author's final draft

Robotic process automation and artificial intelligence in industry 4.0 : a literature review

aking into account the technological evolution of the last decades and the proliferation of information systems in society, today we see the vast majority of services provided by companies and institutions as digital services. Industry 4.0 is the fourth industrial revolution where technologies and automation are asserting themselves as major changes. Robotic Process Automation (RPA) has numerous advantages in terms of automating organizational and business processes. Allied to these advantages, the complementary use of Artificial Intelligence (AI) algorithms and techniques allows to improve the accuracy and execution of RPA processes in the extraction of information, in the recognition, classification, forecasting and optimization of processes. In this context, this paper aims to present a study of the RPA tools associated with AI that can contribute to the improvement of the organizational processes associated with Industry 4.0. It appears that the RPA tools enhance their functionality with the objectives of AI being extended with the use of Artificial Neural Network algorithms, Text Mining techniques and Natural Language Processing techniques for the extraction of information and consequent process of optimization and of forecasting scenarios in improving the operational and business processes of organizations.5311-8814-F0ED | Sara Maria da Cruz Maia de Oliveira PaivaN/

Scientometric analysis in the field of big data and artificial intelligence in industry

Big Data and Artificial Intelligence (BD&AI) in Industry have grown so prevalent, and the potential they provide is so revolutionary that they are seen as critical for competitive growth. Because the number of organizations BD&AI on Industry technology is increasing exponentially, so is the need for BD&AI on Industry practitioners. Until we conducted this research, only 1399 academic documents on BD&AI in Industry found from 2002 to 2020 were obtained by searching the Scopus database. BD&AI in the industrial sector is examined in-depth in this paper. This study uses bibliometric analysis and indexed digital methods to map scientific publications worldwide. This study uses the Scopus database to collect information and online analysis via the Scopus website and VOSViewer to demonstrate bibliometric network mapping. We use the article selection process, starting with the keywords to be searched for, the year limitation, then the database is exported into RIS and CSV format files. From the database, we also perform network mapping using VOSViewer. Researchers in China have the most articles published and indexed by Scopus among the most prolific authors (373), followed by the United States (239) and India with 125 academic publications. Data analysis reveals an upward trend in the number of worldwide publications in BD&AI in Industry, as measured by the Scopus index

Evaluating Generalization, Bias, and Fairness in Deep Learning for Metal Surface Defect Detection:A Comparative Study

In recent years, deep learning models have led to improved accuracy in industrial defect detection, often using variants of YOLO (You Only Look Once), due to its high performance at a low cost. However, the generalizability, fairness and bias of their outcomes have not been examined, which may lead to overconfident predictions. Additionally, the complexity added by co-occurring defects, single and multi-class defects, and the effect on training, is not taken into consideration. This study addresses these critical gaps by introducing new methodologies for analyzing dataset complexity and evaluating model fairness. It introduces the novel approach of co-occurrence impact analysis, examining how the co-occurrence of defects in sample images affects performance, and introducing new dimensions to dataset preparation and training. Its aim is to increase model robustness in the face of real-world scenarios where multiple defects often appear together. Our study also innovates in the evaluation of model fairness by adapting the disparate impact ratio (DIR) to consider the true positive rate (TPR) across different groups and modifying the predictive parity difference (PPD) metric to focus on biases present in industrial quality control. Experiments demonstrate by cross-validation that the model trained on combined datasets significantly outperforms others in accuracy without overfitting and results in increased fairness, as validated by our novel fairness metrics. Explainability also provides valuable insights on the effects of different training regimes, notably absent in prior works. This work not only advances the field of deep learning for defect detection but also provides a strategic framework for future advancements, emphasizing the need for balanced datasets and considerations of ethics, fairness, bias and generalizability in the deployment of artificial intelligence in industry.</p