Artificial Intelligence Advancements for Digitising Industry

In the digital transformation era, when flexibility and know-how in manufacturing complex products become a critical competitive advantage, artificial intelligence (AI) is one of the technologies driving the digital transformation of industry and industrial products. These products with high complexity based on multi-dimensional requirements need flexible and adaptive manufacturing lines and novel components, e.g., dedicated CPUs, GPUs, FPGAs, TPUs and neuromorphic architectures that support AI operations at the edge with reliable sensors and specialised AI capabilities. The change towards AI-driven applications in industrial sectors enables new innovative industrial and manufacturing models. New process management approaches appear and become part of the core competence in the organizations and the network of manufacturing sites. In this context, bringing AI from the cloud to the edge and promoting the silicon-born AI components by advancing Moore’s law and accelerating edge processing adoption in different industries through reference implementations becomes a priority for digitising industry. This article gives an overview of the ECSEL AI4DI project that aims to apply at the edge AI-based technologies, methods, algorithms, and integration with Industrial Internet of Things (IIoT) and robotics to enhance industrial processes based on repetitive tasks, focusing on replacing process identification and validation methods with intelligent technologies across automotive, semiconductor, machinery, food and beverage, and transportation industries.publishedVersio

Trusted Artificial Intelligence in Manufacturing; Trusted Artificial Intelligence in Manufacturing

The successful deployment of AI solutions in manufacturing environments hinges on their security, safety and reliability which becomes more challenging in settings where multiple AI systems (e.g., industrial robots, robotic cells, Deep Neural Networks (DNNs)) interact as atomic systems and with humans. To guarantee the safe and reliable operation of AI systems in the shopfloor, there is a need to address many challenges in the scope of complex, heterogeneous, dynamic and unpredictable environments. Specifically, data reliability, human machine interaction, security, transparency and explainability challenges need to be addressed at the same time. Recent advances in AI research (e.g., in deep neural networks security and explainable AI (XAI) systems), coupled with novel research outcomes in the formal specification and verification of AI systems provide a sound basis for safe and reliable AI deployments in production lines. Moreover, the legal and regulatory dimension of safe and reliable AI solutions in production lines must be considered as well. To address some of the above listed challenges, fifteen European Organizations collaborate in the scope of the STAR project, a research initiative funded by the European Commission in the scope of its H2020 program (Grant Agreement Number: 956573). STAR researches, develops, and validates novel technologies that enable AI systems to acquire knowledge in order to take timely and safe decisions in dynamic and unpredictable environments. Moreover, the project researches and delivers approaches that enable AI systems to confront sophisticated adversaries and to remain robust against security attacks. This book is co-authored by the STAR consortium members and provides a review of technologies, techniques and systems for trusted, ethical, and secure AI in manufacturing. The different chapters of the book cover systems and technologies for industrial data reliability, responsible and transparent artificial intelligence systems, human centered manufacturing systems such as human-centred digital twins, cyber-defence in AI systems, simulated reality systems, human robot collaboration systems, as well as automated mobile robots for manufacturing environments. A variety of cutting-edge AI technologies are employed by these systems including deep neural networks, reinforcement learning systems, and explainable artificial intelligence systems. Furthermore, relevant standards and applicable regulations are discussed. Beyond reviewing state of the art standards and technologies, the book illustrates how the STAR research goes beyond the state of the art, towards enabling and showcasing human-centred technologies in production lines. Emphasis is put on dynamic human in the loop scenarios, where ethical, transparent, and trusted AI systems co-exist with human workers. The book is made available as an open access publication, which could make it broadly and freely available to the AI and smart manufacturing communities

Neuro-symbolic Empowered Denoising Diffusion Probabilistic Models for Real-time Anomaly Detection in Industry 4.0

Industry 4.0 involves the integration of digital technologies, such as IoT, Big Data, and AI, into manufacturing and industrial processes to increase efficiency and productivity. As these technologies become more interconnected and interdependent, Industry 4.0 systems become more complex, which brings the difficulty of identifying and stopping anomalies that may cause disturbances in the manufacturing process. This paper aims to propose a diffusion-based model for real-time anomaly prediction in Industry 4.0 processes. Using a neuro-symbolic approach, we integrate industrial ontologies in the model, thereby adding formal knowledge on smart manufacturing. Finally, we propose a simple yet effective way of distilling diffusion models through Random Fourier Features for deployment on an embedded system for direct integration into the manufacturing process. To the best of our knowledge, this approach has never been explored before.Comment: Accepted at the 26th Forum on specification and Design Languages (FDL 2023

Multi-agent Manufacturing Execution System (MES):Concept, architecture & ML algorithm for a smart factory case

Smart factory of the future is expected to support interoperability on the shop floor, where information systems are pivotal in enabling interconnectivity between its physical assets. In this era of digital transformation, manufacturing execution system (MES) is emerging as a critical software tool to support production planning and control while accessing the shop floor data. However, application of MES as an enterprise information system still lacks the decision support capabilities on the shop floor. As an attempt to design intelligent MES, this paper demonstrates one of the artificial intelligence (AI) applications in the manufacturing domain by presenting a decision support mechanism for MES aimed at production coordination. Machine learning (ML) was used to develop an anomaly detection algorithm for multi-agent based MES to facilitate autonomous production execution and process optimization (in this paper switching the machine off after anomaly detection on the production line). Thus, MES executes the ‘turning off’ of the machine without human intervention. The contribution of the paper includes a concept of next-generation MES that has embedded AI, i.e., a MES system architecture combined with machine learning (ML) technique for multi-agent MES. Future research directions are also put forward in this position paper

The role of artificial intelligence in skilled work and consequences for vocational training

Artificial intelligence (AI) has long been a present-day topic and is having an impact on the economy, society, skilled work and the work environment. However, there are often very different assessments of the effects: On the one hand the loss of jobs and even professions has been predicted, on the other hand new support and shaping options for work are emerging. In addition, AI is treated as a powerful buzzword without considering the real technologies and requirements behind it. Nevertheless, consequences for the world of work and its employees can only be derived and vocational training concepts designed if the handling of AI in skilled work has been concretized beforehand. The impact of AI on vocational education and training and on the skilled worker has so far been discussed in a rather abstract way and only very rarely focused on research. At the same time, technological developments in certain areas (including expert systems, machine learning approaches, digital twins) have already proceeded to such an extent that the effects on skilled work are noticeable and are thus evident. Much will depend on the design of the human-machine interface. In order to evaluate how skilled labour and AI can successfully “cooperate” in manufacturing, a model is presented here that can support the evaluation process

AI based state observer for optimal process control: application to digital twins of manufacturing plants

Les plantes de fabricació estan subjectes a restriccions dinàmiques que requereixen una optimització robusta per millorar el rendiment i l' eficiència del sistema. En aquest projecte es presenta un nou sistema de control òptim basat en IA per a un bessó digital d' una planta de fabricació. El sistema proposat implementa un observador d' estat basat en IA per predir l' estat intern d' un model de procés altament incert i no lineal, tal com seria un sistema de producció real. Una funció d' optimització multi-objectiu es utilitzada per controlar els paràmetres de producció i mantenir el procés funcionant en condicions òptimes. El mètode d'Optimització del Control basat en AI es va implementar en un cas d'estudi d'una planta de fabricació d'acer. El rendiment del sistema es va avaluar utilitzant els KPIs de fabricació rellevants, com ara les taxes d'utilització i productivitat de l'equip del procés. L'ús de sistema de control optimitzat via AI millora amb èxit els KPIs de procés i potencialment podria reduir els costos de producció.Las plantas de fabricación están sujetas a restricciones dinámicas que requieren una optimización robusta para mejorar el rendimiento y la eficiencia. En este informe se presenta un nuevo sistema de control óptimo basado en IA para un gemelo digital de una planta de fabricación. El sistema propuesto implementa un observador de estado basado en IA para predecir el estado interno de un modelo de proceso altamente incierto y no lineal, tal y como sería un sistema de producción real. Una función de optimización multiobjetivo es utilizada para controlar los parámetros de producción y mantener el proceso funcionando en condiciones óptimas. El método de Optimización del Control basado en AI se implementó en un caso de estudio de una planta de fabricación de acero. El rendimiento del sistema se evaluó utilizando los KPIs de fabricación relevantes, como la utilización del equipo y las tasas de productividad del proceso. El uso del sistema de control óptimo de IA mejora los KPIs del proceso y podría reducir potencialmente los costos de producción.Manufacturing plants are subject to dynamic constrains requiring robust optimization methods for improved performance and efficiency. A novel AI based optimal control system for a Digital Twin of a manufacturing plant is presented in this report. The proposed system implements an AI based state observer to predict the internal state of a highly uncertain and non-linear process model, such as a real production system. A multi-objective optimization function is used to control production parameters and keeps the process running at an optimal condition. The AI Optimization Control method was implemented on a study case on a steel manufacturing plant. The performance of the system was evaluated using the relevant manufacturing KPIs such as the equipment utilization and productivity rates of the process. The use of the AI optimal control system successfully improves the process KPIs and could potentially reduce production costs

Regulating AI at work: labour relations, automation, and algorithmic management

Recent innovations in artificial intelligence (AI) have been at the core of massive technological changes that are transforming work. AI is now widely used to automate business processes and replace labour-intensive tasks while changing the skill demands for those that remain. AI-based tools are also deployed to invasively monitor worker conduct and to automate HR management processes. Through the dual lens of comparative labour law and employment relations research, the articles in this special issue of Transfer investigate the role of collective bargaining and government policy in shaping strategies to deploy new digital and AI-based technologies at work. Together, they give new insight into the conditions for encouraging broadly shared benefits from technological innovation while mitigating harm to workers and society. The first section of this issue includes articles comparing union and policy responses to AI at the national level. De Stefano and Taes draw on research in eight EU countries to examine the risks of AI and automated decision-making systems, and union and regulatory strategies to address these risks. Collins and Atkinson discuss the intersection between legal frameworks, collective bargaining, and employers’ algorithmic management choices in ‘post-Brexit Britain’. Krzywdzinski et al. analyse how these issues play out in the more strongly regulated German system, discussing not only the risks to workers, but also how worker voice helps address the challenges management faces in implementing AI at work. Hassel and Özkiziltan also focus on Germany, but they present a more differentiated analysis of how effective responses may differ depending on the type of risk AI poses for work. Molina et al. conclude this section with a broader comparative analysis of policy and union responses to AI and algorithms in Denmark, Germany, Hungary, and Spain. The articles in the second section are based on comparative case studies, allowing the authors to examine how and why worker representatives’ strategies and bargaining power differ across countries. Doellgast et al. compare union and works council responses to algorithmic management in two telecommunications companies in Germany and Norway. Pulignano et al. examine union strategies toward the linked digital and green transitions in the German and Belgian auto industries. Finally, Garneau et al. compare union responses to digitalisation in aerospace manufacturing in Wallonia, Denmark, and Quebec. These three articles show that unions and works councils had most influence over technology-related decisions when they could draw on formal bargaining rights and encompassing collective agreements; as well as a network of intermediary institutions that support knowledge and strategy development and exchange. Together, the articles make a strong case that efforts to better regulate the use of AI and algorithms at work are likely to be most effective where they are underpinned by, and supportive of, social dialogue. Individual legal protections are blunt instruments without mechanisms that also strengthen worker voice in, and oversight over, how technologies are implemented. Collective labour rights are the most effective tools to give workers real voice in the distribution of benefits or costs from the AI- and data-driven ‘digital revolution’. The articles also suggest specific lessons for unions and policy-makers seeking to develop broader strategies to engage with AI and digitalisation at work. We hope that they contribute to these crucial endeavours, by providing both an analytical base and comparative examples to support these strategies

TECHNOLOGICAL TOOLS AND THE IMPACT OF DIGITALISATION ON THE SUPPLY CHAIN

The supply chain has undergone a significant and innovative transformation in recent years with the introduction of new intelligent and digital technologies aimed at streamlining various supply chain activities from demand forecasting, manufacturing of goods, inventory management, and transport planning to delivery to customers. The digitisation of the transport and shipping industry incorporates advanced technologies such as cloud computing, the Internet of Things (IoT), artificial intelligence (AI), blockchain, and big data. These digital innovations are streamlining routes, forecasting demand, tracking shipments, and above all reacting quickly to changes, resulting in overall supply chain efficiency. This paper provides a comprehensive review of the issues that highlight the birth and construction of this transformation, as well as identifying the technological tools impacting the supply chain, in order to analyse the real impact of digitalisation on the development of relative supply chain strategies JEL: L91, O33, L23, C88, M11, L81  Article visualizations

AI, Robotics, and the Future of Jobs

This report is the latest in a sustained effort throughout 2014 by the Pew Research Center's Internet Project to mark the 25th anniversary of the creation of the World Wide Web by Sir Tim Berners-Lee (The Web at 25).The report covers experts' views about advances in artificial intelligence (AI) and robotics, and their impact on jobs and employment

Artificial Intelligence Towards Future Industrial Opportunities and Challenges

The industry 4.0 will bring reflective changes to our society, including an important digital shift in the manufacturing sector. At present, several manufacturing firms are trying to adopt the practices of industry 4.0 throughout their supply chain. The Fourth Industrial Revolution and the artificial intelligence at its core are fundamentally changing the way we live, work and interact as citizens. The complexity of this transformation may look overwhelming and to many threatening. Recently, the dramatic growth of new generation information technologies has prompted several countries to seek new strategies for industrial revolution. The globalization and the competitiveness are forcing companies to rethink and to innovate their production processes following the so-called Industry 4.0 paradigm. It represents the integration of tools already used in the past (big data, cloud, robot, 3D printing, simulation, etc.) that are now connected into a global network by transmitting digital data. Digitization and intelligentization of manufacturing process is the need for today’s industry. The manufacturing industries are currently changing from mass production to customized production. The rapid advancements in manufacturing technologies and applications in the industries help in increasing productivity. The term Industry 4.0 stands for the fourth industrial revolution which is defined as a new level of organization and control over the entire value chain of the life cycle of products; it is geared towards increasingly individualized customer requirements. Industry 4.0 is still visionary but a realistic concept which includes Internet of Things, Industrial Internet, Smart Manufacturing and Cloud based Manufacturing. Industry 4.0 concerns the strict integration of human in the manufacturing process so as to have continuous improvement and focus on value adding activities and avoiding wastes. The objective of this work is to provide an overview of Industry 4.0 and understanding of the pillars of Industry 4.0 with its applications and identifying the challenges and issues occurring with implementation the Industry 4.0 and to study the new trends and streams related to Industry 4.0 with artificial intelligence by using flexible intelligent approach. Based on intelligent and flexible AI methods and the complex safety relations in the process industry, we identify and discuss several technical challenges associated with process safety: knowledge acquisition with scarce labels for process safety; knowledge-based reasoning for process safety; accurate fusion of heterogeneous data from various sources; and effective learning for dynamic risk assessment and aided decision-making