Worker-robot cooperation and integration into the manufacturing workcell via the holonic control architecture

Cooperative manufacturing is a new field of research, which addresses new challenges beyond the physical safety of the worker. Those new challenges appear due to the need to connect the worker and the cobot from the informatics point of view in one cooperative workcell. This requires developing an appropriate manufacturing control system, which fits the nature of both the worker and the cobot. Furthermore, the manufacturing control system must be able to understand the production variations, to guide the cooperation between worker and the cobot and adapt with the production variations.Die kooperative Fertigung ist ein neues Forschungsgebiet, das sich neuen Herausforderungen stellt. Diese neuen Herausforderungen ergeben sich aus der Notwendigkeit, den Arbeiter und den Cobot aus der Sicht der Informatik in einem kooperativen Arbeitsplatz zu verbinden. Dies erfordert die Entwicklung eines geeigneten Produktionskontrollsystems, das sowohl der Natur des Arbeiters als auch der des Cobots entspricht. Darüber hinaus muss die Fertigungssteuerung in der Lage sein, die Produktionsschwankungen zu verstehen, um die Zusammenarbeit zwischen Arbeiter und Cobot zu steuern

Industry 4.0 and human factor: How is technology changing the role of the maintenance operator?

Abstract Industry 4.0 is revolutionizing not only the manufacturing industry but also maintenance strategies. As consequence of the introduction of Industry 4.0 technologies, new skills are demanded to maintenance operators that has to be able to interact, as instance, with Cyber Physical Systems and robots. In this paper, we first investigate the state-of-the-art of Industry 4.0 technologies that are transforming operations and production management and finally we discuss how the role of maintenance operators is changed in a such digitalized environment. We found that, the maintenance Operator 4.0 should be able to find relevant information and predict events by a proper use of Big Data analytics, in addition to the ability of interacting with computers, digital databases and robots. Finally, the ability to rapidly adapt his skills to innovations is also strongly demanded

Implementation of Industry 4.0 technology: New opportunities and challenges for maintenance strategy

Abstract Industry 4.0 is revolutionizing decision-making processes within the manufacturing industry. Maintenance strategies play a crucial role to improve progressively technical performances and economical savings. The introduction of Industry 4.0 technology results in relevant innovations able to condition maintenance policies. Moreover, innovative solutions can be introduced, such as "remote maintenance" and the "self-maintenance". In this paper, we investigate the state-of-the-art of technologies in a "smart factory" with the aim to understand how Industry 4.0 technologies are affecting maintenance policies and to discuss their implication in strategies. We found important trends in maintenance policies, such as "remote maintenance" and the attractive option of the "autonomous maintenance". This study represents the first comprehensive investigation in these research themes, and it desires to produce a broader insight and knowledge of current trends and main difficulties, highlighting critical aspects and disadvantages for the implementation of innovative policies

Enabling technology for maintenance in a smart factory: A literature review

Industry 4.0 technologies are transforming the factory in an "intelligent" or "smart" factory. In a such context, a greater efficiency and innovative relationship is basically demanded within the whole production chain, including suppliers, producers, and customers. To be more competitive, companies are becoming increasingly aware that maintenance plays a key role during the digital transformation from the perspective of both technology and management. In this work, we perform a literature review of published cases to investigate how maintenance is changing through technologies of Industry 4.0 currently used in maintenance. We found 34 papers in literature involved in analyzing relations between maintenance and Industry 4.0 technology. The analysis of such studies let us to establish the current technology state-of-art and identify the most suited technology that today is employed in maintenance tasks. In particular Industrial Internet of Things and Cloud Computing are more common in the analyzed studies, confirming how these concepts and technologies are at the basis of Industry 4.0

Coding by Design: GPT-4 empowers Agile Model Driven Development

Generating code from a natural language using Large Language Models (LLMs) such as ChatGPT, seems groundbreaking. Yet, with more extensive use, it's evident that this approach has its own limitations. The inherent ambiguity of natural language presents challenges for complex software designs. Accordingly, our research offers an Agile Model-Driven Development (MDD) approach that enhances code auto-generation using OpenAI's GPT-4. Our work emphasizes "Agility" as a significant contribution to the current MDD method, particularly when the model undergoes changes or needs deployment in a different programming language. Thus, we present a case-study showcasing a multi-agent simulation system of an Unmanned Vehicle Fleet. In the first and second layer of our approach, we constructed a textual representation of the case-study using Unified Model Language (UML) diagrams. In the next layer, we introduced two sets of constraints that minimize model ambiguity. Object Constraints Language (OCL) is applied to fine-tune the code constructions details, while FIPA ontology is used to shape communication semantics and protocols. Ultimately, leveraging GPT-4, our last layer auto-generates code in both Java and Python. The Java code is deployed within the JADE framework, while the Python code is deployed in PADE framework. Concluding our research, we engaged in a comprehensive evaluation of the generated code. From a behavioural standpoint, the auto-generated code aligned perfectly with the expected UML sequence diagram. Structurally, we compared the complexity of code derived from UML diagrams constrained solely by OCL to that influenced by both OCL and FIPA-ontology. Results indicate that ontology-constrained model produce inherently more intricate code, but it remains manageable and low-risk for further testing and maintenance

The role of Industry 4.0 enabling technologies for safety management: A systematic literature review

Innovations introduced during the Industry 4.0 era consist in the integration of the so called "nine pillars of technologies" in manufacturing, transforming the conventional factory in a smart factory. The aim of this study is to investigate enabling technologies of Industry 4.0, focusing on technologies that have a greater impact on safety management. Main characteristics of such technologies will be identified and described according to their use in an industrial environment. In order to do this, we chose a systematic literature review (SLR) to answer the research question in a comprehensively way. Results show that articles can be grouped according to different criteria. Moreover, we found that Industry 4.0 can increase safety levels in warehouse and logistic, as well as several solutions are available for building sector

A Generic Multi-Layer Architecture Based on ROS-JADE Integration for Autonomous Transport Vehicles

The design and operation of manufacturing systems is evolving to adapt to different challenges. One of the most important is the reconfiguration of the manufacturing process in response to context changes (e.g., faulty equipment or urgent orders, among others). In this sense, the Autonomous Transport Vehicle (ATV) plays a key role in building more flexible and decentralized manufacturing systems. Nowadays, robotic frameworks (RFs) are used for developing robotic systems such as ATVs, but they focus on the control of the robotic system itself. However, social abilities are required for performing intelligent interaction (peer-to-peer negotiation and decision-making) among the different and heterogeneous Cyber Physical Production Systems (such as machines, transport systems and other equipment present in the factory) to achieve manufacturing reconfiguration. This work contributes a generic multi-layer architecture that integrates a RF with a Multi-Agent System (MAS) to provide social abilities to ATVs. This architecture has been implemented on ROS and JADE, the most widespread RF and MAS framework, respectively. We believe this to be the first work that addresses the intelligent interaction of transportation systems for flexible manufacturing environments in a holistic form.This work was financed by MINECO/FEDER, UE (grant number DPI2015-68602-R) and by UPV/EHU (grant number PPG17/56)

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

A Strategic Roadmap for the Manufacturing Industry to Implement Industry 4.0

Industry 4.0 (also referred to as digitization of manufacturing) is characterized by cyber physical systems, automation, and data exchange. It is no longer a future trend and is being employed worldwide by manufacturing organizations, to gain benefits of improved performance, reduced inefficiencies, and lower costs, while improving flexibility. However, the implementation of Industry 4.0 enabling technologies is a difficult task and becomes even more challenging without any standardized approach. The barriers include, but are not limited to, lack of knowledge, inability to realistically quantify the return on investment, and lack of a skilled workforce. This study presents a systematic and content-centric literature review of Industry 4.0 enabling technologies, to highlight their impact on the manufacturing industry. It also provides a strategic roadmap for the implementation of Industry 4.0, based on lean six sigma approaches. The basis of the roadmap is the design for six sigma approach for the development of a new process chain, followed by a continuous improvement plan. The reason for choosing lean six sigma is to provide manufacturers with a sense of familiarity, as they have been employing these principles for removing waste and reducing variability. Major reasons for the rejection of Industry 4.0 implementation methodologies by manufactures are fear of the unknown and resistance to change, whereas the use of lean six sigma can mitigate them. The strategic roadmap presented in this paper can offer a holistic view of phases that manufacturers should undertake and the challenges they might face in their journey toward Industry 4.0 transition